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This audited derivative retains that declaration. + +- License summary: https://creativecommons.org/licenses/by/4.0/ +- Legal code: https://creativecommons.org/licenses/by/4.0/legalcode +- Upstream dataset: https://huggingface.co/datasets/SmartOR/FrontierOR + +Please attribute the upstream FrontierOR authors and identify this repository as an +audited derivative. 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The upstream +`ostrowski2012` directory was a mislabeled byte-level duplicate of the Earl/D'Andrea +2005 defensive-drill task and had no canonical metadata row. It is retained so that +existing 180-directory evaluation code does not break, and its alias status is +disclosed in the prompt, formulation, model, checker, and `CASE_IDENTITY.json`. + +## What “audited” means + +For every published instance, its bundled solution is finite, accepted by the current +checker, and has a bundled checker result matching a fresh replay. Objective-tamper +preflight and decision-erasure mutation probes were also run. The nine checkers +that formerly reconstructed omitted decisions with Gurobi now receive complete +operational witnesses. They validate bounds, domains, constraints, and objectives +by direct numeric substitution. Their 54-pair regression and mutation results are +recorded in `audit/SOLVERLESS_CHECKER_AUDIT.json`. + +The static triad scan is deliberately heuristic and still reports lexical findings; +those findings are retained for transparency and are not runtime failures. + +## Optimality boundary + +“Accepted” means a complete checker-verifiable **feasible evaluation baseline**. It +does not mean that every historical large incumbent was re-proved globally optimal. +For the 1,095 published references, the provenance classes are `explicit_incumbent=398, solver_optimal=474, solver_optimal_with_tolerance=77, objective_without_certificate=138, ambiguous_status=8`. In total, +544 published references across 128 cases do not carry a global-optimality +certificate. No feasible incumbent has been relabeled as a proven optimum. + +## Layout + +Each case directory contains its description, formulation, schemas, generation +code, checker, official instances, paired solutions, paired feasibility results, +and any matching solver logs. Cases using generic solver-free witness checking +also contain trusted compressed coefficients under `solverless_models/`. Gurobi +can still be used offline to regenerate reference solutions, but it is not needed +to run any released feasibility checker. Two legacy `segundo2019` solutions +without corresponding instances are excluded from this release. + +- `RELEASE_MANIFEST_180.json`: case identities and all 1,095 official pair bindings. +- `audit/`: final per-case matrix, change manifest, and compact gate summaries. +- `PUBLISH_FILE_MANIFEST.json` and `SHA256SUMS`: complete file inventory and hashes. + +Metadata can be loaded with: + +```python +from datasets import load_dataset + +meta = load_dataset("LeoJiangOR/FrontierOR-Audited-180", "meta", split="train") +print(meta[0]) +``` + +Download the complete self-contained task tree with: + +```bash +hf download LeoJiangOR/FrontierOR-Audited-180 --repo-type dataset --local-dir frontieror-audited-180 +``` + +## Provenance and license + +- Upstream dataset commit: `37ccd8b6dca3bf7f4e0c58941a6ed156832a6d9e` +- Evaluator commit: `93fb106d98c9d21729aa846f29822e12ad22db90` +- Audit/release date: 2026-08-29 (America/New_York) + +The upstream dataset card declares CC-BY-4.0. This derivative retains that license +and attribution; see `LICENSE.md` and `UPSTREAM_README.md`. diff --git a/RELEASE_MANIFEST_180.json b/RELEASE_MANIFEST_180.json new file mode 100644 index 0000000000000000000000000000000000000000..5295868edc069acf1553c028806b2461ea1a4718 --- /dev/null +++ b/RELEASE_MANIFEST_180.json @@ -0,0 +1,8077 @@ +{ + "format": "frontieror-audited-180-release-v2", + "repo_id": "LeoJiangOR/FrontierOR-Audited-180", + "visibility": "public", + "source_dataset": "SmartOR/FrontierOR", + "source_commit": "37ccd8b6dca3bf7f4e0c58941a6ed156832a6d9e", + "evaluator_commit": "93fb106d98c9d21729aa846f29822e12ad22db90", + "release_date": "2026-09-05", + "directory_count": 180, + "canonical_independent_case_count": 179, + "legacy_aliases": { + "ostrowski2012": "earl2005" + }, + "official_pair_count": 1095, + "official_reference_classification_counts": { + "explicit_incumbent": 398, + "solver_optimal": 474, + "solver_optimal_with_tolerance": 77, + "objective_without_certificate": 138, + "ambiguous_status": 8 + }, + "official_unproven_reference_count": 544, + "excluded_legacy_artifacts": [ + "segundo2019/gurobi_solution/large_solution_3.json", + "segundo2019/gurobi_solution/large_solution_4.json" + ], + "cases": [ + { + "case_id": "adulyasak2015", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "official_pair_count": 6, + "official_pairs": [ + { + "instance": "large_instance_1.json", + "solution": "large_solution_1.json", + "feasibility_result": "large_feasi_result_1.json", + "solver_log": null, + "solverless_model": "solverless_models/8b39f2b2f2de796c8dec9267f1bcbe358685fad9e8c0dc645cf29cf975f6d1ed.npz" + }, + { + "instance": "large_instance_2.json", + "solution": "large_solution_2.json", + "feasibility_result": "large_feasi_result_2.json", + "solver_log": null, + "solverless_model": "solverless_models/fb4ba36fa7ea76e5257c30f766f3837a294b653c03c51a5f7d0f5bcd0a7da3ea.npz" + }, + { + "instance": "large_instance_3.json", + "solution": "large_solution_3.json", + "feasibility_result": "large_feasi_result_3.json", + "solver_log": null, + "solverless_model": "solverless_models/2c863f95829818884a9dac9eb421d19669a4f40e13371d2cd755ada1d2eacdba.npz" + }, + { + "instance": "large_instance_4.json", + "solution": "large_solution_4.json", + "feasibility_result": "large_feasi_result_4.json", + "solver_log": null, + "solverless_model": "solverless_models/6c2ff584e5327058c384373e329d8708216fd3c2bdf0ec5cfdd4804b5e8e57ce.npz" + }, + { + "instance": "large_instance_5.json", + "solution": "large_solution_5.json", + "feasibility_result": "large_feasi_result_5.json", + "solver_log": null, + "solverless_model": "solverless_models/2b3e6984305a4edcfc2169b3e3e5b3a88fba7dbe3f00aaac0774e9ebb39cecc0.npz" + }, + { + "instance": "tiny_instance.json", + "solution": "tiny_solution.json", + "feasibility_result": "tiny_feasi_result.json", + "solver_log": "tiny_log.jsonl", + "solverless_model": "solverless_models/14d69b67d716fa56028ddc806bceb14da45ed4b1b2190c889e798f0711ecfb47.npz" + } + ] + }, + { + "case_id": "alba2013", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "official_pair_count": 6, + "official_pairs": [ + { + "instance": "large_instance_1.json", + "solution": "large_solution_1.json", + "feasibility_result": "large_feasi_result_1.json", + "solver_log": "large_log_1.jsonl" + }, + { + "instance": "large_instance_2.json", + "solution": "large_solution_2.json", + "feasibility_result": "large_feasi_result_2.json", + "solver_log": "large_log_2.jsonl" + }, + { + "instance": "large_instance_3.json", + "solution": "large_solution_3.json", + "feasibility_result": "large_feasi_result_3.json", + "solver_log": "large_log_3.jsonl" + }, + { + "instance": "large_instance_4.json", + "solution": "large_solution_4.json", + "feasibility_result": "large_feasi_result_4.json", + "solver_log": "large_log_4.jsonl" + }, + { + "instance": "large_instance_5.json", + "solution": "large_solution_5.json", + "feasibility_result": "large_feasi_result_5.json", + "solver_log": "large_log_5.jsonl" + }, + { + "instance": "tiny_instance.json", + "solution": "tiny_solution.json", + "feasibility_result": "tiny_feasi_result.json", + "solver_log": "tiny_log.jsonl" + } + ] + }, + { + "case_id": "alfieri2020", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "official_pair_count": 6, + "official_pairs": [ + { + "instance": "large_instance_1.json", + "solution": "large_solution_1.json", + "feasibility_result": "large_feasi_result_1.json", + "solver_log": "large_log_1.jsonl" + }, + { + "instance": "large_instance_2.json", + "solution": "large_solution_2.json", + "feasibility_result": "large_feasi_result_2.json", + "solver_log": "large_log_2.jsonl" + }, + { + "instance": "large_instance_3.json", + "solution": "large_solution_3.json", + "feasibility_result": "large_feasi_result_3.json", + "solver_log": "large_log_3.jsonl" + }, + { + "instance": "large_instance_4.json", + "solution": "large_solution_4.json", + "feasibility_result": "large_feasi_result_4.json", + "solver_log": "large_log_4.jsonl" + }, + { + "instance": "large_instance_5.json", + "solution": "large_solution_5.json", + "feasibility_result": "large_feasi_result_5.json", + "solver_log": "large_log_5.jsonl" + }, + { + "instance": "tiny_instance.json", + "solution": "tiny_solution.json", + "feasibility_result": "tiny_feasi_result.json", + "solver_log": "tiny_log.jsonl" + } + ] + }, + { + "case_id": "amaldi2013", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "official_pair_count": 6, + "official_pairs": [ + { + "instance": "large_instance_1.json", + "solution": "large_solution_1.json", + "feasibility_result": "large_feasi_result_1.json", + "solver_log": "large_log_1.jsonl" + }, + { + "instance": "large_instance_2.json", + "solution": "large_solution_2.json", + "feasibility_result": "large_feasi_result_2.json", + "solver_log": "large_log_2.jsonl" + }, + { + "instance": "large_instance_3.json", + "solution": "large_solution_3.json", + "feasibility_result": "large_feasi_result_3.json", + "solver_log": "large_log_3.jsonl" + }, + { + "instance": "large_instance_4.json", + "solution": "large_solution_4.json", + "feasibility_result": "large_feasi_result_4.json", + "solver_log": "large_log_4.jsonl" + }, + { + "instance": "large_instance_5.json", + "solution": "large_solution_5.json", + "feasibility_result": "large_feasi_result_5.json", + "solver_log": "large_log_5.jsonl" + }, + { + "instance": "tiny_instance.json", + "solution": "tiny_solution.json", + "feasibility_result": "tiny_feasi_result.json", + "solver_log": "tiny_log.jsonl" + } + ] + }, + { + "case_id": "araujo2015", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "official_pair_count": 6, + "official_pairs": [ + { + "instance": "large_instance_1.json", + "solution": "large_solution_1.json", + "feasibility_result": "large_feasi_result_1.json", + "solver_log": null + }, + { + "instance": "large_instance_2.json", + "solution": "large_solution_2.json", + "feasibility_result": "large_feasi_result_2.json", + "solver_log": null + }, + { + "instance": "large_instance_3.json", + "solution": "large_solution_3.json", + "feasibility_result": "large_feasi_result_3.json", + "solver_log": null + }, + { + "instance": "large_instance_4.json", + "solution": "large_solution_4.json", + "feasibility_result": "large_feasi_result_4.json", + "solver_log": null + }, + { + "instance": "large_instance_5.json", + "solution": "large_solution_5.json", + "feasibility_result": "large_feasi_result_5.json", + "solver_log": null + }, + { + "instance": "tiny_instance.json", + "solution": "tiny_solution.json", + "feasibility_result": "tiny_feasi_result.json", + "solver_log": "tiny_log.jsonl" + } + ] + }, + { + "case_id": "araujo2020", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "official_pair_count": 6, + "official_pairs": [ + { + "instance": "large_instance_1.json", + "solution": "large_solution_1.json", + "feasibility_result": "large_feasi_result_1.json", + "solver_log": "large_log_1.jsonl" + }, + { + "instance": "large_instance_2.json", + "solution": "large_solution_2.json", + "feasibility_result": "large_feasi_result_2.json", + "solver_log": "large_log_2.jsonl" + }, + { + "instance": "large_instance_3.json", + "solution": "large_solution_3.json", + "feasibility_result": "large_feasi_result_3.json", + "solver_log": "large_log_3.jsonl" + }, + { + "instance": "large_instance_4.json", + "solution": "large_solution_4.json", + "feasibility_result": "large_feasi_result_4.json", + "solver_log": "large_log_4.jsonl" + }, + { + "instance": "large_instance_5.json", + "solution": "large_solution_5.json", + "feasibility_result": "large_feasi_result_5.json", + "solver_log": "large_log_5.jsonl" + }, + { + "instance": "tiny_instance.json", + "solution": "tiny_solution.json", + "feasibility_result": "tiny_feasi_result.json", + "solver_log": "tiny_log.jsonl" + } + ] + }, + { + "case_id": "archetti2007", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "official_pair_count": 6, + "official_pairs": [ + { + "instance": "large_instance_1.json", + "solution": "large_solution_1.json", + "feasibility_result": "large_feasi_result_1.json", + "solver_log": "large_log_1.jsonl" + }, + { + "instance": "large_instance_2.json", + "solution": "large_solution_2.json", + "feasibility_result": "large_feasi_result_2.json", + "solver_log": "large_log_2.jsonl" + }, + { + "instance": "large_instance_3.json", + "solution": "large_solution_3.json", + "feasibility_result": "large_feasi_result_3.json", + "solver_log": "large_log_3.jsonl" + }, + { + "instance": "large_instance_4.json", + "solution": "large_solution_4.json", + "feasibility_result": "large_feasi_result_4.json", + "solver_log": "large_log_4.jsonl" + }, + { + "instance": "large_instance_5.json", + "solution": "large_solution_5.json", + "feasibility_result": "large_feasi_result_5.json", + "solver_log": "large_log_5.jsonl" + }, + { + "instance": "tiny_instance.json", + "solution": "tiny_solution.json", + "feasibility_result": "tiny_feasi_result.json", + "solver_log": "tiny_log.jsonl" + } + ] + }, + { + "case_id": "armbruster2012", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "official_pair_count": 7, + "official_pairs": [ + { + "instance": "large_instance_1.json", + "solution": "large_solution_1.json", + "feasibility_result": "large_feasi_result_1.json", + "solver_log": null + }, + { + "instance": "large_instance_2.json", + "solution": "large_solution_2.json", + "feasibility_result": "large_feasi_result_2.json", + "solver_log": null + }, + { + "instance": "large_instance_3.json", + "solution": "large_solution_3.json", + "feasibility_result": "large_feasi_result_3.json", + "solver_log": null + }, + { + "instance": "large_instance_4.json", + "solution": "large_solution_4.json", + "feasibility_result": "large_feasi_result_4.json", + "solver_log": null + }, + { + "instance": "large_instance_5.json", + "solution": "large_solution_5.json", + "feasibility_result": "large_feasi_result_5.json", + "solver_log": null + }, + { + "instance": "tiny_instance.json", + "solution": "tiny_solution.json", + "feasibility_result": "tiny_feasi_result.json", + "solver_log": "tiny_log.jsonl" + }, + { + "instance": "tiny_instance_2.json", + "solution": "tiny_solution_2.json", + "feasibility_result": "tiny_feasi_result_2.json", + "solver_log": null + } + ] + }, + { + "case_id": "arslan2021", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "official_pair_count": 6, + "official_pairs": [ + { + "instance": "large_instance_1.json", + "solution": "large_solution_1.json", + "feasibility_result": "large_feasi_result_1.json", + "solver_log": "large_log_1.jsonl" + }, + { + "instance": "large_instance_2.json", + "solution": "large_solution_2.json", + "feasibility_result": "large_feasi_result_2.json", + "solver_log": "large_log_2.jsonl" + }, + { + "instance": "large_instance_3.json", + "solution": "large_solution_3.json", + "feasibility_result": "large_feasi_result_3.json", + "solver_log": "large_log_3.jsonl" + }, + { + "instance": "large_instance_4.json", + "solution": "large_solution_4.json", + "feasibility_result": "large_feasi_result_4.json", + "solver_log": "large_log_4.jsonl" + }, + { + "instance": "large_instance_5.json", + "solution": "large_solution_5.json", + "feasibility_result": "large_feasi_result_5.json", + "solver_log": "large_log_5.jsonl" + }, + { + "instance": "tiny_instance.json", + "solution": "tiny_solution.json", + "feasibility_result": "tiny_feasi_result.json", + "solver_log": "tiny_log.jsonl" + } + ] + }, + { + "case_id": "bard2002", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "official_pair_count": 6, + "official_pairs": [ + { + "instance": "large_instance_1.json", + "solution": "large_solution_1.json", + "feasibility_result": "large_feasi_result_1.json", + "solver_log": "large_log_1.jsonl" + }, + { + "instance": "large_instance_2.json", + "solution": "large_solution_2.json", + "feasibility_result": "large_feasi_result_2.json", + "solver_log": "large_log_2.jsonl" + }, + { + "instance": "large_instance_3.json", + "solution": "large_solution_3.json", + "feasibility_result": "large_feasi_result_3.json", + "solver_log": "large_log_3.jsonl" + }, + { + "instance": "large_instance_4.json", + "solution": "large_solution_4.json", + "feasibility_result": "large_feasi_result_4.json", + "solver_log": "large_log_4.jsonl" + }, + { + "instance": "large_instance_5.json", + "solution": "large_solution_5.json", + "feasibility_result": "large_feasi_result_5.json", + "solver_log": "large_log_5.jsonl" + }, + { + "instance": "tiny_instance.json", + "solution": "tiny_solution.json", + "feasibility_result": "tiny_feasi_result.json", + "solver_log": "tiny_log.jsonl" + } + ] + }, + { + "case_id": "barnhart2000", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "official_pair_count": 6, + "official_pairs": [ + { + "instance": "large_instance_1.json", + "solution": "large_solution_1.json", + "feasibility_result": "large_feasi_result_1.json", + "solver_log": "large_log_1.jsonl" + }, + { + "instance": "large_instance_2.json", + "solution": "large_solution_2.json", + "feasibility_result": "large_feasi_result_2.json", + "solver_log": "large_log_2.jsonl" + }, + { + "instance": "large_instance_3.json", + "solution": "large_solution_3.json", + "feasibility_result": "large_feasi_result_3.json", + "solver_log": "large_log_3.jsonl" + }, + { + "instance": "large_instance_4.json", + "solution": "large_solution_4.json", + "feasibility_result": "large_feasi_result_4.json", + "solver_log": "large_log_4.jsonl" + }, + { + "instance": "large_instance_5.json", + "solution": "large_solution_5.json", + "feasibility_result": "large_feasi_result_5.json", + "solver_log": "large_log_5.jsonl" + }, + { + "instance": "tiny_instance.json", + "solution": "tiny_solution.json", + "feasibility_result": "tiny_feasi_result.json", + "solver_log": "tiny_log.jsonl" + } + ] + }, + { + "case_id": "bayani2024", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "official_pair_count": 6, + "official_pairs": [ + { + "instance": "large_instance_1.json", + "solution": "large_solution_1.json", + "feasibility_result": "large_feasi_result_1.json", + "solver_log": null + }, + { + "instance": "large_instance_2.json", + "solution": "large_solution_2.json", + "feasibility_result": "large_feasi_result_2.json", + "solver_log": null + }, + { + "instance": "large_instance_3.json", + "solution": "large_solution_3.json", + "feasibility_result": "large_feasi_result_3.json", + "solver_log": null + }, + { + "instance": "large_instance_4.json", + "solution": "large_solution_4.json", + "feasibility_result": "large_feasi_result_4.json", + "solver_log": null + }, + { + "instance": "large_instance_5.json", + "solution": "large_solution_5.json", + "feasibility_result": "large_feasi_result_5.json", + "solver_log": null + }, + { + "instance": "tiny_instance.json", + "solution": "tiny_solution.json", + "feasibility_result": "tiny_feasi_result.json", + "solver_log": "tiny_log.jsonl" + } + ] + }, + { + "case_id": "belhoul2014", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "official_pair_count": 6, + "official_pairs": [ + { + "instance": "large_instance_1.json", + "solution": "large_solution_1.json", + "feasibility_result": "large_feasi_result_1.json", + "solver_log": "large_log_1.jsonl" + }, + { + "instance": "large_instance_2.json", + "solution": "large_solution_2.json", + "feasibility_result": "large_feasi_result_2.json", + "solver_log": "large_log_2.jsonl" + }, + { + "instance": "large_instance_3.json", + "solution": "large_solution_3.json", + "feasibility_result": "large_feasi_result_3.json", + "solver_log": "large_log_3.jsonl" + }, + { + "instance": "large_instance_4.json", + "solution": "large_solution_4.json", + "feasibility_result": "large_feasi_result_4.json", + "solver_log": "large_log_4.jsonl" + }, + { + "instance": "large_instance_5.json", + "solution": "large_solution_5.json", + "feasibility_result": "large_feasi_result_5.json", + "solver_log": "large_log_5.jsonl" + }, + { + "instance": "tiny_instance.json", + "solution": "tiny_solution.json", + "feasibility_result": "tiny_feasi_result.json", + "solver_log": "tiny_log.jsonl" + } + ] + }, + { + "case_id": "belvaux2000", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "official_pair_count": 6, + "official_pairs": [ + { + "instance": "large_instance_1.json", + "solution": "large_solution_1.json", + "feasibility_result": "large_feasi_result_1.json", + "solver_log": "large_log_1.jsonl" + }, + { + "instance": "large_instance_2.json", + "solution": "large_solution_2.json", + "feasibility_result": "large_feasi_result_2.json", + "solver_log": "large_log_2.jsonl" + }, + { + "instance": "large_instance_3.json", + "solution": "large_solution_3.json", + "feasibility_result": "large_feasi_result_3.json", + "solver_log": "large_log_3.jsonl" + }, + { + "instance": "large_instance_4.json", + "solution": "large_solution_4.json", + "feasibility_result": "large_feasi_result_4.json", + "solver_log": "large_log_4.jsonl" + }, + { + "instance": "large_instance_5.json", + "solution": "large_solution_5.json", + "feasibility_result": "large_feasi_result_5.json", + "solver_log": "large_log_5.jsonl" + }, + { + "instance": "tiny_instance.json", + "solution": "tiny_solution.json", + "feasibility_result": "tiny_feasi_result.json", + "solver_log": "tiny_log.jsonl" + } + ] + }, + { + "case_id": "bentert2022", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "official_pair_count": 6, + "official_pairs": [ + { + "instance": "large_instance_1.json", + "solution": "large_solution_1.json", + "feasibility_result": "large_feasi_result_1.json", + "solver_log": "large_log_1.jsonl" + }, + { + "instance": "large_instance_2.json", + "solution": "large_solution_2.json", + "feasibility_result": "large_feasi_result_2.json", + "solver_log": "large_log_2.jsonl" + }, + { + "instance": "large_instance_3.json", + "solution": "large_solution_3.json", + "feasibility_result": "large_feasi_result_3.json", + "solver_log": "large_log_3.jsonl" + }, + { + "instance": "large_instance_4.json", + "solution": "large_solution_4.json", + "feasibility_result": "large_feasi_result_4.json", + "solver_log": "large_log_4.jsonl" + }, + { + "instance": "large_instance_5.json", + "solution": "large_solution_5.json", + "feasibility_result": "large_feasi_result_5.json", + "solver_log": "large_log_5.jsonl" + }, + { + "instance": "tiny_instance.json", + "solution": "tiny_solution.json", + "feasibility_result": "tiny_feasi_result.json", + "solver_log": "tiny_log.jsonl" + } + ] + }, + { + "case_id": "bergman2019", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "official_pair_count": 6, + "official_pairs": [ + { + "instance": "large_instance_1.json", + "solution": "large_solution_1.json", + "feasibility_result": "large_feasi_result_1.json", + "solver_log": "large_log_1.jsonl" + }, + { + "instance": "large_instance_2.json", + "solution": "large_solution_2.json", + "feasibility_result": "large_feasi_result_2.json", + "solver_log": "large_log_2.jsonl" + }, + { + "instance": "large_instance_3.json", + "solution": "large_solution_3.json", + "feasibility_result": "large_feasi_result_3.json", + "solver_log": "large_log_3.jsonl" + }, + { + "instance": "large_instance_4.json", + "solution": "large_solution_4.json", + 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"large_instance_2.json", + "solution": "large_solution_2.json", + "feasibility_result": "large_feasi_result_2.json", + "solver_log": "large_log_2.jsonl" + }, + { + "instance": "large_instance_3.json", + "solution": "large_solution_3.json", + "feasibility_result": "large_feasi_result_3.json", + "solver_log": "large_log_3.jsonl" + }, + { + "instance": "large_instance_4.json", + "solution": "large_solution_4.json", + "feasibility_result": "large_feasi_result_4.json", + "solver_log": "large_log_4.jsonl" + }, + { + "instance": "large_instance_5.json", + "solution": "large_solution_5.json", + "feasibility_result": "large_feasi_result_5.json", + "solver_log": "large_log_5.jsonl" + }, + { + "instance": "tiny_instance.json", + "solution": "tiny_solution.json", + "feasibility_result": "tiny_feasi_result.json", + "solver_log": "tiny_log.jsonl" + } + ] + } + ], + "solver_free_complete_witness_cases": 9, + "solver_free_complete_witness_pairs": 54 +} diff --git a/RELEASE_NOTES.md b/RELEASE_NOTES.md new file mode 100644 index 0000000000000000000000000000000000000000..6514b8da370d221b5665e8f506b93741c727961a --- /dev/null +++ b/RELEASE_NOTES.md @@ -0,0 +1,22 @@ +# Release notes: Audited-180 v2 + +This revision removes runtime optimization dependencies from the nine +feasibility checkers that previously invoked Gurobi directly or dynamically: +`adulyasak2015`, `bertsimas2024`, `cote2018`, `liao2020`, `lozano2017`, +`rahmaniani2018`, `rahmaniani2022`, `riley2025`, and `taninmis2022`. + +All 54 affected reference solutions now carry complete operational variables. +Seven cases use a sparse `model_variables` map; `lozano2017` retains its full +leader/follower vectors, and `taninmis2022` retains its named operational maps. +Six cases also include trusted compressed coefficient snapshots under +`solverless_models/`. Checkers only perform numeric substitution and do not +import or invoke Gurobi, SciPy optimizers, HiGHS, CVXPY, or another solver. + +Regression on 2026-09-05 accepted all 54 official pairs, rejected all 54 +objective mutations, and rejected all 54 complete-variable mutations. The +detailed record is in `audit/SOLVERLESS_CHECKER_AUDIT.json`. + +As in v1, acceptance proves feasibility and objective consistency for the +submitted complete witness. It does not relabel historical incumbents as +globally optimal. In particular, integer follower/recourse optimality is now a +solution-quality concern rather than an online checker solve. diff --git a/SHA256SUMS b/SHA256SUMS new file mode 100644 index 0000000000000000000000000000000000000000..275a7caa6cf5a93daf0b5af89345189dc00efbb5 --- /dev/null +++ b/SHA256SUMS @@ -0,0 +1,5547 @@ +e3bbb2e297167dd119bff49157afe564ab6013a5ae9a2adafce262018c2d7c40 .gitattributes +afe57098691c99101321d1ee134d6081bdd5c878292f917bcc3cc487805c2309 BUILD_INFO.json +28aef5f72840c5b582d70d731c1b539792744538ecfd61225daa8ddc108f4b3d CASE_IDS.txt +201897fafb38061ac07a207c34b6a146685f78261a9aab3c7e651e1510f7edf8 LICENSE.md +3f8b8d0f80b6d294d57d1070e8042b79ccc0d67c6e77a568f160ad4a60c7164c PUBLISH_FILE_MANIFEST.json +7fe2113af4004743c05f264e6b6a4bbced6e69a7eea46f215b187be5119a1665 README.md +c64f9905931d4bf020f1cde238af37fa2fd0e433f5aff07d84bebc1dfab881cd RELEASE_MANIFEST_180.json +d00d159a4ce3f5fb8bcd359638b207f09149cbf50f0dda4d2ab1693e10a34ab0 RELEASE_NOTES.md 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b/UPSTREAM_README.md new file mode 100644 index 0000000000000000000000000000000000000000..0cd8fecf8a1f8a4e921356e5375ac0132763f6d0 --- /dev/null +++ b/UPSTREAM_README.md @@ -0,0 +1,91 @@ +--- +license: cc-by-4.0 +language: + - en +pretty_name: Frontier-OR Benchmark +size_categories: + - n<1K +task_categories: + - other +tags: + - operations-research + - optimization + - mixed-integer-programming + - gurobi + - benchmark + - llm-for-or +configs: + - config_name: meta + data_files: paper_meta_info.json +--- + +# Frontier-OR Benchmark + +A benchmark of **180 literature-grounded OR tasks**, each packaged as a +self-contained reproducible unit: natural-language problem description, +mathematical formulation, reference Gurobi implementation, test instances, +reference solutions, and an automated feasibility checker. + +Designed for evaluating LLMs on the end-to-end task of turning a research +paper's OR problem into runnable, verifiably-correct optimization code. + +## What's in each paper folder + +| File / dir | Purpose | +| --- | --- | +| `problem_description.txt` | Natural-language problem statement | +| `mathematical_formulation.md` | Mathematical model (variables, constraints, objective) | +| `gurobi_code.py` | Reference Gurobi implementation | +| `feasibility_check.py` | Automated solution validator | +| `solution_logger.py` | Utility for serializing solutions | +| `instance_schema.json` | JSON schema for input instances | +| `solution_schema.json` | JSON schema for solutions | +| `instance/` | Test instances (JSON) | +| `gurobi_solution/` | Reference solutions produced by `gurobi_code.py` | +| `gurobi_solution_log/` | Anytime objective trajectory from the reference Gurobi run | +| `gurobi_feasi_result/` | Output of `feasibility_check.py` on the reference solutions | + + + + + +## Loading the metadata + +```python +from datasets import load_dataset +meta = load_dataset("SmartOR/FrontierOR", "meta", split="train") +print(meta[0]) +``` + +For the per-paper instance / solution data, clone or download the full +repository — the structure is intentionally a file tree, not a flat HF +dataset, because each paper is its own reproducible package. + +```bash +hf download SmartOR/FrontierOR --repo-type dataset --local-dir frontier-or +``` + + + + diff --git a/adulyasak2015/feasibility_check.py b/adulyasak2015/feasibility_check.py new file mode 100644 index 0000000000000000000000000000000000000000..99c379ddd482f3aba00227dc1b0e63fefb9ec511 --- /dev/null +++ b/adulyasak2015/feasibility_check.py @@ -0,0 +1,820 @@ +#!/usr/bin/env python3 +""" +Feasibility checker for the Two-Stage Stochastic Production Routing Problem (2-SPRP). +Reference: Adulyasak, Cordeau, Jans (2015), Operations Research. + +Checks all hard constraints from the mathematical formulation. +Constraint numbering follows the paper's formulation section, counted from top to bottom: + Constraint 1 = Eq.(2): Plant inventory flow balance (=) + Constraint 2 = Eq.(3): Customer inventory flow balance (=) + Constraint 3 = Eq.(4): Maximum inventory at plant (<=) + Constraint 4 = Eq.(5): Maximum inventory at customers (<=) + Constraint 5 = Eq.(6): Production capacity / setup (<=) + Constraint 6 = Eq.(7): Vehicle load capacity (<=) + Constraint 7 = Eq.(8): Delivery only if customer visited (<=) + Constraint 8 = Eq.(9): Each customer visited at most once per period (<=) + Constraint 9 = Eq.(10): Degree constraints (=) + Constraint 10 = Eq.(11): Subtour elimination (<=) + Constraint 11 = Eq.(12): Nonnegativity of second-stage variables (>=) + Constraint 12 = Eq.(13): Binary y, z (domain) + Constraint 13 = Eq.(14): Binary x for non-depot edges (domain) + Constraint 14 = Eq.(15): x at depot edges in {0,1,2} (domain) + Constraint 15 = Eq.(16): Customer visit requires vehicle dispatch (<=) + Constraint 16 = Eq.(17): Edge implies both endpoints visited (<=) + Constraint 17 = Eq.(18): Vehicle symmetry breaking (>=) + Constraint 18 = Eq.(19): Lexicographic ordering of visits (>=) + Constraint 19 = Eq.(20): Nonanticipativity for p (=, M-SPRP only) + Constraint 20 = Eq.(21): Nonanticipativity for I (=, M-SPRP only) + Constraint 21 = Eq.(22): Nonanticipativity for e (=, M-SPRP only) + Constraint 22 = Eq.(23): Nonanticipativity for q (=, M-SPRP only) + Constraint 23 = Objective consistency (lower-bound check on reported obj) +""" + +import argparse +import json +import sys +from collections import defaultdict +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parent.parent)) +from solverless_witness import require_linked_variables, validate_complete_witness + +# FRONTIEROR_RELEASE_CONTRACT_V2 +_FRONTIEROR_REQUIRED_SOLUTION_FIELDS = ('model_variables', 'objective_value', 'x', 'y', 'z') +_FRONTIEROR_NONEMPTY_SOLUTION_FIELDS = ('model_variables',) +_FRONTIEROR_DERIVED_FIELD_RULES = () +_FRONTIEROR_CONDITIONAL_FIELD_RULES = () + + +def _frontieror_validate_solution_contract(solution): + """Return a structured rejection when the public solution contract is absent.""" + violations = [] + if not isinstance(solution, dict): + violations.append("solution must be a JSON object") + else: + missing = [ + field + for field in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS + if field not in solution or solution[field] is None + ] + if missing: + violations.append( + "missing required solution field(s): " + ", ".join(missing) + ) + + for discriminator, expected_value, fields in _FRONTIEROR_CONDITIONAL_FIELD_RULES: + if solution.get(discriminator) != expected_value: + continue + conditional_missing = [ + field + for field in fields + if field not in solution or solution[field] is None + ] + if conditional_missing: + violations.append( + "missing conditionally required solution field(s): " + + ", ".join(conditional_missing) + ) + + empty = [ + field + for field in _FRONTIEROR_NONEMPTY_SOLUTION_FIELDS + if field in solution and solution[field] in (None, [], {}) + ] + if empty: + violations.append( + "empty required decision witness field(s): " + ", ".join(empty) + ) + + for target, operation, source, source_key in _FRONTIEROR_DERIVED_FIELD_RULES: + try: + reported = float(solution[target]) + if operation == "length": + expected = float(len(solution[source])) + elif operation == "mapping_value": + expected = float(solution[source][source_key]) + else: + raise ValueError("unknown derived-field operation") + except (KeyError, TypeError, ValueError, OverflowError): + violations.append( + f"cannot derive {target} from decision witness field {source}" + ) + continue + if ( + reported != reported + or expected != expected + or reported in (float("inf"), float("-inf")) + or expected in (float("inf"), float("-inf")) + or abs(reported - expected) > 1e-6 + ): + violations.append( + f"{target}={reported} does not match the decision-derived " + f"value {expected}" + ) + + if "objective_value" in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS: + objective = solution.get("objective_value") + if ( + isinstance(objective, bool) + or not isinstance(objective, (int, float)) + or objective != objective + or objective in (float("inf"), float("-inf")) + ): + violations.append("objective_value must be a finite JSON number") + + if not violations: + return None + return { + "feasible": False, + "violated_constraints": ["solution_schema"], + "violations": violations, + "violation_magnitudes": [], + } + + +def _frontieror_write_unexpected_rejection(error): + """Turn malformed candidate/checker exceptions into a normal rejection.""" + import json as _frontieror_json + import sys as _frontieror_sys + + result_path = None + for index, argument in enumerate(_frontieror_sys.argv): + if argument == "--result_path" and index + 1 < len(_frontieror_sys.argv): + result_path = _frontieror_sys.argv[index + 1] + break + if argument.startswith("--result_path="): + result_path = argument.split("=", 1)[1] + break + if not result_path: + return False + try: + with open(result_path, "w") as result_handle: + _frontieror_json.dump( + { + "feasible": False, + "violated_constraints": ["malformed_solution"], + "violations": [ + "checker rejected malformed candidate: " + + type(error).__name__ + + ": " + + str(error)[:500] + ], + "violation_magnitudes": [], + }, + result_handle, + indent=2, + ) + except Exception: + return False + return True + + + + +# FRONTIEROR_ADULYASAK_EXACT_OBJECTIVE_V1 +import math +from typing import Any + +def _binary(mapping: dict[str, Any], key: str) -> int: + value = mapping.get(key, 0) + if isinstance(value, bool): + return int(value) + number = float(value) + rounded = round(number) + if not math.isfinite(number) or abs(number - rounded) > 1e-6 or rounded not in (0, 1): + raise ValueError(f"{key} must be binary") + return int(rounded) + +def _edge_value(mapping: dict[str, Any], key: str, depot_edge: bool) -> int: + value = mapping.get(key, 0) + if isinstance(value, bool): + value = int(value) + number = float(value) + rounded = round(number) + upper = 2 if depot_edge else 1 + if ( + not math.isfinite(number) + or abs(number - rounded) > 1e-6 + or rounded < 0 + or rounded > upper + ): + raise ValueError(f"{key} must be an integer in [0, {upper}]") + return int(rounded) + +def _frontieror_solverless_objective(instance, solution, instance_path): + """Validate complete recourse and return its directly evaluated cost.""" + n = int(instance["n"]) + horizon = int(instance["T"]) + vehicle_count = int(instance["m"]) + nodes = range(n + 1) + periods = range(1, horizon + 1) + vehicles = range(1, vehicle_count + 1) + edges = [(i, j) for i in nodes for j in nodes if i < j] + expected = { + f"y_{t}": _binary(solution["y"], str(t)) for t in periods + } + expected.update({ + f"z_{i}_{k}_{t}": _binary(solution["z"], f"{i}_{k}_{t}") + for i in nodes for k in vehicles for t in periods + }) + expected.update({ + f"x_{i}_{j}_{k}_{t}": _edge_value( + solution["x"], f"{i}_{j}_{k}_{t}", i == 0 + ) + for i, j in edges for k in vehicles for t in periods + }) + objective, errors, _ = validate_complete_witness( + instance_path, solution, Path(__file__).resolve().parent + ) + errors.extend(require_linked_variables(solution, expected)) + if errors: + raise ValueError("; ".join(errors[:20])) + if objective is None: + raise ValueError("complete witness objective is unavailable") + return objective + + +def check_feasibility(data, sol, instance_path=None): + tol = 1e-5 + eps = 1e-5 + + n = data['n'] + T = data['T'] + m = data['m'] + Q = data['Q'] + C = data['C'] + L = data['L'] + I0 = data['I0'] + demand_sc = data['demand_scenarios'] + n_omega = data['n_scenarios'] + problem_type = data.get('problem_type', '2-SPRP') + + Nc = list(range(1, n + 1)) + N = list(range(0, n + 1)) + periods = list(range(1, T + 1)) + vehicles = list(range(1, m + 1)) + scenarios = list(range(n_omega)) + + edges = [(i, j) for i in N for j in N if i < j] + edges_Nc = [(i, j) for i in Nc for j in Nc if i < j] + + def d(omega, i, t): + return demand_sc[omega][i - 1][t - 1] + + def M_t_val(t, omega): + rem = sum(d(omega, ci, s) for ci in Nc for s in range(t, T + 1)) + return min(C, Q, rem) + + def M_prime_val(i, t, omega): + rem = sum(d(omega, i, s) for s in range(t, T + 1)) + return min(L[i], Q, rem) + + # --- Parse solution --- + y = {} + for key, val in sol.get('y', {}).items(): + y[int(key)] = val + for t in periods: + y.setdefault(t, 0) + + z = {} + for key, val in sol.get('z', {}).items(): + parts = key.split('_') + i, k, t = int(parts[0]), int(parts[1]), int(parts[2]) + z[i, k, t] = val + for i in N: + for k in vehicles: + for t in periods: + z.setdefault((i, k, t), 0) + + x = {} + for key, val in sol.get('x', {}).items(): + parts = key.split('_') + i, j, k, t = int(parts[0]), int(parts[1]), int(parts[2]), int(parts[3]) + x[i, j, k, t] = val + for (i, j) in edges: + for k in vehicles: + for t in periods: + x.setdefault((i, j, k, t), 0) + + violations_list = [] # list of (constraint_idx, message, lhs, rhs, raw_excess, normalizer, ratio) + violated_set = set() + + def record_violation(constraint_idx, lhs, rhs, op, msg): + if op == '<=': + violation_amount = max(0.0, lhs - rhs) + elif op == '>=': + violation_amount = max(0.0, rhs - lhs) + elif op == '=': + violation_amount = abs(lhs - rhs) + else: + return + + if violation_amount > tol: + normalizer = max(abs(rhs), eps) + ratio = violation_amount / normalizer + violated_set.add(constraint_idx) + violations_list.append({ + 'constraint': constraint_idx, + 'msg': msg, + 'lhs': float(lhs), + 'rhs': float(rhs), + 'raw_excess': float(violation_amount), + 'normalizer': float(normalizer), + 'ratio': float(ratio), + }) + + # ===================================================================== + # Constraints 1-7 (Eqs. 2-8): Second-stage constraints + # These involve second-stage variables (p, I, q, e) not present in the + # solution. We construct feasible second-stage variables to verify. + # For any first-stage (y, z, x), feasible second-stage always exists + # because unmet demand variable e acts as slack. + # ===================================================================== + + # Construct feasible second-stage variables for each scenario + for omega in scenarios: + # Determine maximum production and delivery given first-stage decisions + # Greedy: produce as much as allowed, deliver as much as possible + # to minimize unmet demand (which always makes constraints feasible). + + # Production capacity per period + p_max = {} + for t in periods: + p_max[t] = M_t_val(t, omega) * y[t] + + # Delivery capacity per customer/vehicle/period + q_max = {} + for i_c in Nc: + for k in vehicles: + for t in periods: + q_max[i_c, k, t] = M_prime_val(i_c, t, omega) * z[i_c, k, t] + + # Vehicle load limit per vehicle/period + veh_cap = {} + for k in vehicles: + for t in periods: + veh_cap[k, t] = Q * z[0, k, t] + + # Forward simulation: allocate production and deliveries + I_plant = {0: float(I0[0])} + I_cust = {} + for i_c in Nc: + I_cust[i_c, 0] = float(I0[i_c]) + + p_val = {} + q_val = {} + e_val = {} + + for t in periods: + # Determine total demand and delivery needs + total_need = 0.0 + for i_c in Nc: + need = max(0.0, d(omega, i_c, t) - I_cust[i_c, t - 1]) + total_need += need + + # Production: produce up to capacity, limited by plant storage + produce = min(p_max[t], total_need, L[0] - I_plant[t - 1] + total_need) + produce = max(0.0, produce) + p_val[t] = produce + + # Available at plant for delivery + available = I_plant[t - 1] + produce + + # Allocate deliveries: respect vehicle capacity and customer visit + total_delivered = 0.0 + for k in vehicles: + veh_remaining = veh_cap[k, t] + for i_c in Nc: + cap = min(q_max[i_c, k, t], veh_remaining, + max(0.0, available - total_delivered)) + # Deliver up to demand shortfall + need = max(0.0, d(omega, i_c, t) - I_cust[i_c, t - 1]) + deliver = min(cap, need) + q_val[i_c, k, t] = deliver + veh_remaining -= deliver + total_delivered += deliver + + # Update plant inventory + I_plant[t] = I_plant[t - 1] + p_val[t] - total_delivered + + # Update customer inventories and unmet demand + for i_c in Nc: + delivery = sum(q_val[i_c, k, t] for k in vehicles) + dem = d(omega, i_c, t) + prev = I_cust[i_c, t - 1] + # Balance: prev + delivery + e = dem + I_new + I_cust[i_c, t] = max(0.0, prev + delivery - dem) + e_val[i_c, t] = max(0.0, dem - prev - delivery) + + # --- Constraint 1 (Eq.2): Plant inventory balance --- + lhs = I_plant[t - 1] + p_val[t] + rhs = total_delivered + I_plant[t] + record_violation(1, lhs, rhs, '=', + f"Constraint 1 (Eq.2): Plant balance violated in period {t}, scenario {omega}: " + f"LHS={lhs:.6f} != RHS={rhs:.6f}") + + # --- Constraint 3 (Eq.4): Plant inventory capacity --- + record_violation(3, I_plant[t], float(L[0]), '<=', + f"Constraint 3 (Eq.4): Plant inventory {I_plant[t]:.4f} > capacity {L[0]} " + f"in period {t}, scenario {omega}") + + # --- Constraint 5 (Eq.6): Production capacity --- + record_violation(5, p_val[t], p_max[t], '<=', + f"Constraint 5 (Eq.6): Production {p_val[t]:.4f} > capacity {p_max[t]:.4f} " + f"in period {t}, scenario {omega}") + + for i_c in Nc: + delivery = sum(q_val[i_c, k, t] for k in vehicles) + dem = d(omega, i_c, t) + + # --- Constraint 2 (Eq.3): Customer inventory balance --- + lhs2 = I_cust[i_c, t - 1] + delivery + e_val[i_c, t] + rhs2 = dem + I_cust[i_c, t] + record_violation(2, lhs2, rhs2, '=', + f"Constraint 2 (Eq.3): Customer {i_c} balance violated in period {t}, " + f"scenario {omega}: LHS={lhs2:.6f} != RHS={rhs2:.6f}") + + # --- Constraint 4 (Eq.5): Customer inventory capacity --- + record_violation(4, I_cust[i_c, t] + dem, float(L[i_c]), '<=', + f"Constraint 4 (Eq.5): Customer {i_c} inventory+demand " + f"{I_cust[i_c, t] + dem:.4f} > capacity {L[i_c]} " + f"in period {t}, scenario {omega}") + + # --- Constraint 7 (Eq.8): Delivery only if visited --- + for k in vehicles: + mp = M_prime_val(i_c, t, omega) * z[i_c, k, t] + record_violation(7, q_val[i_c, k, t], mp, '<=', + f"Constraint 7 (Eq.8): Delivery to customer {i_c} by vehicle {k} " + f"in period {t}, scenario {omega}: {q_val[i_c, k, t]:.4f} > {mp:.4f}") + + # --- Constraint 6 (Eq.7): Vehicle load capacity --- + for k in vehicles: + total_load = sum(q_val[i_c, k, t] for i_c in Nc) + cap_rhs = Q * z[0, k, t] + record_violation(6, total_load, float(cap_rhs), '<=', + f"Constraint 6 (Eq.7): Vehicle {k} load {total_load:.4f} > capacity " + f"{cap_rhs:.4f} in period {t}, scenario {omega}") + + # --- Constraint 11 (Eq.12): Nonnegativity --- + for t in periods: + record_violation(11, 0.0, p_val[t], '<=', + f"Constraint 11 (Eq.12): p_{t} negative ({p_val[t]:.6f}) in scenario {omega}") + record_violation(11, 0.0, I_plant[t], '<=', + f"Constraint 11 (Eq.12): I_plant_{t} negative ({I_plant[t]:.6f}) in scenario {omega}") + for i_c in Nc: + record_violation(11, 0.0, I_cust[i_c, t], '<=', + f"Constraint 11 (Eq.12): I_{i_c}_{t} negative ({I_cust[i_c, t]:.6f}) " + f"in scenario {omega}") + record_violation(11, 0.0, e_val[i_c, t], '<=', + f"Constraint 11 (Eq.12): e_{i_c}_{t} negative ({e_val[i_c, t]:.6f}) " + f"in scenario {omega}") + for k in vehicles: + record_violation(11, 0.0, q_val[i_c, k, t], '<=', + f"Constraint 11 (Eq.12): q_{i_c}_{k}_{t} negative " + f"({q_val[i_c, k, t]:.6f}) in scenario {omega}") + + # ===================================================================== + # Constraint 8 (Eq.9): Each customer visited at most once per period + # sum_k z_{ikt} <= 1 for all i in Nc, t in T + # ===================================================================== + for i_c in Nc: + for t in periods: + lhs = sum(z[i_c, k, t] for k in vehicles) + record_violation(8, float(lhs), 1.0, '<=', + f"Constraint 8 (Eq.9): Customer {i_c} visited {lhs} times " + f"(> 1) in period {t}") + + # ===================================================================== + # Constraint 9 (Eq.10): Degree constraints + # sum_{(j,j') in delta(i)} x_{jj'kt} = 2 * z_{ikt} + # ===================================================================== + for i_node in N: + for k in vehicles: + for t in periods: + inc_edges = [(a, b) for (a, b) in edges if a == i_node or b == i_node] + lhs = sum(x[a, b, k, t] for (a, b) in inc_edges) + rhs = 2.0 * z[i_node, k, t] + record_violation(9, float(lhs), float(rhs), '=', + f"Constraint 9 (Eq.10): Degree of node {i_node} by vehicle {k} " + f"in period {t}: sum_edges={lhs} != 2*z={rhs}") + + # ===================================================================== + # Constraint 10 (Eq.11): Subtour elimination + # For each vehicle k and period t, check that visited customers form + # a connected component through the depot (no customer-only subtours). + # ===================================================================== + for k in vehicles: + for t in periods: + visited = [i_c for i_c in Nc if z[i_c, k, t] > 0.5] + if len(visited) < 2: + continue + + # Build adjacency among all nodes with edges + adj = defaultdict(set) + for (a, b) in edges: + if x[a, b, k, t] > 0.5: + adj[a].add(b) + adj[b].add(a) + + # BFS from depot to find reachable customers + reachable = set() + stack = [0] + seen = set() + while stack: + node = stack.pop() + if node in seen: + continue + seen.add(node) + if node in visited: + reachable.add(node) + for nb in adj[node]: + if nb not in seen: + stack.append(nb) + + # Customers visited but not reachable from depot form subtours + subtour_nodes = set(visited) - reachable + if subtour_nodes: + # Find the subtour components + remaining = set(subtour_nodes) + while remaining: + start = next(iter(remaining)) + comp = set() + stack = [start] + while stack: + node = stack.pop() + if node in comp: + continue + comp.add(node) + for nb in adj[node]: + if nb in remaining and nb not in comp: + stack.append(nb) + remaining -= comp + S = comp + # Compute violation: sum x_{ij} for (i,j) in E(S) <= sum z - z_e + E_S = [(a, b) for (a, b) in edges if a in S and b in S] + sum_x = sum(x[a, b, k, t] for (a, b) in E_S) + for e_node in S: + rhs_val = sum(z[i_c, k, t] for i_c in S) - z[e_node, k, t] + violation_amount = max(0.0, sum_x - rhs_val) + if violation_amount > tol: + normalizer = max(abs(rhs_val), eps) + ratio = violation_amount / normalizer + violated_set.add(10) + violations_list.append({ + 'constraint': 10, + 'msg': (f"Constraint 10 (Eq.11): Subtour among customers " + f"{sorted(S)} by vehicle {k} in period {t}"), + 'lhs': float(sum_x), + 'rhs': float(rhs_val), + 'raw_excess': float(violation_amount), + 'normalizer': float(normalizer), + 'ratio': float(ratio), + }) + break # one violation per component suffices + + # ===================================================================== + # Constraint 12 (Eq.13): Binary y_t, z_{ikt} + # ===================================================================== + for t in periods: + val = y[t] + if abs(val - round(val)) > tol or round(val) not in (0, 1): + record_violation(12, float(val), float(round(val)), '=', + f"Constraint 12 (Eq.13): y_{t} = {val} is not binary") + + for i_node in N: + for k in vehicles: + for t in periods: + val = z[i_node, k, t] + if abs(val - round(val)) > tol or round(val) not in (0, 1): + record_violation(12, float(val), float(round(val)), '=', + f"Constraint 12 (Eq.13): z_{i_node}_{k}_{t} = {val} is not binary") + + # ===================================================================== + # Constraint 13 (Eq.14): Binary x for non-depot edges (i != 0) + # ===================================================================== + for (i_e, j_e) in edges: + if i_e == 0: + continue + for k in vehicles: + for t in periods: + val = x[i_e, j_e, k, t] + if abs(val - round(val)) > tol or round(val) not in (0, 1): + record_violation(13, float(val), float(round(val)), '=', + f"Constraint 13 (Eq.14): x_{i_e}_{j_e}_{k}_{t} = {val} " + f"is not binary (non-depot edge)") + + # ===================================================================== + # Constraint 14 (Eq.15): x at depot edges in {0, 1, 2} + # ===================================================================== + for j_e in Nc: + for k in vehicles: + for t in periods: + val = x[0, j_e, k, t] + if abs(val - round(val)) > tol or round(val) not in (0, 1, 2): + record_violation(14, float(val), float(round(val)), '=', + f"Constraint 14 (Eq.15): x_0_{j_e}_{k}_{t} = {val} " + f"not in {{0,1,2}} (depot edge)") + + # ===================================================================== + # Constraint 15 (Eq.16): z_{ikt} <= z_{0kt} for i in Nc + # ===================================================================== + for i_c in Nc: + for k in vehicles: + for t in periods: + lhs = float(z[i_c, k, t]) + rhs = float(z[0, k, t]) + record_violation(15, lhs, rhs, '<=', + f"Constraint 15 (Eq.16): z_{i_c}_{k}_{t}={lhs} > " + f"z_0_{k}_{t}={rhs} (customer visited without vehicle dispatch)") + + # ===================================================================== + # Constraint 16 (Eq.17): x_{ijkt} <= z_{ikt} and x_{ijkt} <= z_{jkt} + # for (i,j) in E(Nc) + # ===================================================================== + for (i_e, j_e) in edges_Nc: + for k in vehicles: + for t in periods: + val = float(x[i_e, j_e, k, t]) + zi = float(z[i_e, k, t]) + zj = float(z[j_e, k, t]) + record_violation(16, val, zi, '<=', + f"Constraint 16 (Eq.17): x_{i_e}_{j_e}_{k}_{t}={val} > " + f"z_{i_e}_{k}_{t}={zi}") + record_violation(16, val, zj, '<=', + f"Constraint 16 (Eq.17): x_{i_e}_{j_e}_{k}_{t}={val} > " + f"z_{j_e}_{k}_{t}={zj}") + + # ===================================================================== + # Constraint 17 (Eq.18): z_{0kt} >= z_{0,k+1,t} (vehicle symmetry) + # Only applies when m > 1 + # ===================================================================== + if m > 1: + for k in range(1, m): + for t in periods: + lhs = float(z[0, k, t]) + rhs = float(z[0, k + 1, t]) + record_violation(17, lhs, rhs, '>=', + f"Constraint 17 (Eq.18): z_0_{k}_{t}={lhs} < " + f"z_0_{k+1}_{t}={rhs} (vehicle ordering violated)") + + # ===================================================================== + # Constraint 18 (Eq.19): Lexicographic ordering + # sum_{i=1}^{j} 2^{j-i} z_{ikt} >= sum_{i=1}^{j} 2^{j-i} z_{i,k+1,t} + # Only applies when m > 1 + # ===================================================================== + if m > 1: + for j_node in Nc: + for k in range(1, m): + for t in periods: + lhs = sum(2 ** (j_node - i_c) * z[i_c, k, t] + for i_c in range(1, j_node + 1)) + rhs = sum(2 ** (j_node - i_c) * z[i_c, k + 1, t] + for i_c in range(1, j_node + 1)) + record_violation(18, float(lhs), float(rhs), '>=', + f"Constraint 18 (Eq.19): Lexicographic ordering violated " + f"for j={j_node}, k={k}, t={t}: {lhs} < {rhs}") + + # ===================================================================== + # Constraints 19-22 (Eqs.20-23): Nonanticipativity (M-SPRP only) + # These only apply to multistage problems. For 2-SPRP, first-stage + # variables are scenario-independent by construction, so these are + # trivially satisfied. + # ===================================================================== + # (Skipped for 2-SPRP; the solution format already has scenario-independent y, z, x.) + + # ===================================================================== + # Constraint 23: exact objective consistency. + # The public witness contains first-stage routing decisions but omits + # scenario recourse variables. Fix those decisions and solve the continuous + # recourse LP so the reported expected cost is certified in both directions. + try: + reported = float(sol["objective_value"]) + if not math.isfinite(reported): + raise ValueError("objective_value must be finite") + if instance_path is None: + raise ValueError("instance_path is required for solverless validation") + certified = _frontieror_solverless_objective(data, sol, instance_path) + objective_tolerance = max( + 1e-4, + 1e-8 * max(abs(reported), abs(certified)), + ) + objective_difference = abs(reported - certified) + if objective_difference > objective_tolerance: + violated_set.add(23) + violations_list.append({ + "constraint": 23, + "msg": ( + "Constraint 23 (exact objective consistency): reported " + f"objective_value={reported:.10g} differs from fixed-decision " + f"recourse value={certified:.10g}; " + f"difference={objective_difference:.6g}, " + f"tolerance={objective_tolerance:.6g}" + ), + "lhs": float(reported), + "rhs": float(certified), + "raw_excess": float(objective_difference), + "normalizer": float(max(abs(certified), eps)), + "ratio": float(objective_difference / max(abs(certified), eps)), + }) + except Exception as exc: + violated_set.add(23) + violations_list.append({ + "constraint": 23, + "msg": ( + "Constraint 23 (exact objective consistency) could not certify " + f"the candidate: {type(exc).__name__}: {exc}" + ), + "lhs": 1.0, + "rhs": 0.0, + "raw_excess": 1.0, + "normalizer": 1.0, + "ratio": 1.0, + }) + + # ===================================================================== + # Aggregate results + # ===================================================================== + # Build unique violation messages per constraint + msg_per_constraint = defaultdict(list) + for v in violations_list: + msg_per_constraint[v['constraint']].append(v['msg']) + + # Deduplicate messages: summarize if too many + final_messages = [] + for c_idx in sorted(violated_set): + msgs = msg_per_constraint[c_idx] + if len(msgs) <= 3: + final_messages.extend(msgs) + else: + final_messages.append(msgs[0]) + final_messages.append(f" ... and {len(msgs) - 1} more violations of Constraint {c_idx}") + + # Build violation_magnitudes (all entries) + final_magnitudes = [] + for v in violations_list: + final_magnitudes.append({ + 'constraint': v['constraint'], + 'lhs': v['lhs'], + 'rhs': v['rhs'], + 'raw_excess': v['raw_excess'], + 'normalizer': v['normalizer'], + 'ratio': v['ratio'], + }) + + result = { + 'feasible': len(violated_set) == 0, + 'violated_constraints': sorted(violated_set), + 'violations': final_messages, + 'violation_magnitudes': final_magnitudes, + } + return result + + +def main(): + parser = argparse.ArgumentParser( + description="Feasibility checker for the Stochastic Production Routing Problem" + ) + parser.add_argument('--instance_path', type=str, required=True, + help="Path to JSON instance file") + parser.add_argument('--solution_path', type=str, required=True, + help="Path to JSON solution file") + parser.add_argument('--result_path', type=str, required=True, + help="Path to write JSON feasibility result") + args = parser.parse_args() + + with open(args.instance_path) as f: + data = json.load(f) + with open(args.solution_path) as f: + sol = json.load(f) + _frontieror_contract_result = _frontieror_validate_solution_contract(sol) + if _frontieror_contract_result is not None: + with open(args.result_path, "w") as _frontieror_result_handle: + json.dump( + _frontieror_contract_result, + _frontieror_result_handle, + indent=2, + ) + return + + result = check_feasibility(data, sol, args.instance_path) + + with open(args.result_path, 'w') as f: + json.dump(result, f, indent=2) + + if result['feasible']: + print("FEASIBLE: All hard constraints satisfied.") + else: + print(f"INFEASIBLE: {len(result['violated_constraints'])} constraint(s) violated: " + f"{result['violated_constraints']}") + for msg in result['violations']: + print(f" - {msg}") + + +if __name__ == '__main__': + try: + main() + except Exception as _frontieror_unexpected_error: + if not _frontieror_write_unexpected_rejection( + _frontieror_unexpected_error + ): + raise diff --git a/adulyasak2015/gurobi_code.py b/adulyasak2015/gurobi_code.py new file mode 100644 index 0000000000000000000000000000000000000000..d4bbfbf2c09c6b02501c6965c1e87598902e73aa --- /dev/null +++ b/adulyasak2015/gurobi_code.py @@ -0,0 +1,366 @@ +""" +Gurobi implementation of the Two-Stage Stochastic Production Routing Problem (2-SPRP). +Model (1)-(19): 2-BF (Two-Stage Base Formulation). +Reference: Adulyasak, Cordeau, Jans (2015), Operations Research. +""" +import argparse +import json +import math +import gurobipy as gp +from gurobipy import GRB +import os as _os, sys as _sys +# Walk up from this file's directory to find repo root (containing scripts/). +_repo = _os.path.dirname(_os.path.abspath(__file__)) +while _repo != _os.path.dirname(_repo) and not _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _repo = _os.path.dirname(_repo) +if _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _sys.path.insert(0, _repo) +try: + from scripts.utils.gurobi_log_helper import install_gurobi_logger +except ImportError: + def install_gurobi_logger(log_path): # no-op fallback when scripts/ unavailable + pass + + +def load_instance(path): + with open(path) as f: + return json.load(f) + + +def solve(instance_path, solution_path, time_limit): + data = load_instance(instance_path) + + n = data['n'] # number of customers + T = data['T'] # number of periods + m = data['m'] # number of vehicles + Q = data['Q'] # vehicle capacity + C = data['C'] # production capacity per period + f_cost = data['f'] # fixed production setup cost + u = data['u'] # unit production cost + h = data['h'] # holding costs, h[0]=plant, h[1..n]=customers + L = data['L'] # inventory capacities, L[0]=plant, L[1..n]=customers + I0 = data['I0'] # initial inventories + sigma = data['sigma'] # penalty costs for customers (list of length n) + c = data['transportation_costs'] # c[i][j], i,j in 0..n + rho = data['scenario_probabilities'] # rho[omega] + demand_sc = data['demand_scenarios'] # demand_sc[omega][cust_idx][period_idx] (0-indexed) + n_omega = data['n_scenarios'] + + # Index sets (1-indexed periods, vehicles; 0-indexed customers shifted by 1) + Nc = list(range(1, n + 1)) # customers + N = list(range(0, n + 1)) # all nodes (0=plant) + periods = list(range(1, T + 1)) + vehicles = list(range(1, m + 1)) + scenarios = list(range(n_omega)) + + # All undirected edges (i=t, i in Nc} d_{is,omega}}""" + rem = sum(d(omega, i, s) for i in Nc for s in range(t, T + 1)) + return min(C, Q, rem) + + def M_prime(i, t, omega): + """M'_{it,omega} = min{L_i, Q, sum_{s>=t} d_{is,omega}}""" + rem = sum(d(omega, i, s) for s in range(t, T + 1)) + return min(L[i], Q, rem) + + # ---------------------------------------------------------------- + model = gp.Model("2SPRP_2BF") + model.setParam("Threads", 1) + model.setParam("TimeLimit", time_limit) + model.setParam("LazyConstraints", 1) + + # --- First-stage variables --- + y = {t: model.addVar(vtype=GRB.BINARY, name=f"y_{t}") for t in periods} + + z = {} + for i in N: + for k in vehicles: + for t in periods: + z[i, k, t] = model.addVar(vtype=GRB.BINARY, name=f"z_{i}_{k}_{t}") + + x = {} + for (i, j) in edges: + for k in vehicles: + for t in periods: + if i == 0: + # Depot edge: can be traversed 0,1,2 times (out-and-back) + x[i, j, k, t] = model.addVar(vtype=GRB.INTEGER, lb=0, ub=2, + name=f"x_{i}_{j}_{k}_{t}") + else: + x[i, j, k, t] = model.addVar(vtype=GRB.BINARY, + name=f"x_{i}_{j}_{k}_{t}") + + # --- Second-stage variables --- + p = {} + I_var = {} + q_var = {} + e_var = {} + + for omega in scenarios: + for t in periods: + p[t, omega] = model.addVar(lb=0.0, name=f"p_{t}_{omega}") + for i in N: + for t in periods: + I_var[i, t, omega] = model.addVar(lb=0.0, name=f"I_{i}_{t}_{omega}") + for i in Nc: + for t in periods: + e_var[i, t, omega] = model.addVar(lb=0.0, name=f"e_{i}_{t}_{omega}") + for k in vehicles: + for t in periods: + q_var[i, k, t, omega] = model.addVar(lb=0.0, name=f"q_{i}_{k}_{t}_{omega}") + + model.update() + + # --- Objective (1) --- + routing_cost = gp.quicksum( + c[i][j] * x[i, j, k, t] + for (i, j) in edges for k in vehicles for t in periods + ) + setup_cost = gp.quicksum(f_cost * y[t] for t in periods) + + second_stage = gp.quicksum( + rho[omega] * ( + gp.quicksum(u * p[t, omega] for t in periods) + + gp.quicksum(h[i] * I_var[i, t, omega] for i in N for t in periods) + + gp.quicksum(sigma[i - 1] * e_var[i, t, omega] for i in Nc for t in periods) + ) + for omega in scenarios + ) + + model.setObjective(setup_cost + routing_cost + second_stage, GRB.MINIMIZE) + + # --- Constraint (2): Plant inventory balance --- + for t in periods: + for omega in scenarios: + I_prev = I0[0] if t == 1 else I_var[0, t - 1, omega] + model.addConstr( + I_prev + p[t, omega] + == gp.quicksum(q_var[i, k, t, omega] for i in Nc for k in vehicles) + + I_var[0, t, omega], + name=f"plant_bal_{t}_{omega}" + ) + + # --- Constraint (3): Customer inventory balance --- + for i in Nc: + for t in periods: + for omega in scenarios: + I_prev = I0[i] if t == 1 else I_var[i, t - 1, omega] + model.addConstr( + I_prev + gp.quicksum(q_var[i, k, t, omega] for k in vehicles) + + e_var[i, t, omega] + == d(omega, i, t) + I_var[i, t, omega], + name=f"cust_bal_{i}_{t}_{omega}" + ) + + # --- Constraint (4): Max plant inventory --- + for t in periods: + for omega in scenarios: + model.addConstr(I_var[0, t, omega] <= L[0], name=f"plant_inv_{t}_{omega}") + + # --- Constraint (5): Max customer inventory --- + for i in Nc: + for t in periods: + for omega in scenarios: + model.addConstr( + I_var[i, t, omega] + d(omega, i, t) <= L[i], + name=f"cust_inv_{i}_{t}_{omega}" + ) + + # --- Constraint (6): Production capacity --- + for t in periods: + for omega in scenarios: + model.addConstr( + p[t, omega] <= M_t(t, omega) * y[t], + name=f"prod_cap_{t}_{omega}" + ) + + # --- Constraint (7): Vehicle load capacity --- + for k in vehicles: + for t in periods: + for omega in scenarios: + model.addConstr( + gp.quicksum(q_var[i, k, t, omega] for i in Nc) <= Q * z[0, k, t], + name=f"veh_load_{k}_{t}_{omega}" + ) + + # --- Constraint (8): Delivery only if customer visited --- + for i in Nc: + for k in vehicles: + for t in periods: + for omega in scenarios: + model.addConstr( + q_var[i, k, t, omega] <= M_prime(i, t, omega) * z[i, k, t], + name=f"del_cap_{i}_{k}_{t}_{omega}" + ) + + # --- Constraint (9): Each customer visited at most once per period --- + for i in Nc: + for t in periods: + model.addConstr( + gp.quicksum(z[i, k, t] for k in vehicles) <= 1, + name=f"visit_{i}_{t}" + ) + + # --- Constraint (10): Degree constraints --- + for i in N: + for k in vehicles: + for t in periods: + inc = [(a, b) for (a, b) in edges if a == i or b == i] + model.addConstr( + gp.quicksum(x[a, b, k, t] for (a, b) in inc) == 2 * z[i, k, t], + name=f"degree_{i}_{k}_{t}" + ) + + # --- Valid inequality (16): z_{ikt} <= z_{0kt} --- + for i in Nc: + for k in vehicles: + for t in periods: + model.addConstr(z[i, k, t] <= z[0, k, t], name=f"dispatch_{i}_{k}_{t}") + + # --- Valid inequality (17): edge implies both endpoints visited --- + for (i, j) in edges_Nc: + for k in vehicles: + for t in periods: + model.addConstr(x[i, j, k, t] <= z[i, k, t], name=f"ev_i_{i}_{j}_{k}_{t}") + model.addConstr(x[i, j, k, t] <= z[j, k, t], name=f"ev_j_{i}_{j}_{k}_{t}") + + # --- Valid inequalities (18)-(19): symmetry breaking for multiple vehicles --- + if m > 1: + for k in range(1, m): + for t in periods: + model.addConstr(z[0, k, t] >= z[0, k + 1, t], name=f"sym_use_{k}_{t}") + for j_node in Nc: + for k in range(1, m): + for t in periods: + model.addConstr( + gp.quicksum(2 ** (j_node - i) * z[i, k, t] for i in range(1, j_node + 1)) + >= gp.quicksum(2 ** (j_node - i) * z[i, k + 1, t] for i in range(1, j_node + 1)), + name=f"lex_{j_node}_{k}_{t}" + ) + + # --- Lazy SEC callback (constraint 11) --- + def find_subtours(x_val, z_val, k, t): + """Find customer-only subtours (not connected to depot) in solution.""" + visited = [i for i in Nc if z_val.get((i, k, t), 0) > 0.5] + if len(visited) < 2: + return [] + + # Build adjacency among visited customers + adj = {i: [] for i in visited} + for (a, b) in edges_Nc: + if a in visited and b in visited: + val = x_val.get((a, b, k, t), 0) + if val > 0.5: + adj[a].append(b) + adj[b].append(a) + + # Find connected components (ignoring depot) + seen = set() + components = [] + for start in visited: + if start not in seen: + comp = [] + stack = [start] + while stack: + node = stack.pop() + if node not in seen: + seen.add(node) + comp.append(node) + for nb in adj.get(node, []): + if nb not in seen: + stack.append(nb) + components.append(comp) + # Only components not connected to depot are subtours + # Check depot connectivity + depot_neighbors = set() + for (a, b) in edges: + if a == 0 and b in visited and x_val.get((a, b, k, t), 0) > 0.5: + depot_neighbors.add(b) + elif b == 0 and a in visited and x_val.get((a, b, k, t), 0) > 0.5: + depot_neighbors.add(a) + + subtours = [] + for comp in components: + if not any(v in depot_neighbors for v in comp): + subtours.append(comp) + return subtours + + def callback(model, where): + if where == GRB.Callback.MIPSOL: + x_vals = {key: model.cbGetSolution(x[key]) for key in x} + z_vals = {key: model.cbGetSolution(z[key]) for key in z} + + for k in vehicles: + for t in periods: + subtours = find_subtours(x_vals, z_vals, k, t) + for comp in subtours: + S = set(comp) + E_S = [(a, b) for (a, b) in edges if a in S and b in S] + e_node = comp[0] + model.cbLazy( + gp.quicksum(x[a, b, k, t] for (a, b) in E_S) + <= gp.quicksum(z[i, k, t] for i in S) - z[e_node, k, t] + ) + + model.optimize(callback) + + # --- Extract and write solution --- + result = {"objective_value": None} + + if model.SolCount > 0: + result["objective_value"] = model.ObjVal + result["gap"] = model.MIPGap + result["status"] = model.Status + + y_sol = {str(t): round(y[t].X) for t in periods} + result["y"] = y_sol + + z_sol = {} + for i in N: + for k in vehicles: + for t in periods: + v = z[i, k, t].X + if v > 0.5: + z_sol[f"{i}_{k}_{t}"] = 1 + result["z"] = z_sol + + x_sol = {} + for (i, j) in edges: + for k in vehicles: + for t in periods: + v = x[i, j, k, t].X + if v > 0.5: + x_sol[f"{i}_{j}_{k}_{t}"] = round(v) + result["x"] = x_sol + + with open(solution_path, 'w') as fp: + json.dump(result, fp, indent=2) + + print(f"Objective value: {result['objective_value']}") + return result + + +def main(): + parser = argparse.ArgumentParser(description="Solve 2-SPRP with Gurobi (2-BF formulation)") + parser.add_argument("--instance_path", type=str, required=True, + help="Path to JSON instance file") + parser.add_argument("--solution_path", type=str, required=True, + help="Path to write solution JSON") + parser.add_argument("--time_limit", type=int, default=3600, + help="Solver time limit in seconds") + parser.add_argument("--log_path", type=str, default=None, help="Path to log incumbent solutions") + args = parser.parse_args() + install_gurobi_logger(args.log_path) + solve(args.instance_path, args.solution_path, args.time_limit) + + +if __name__ == "__main__": + main() diff --git a/adulyasak2015/gurobi_feasi_result/large_feasi_result_1.json b/adulyasak2015/gurobi_feasi_result/large_feasi_result_1.json new file mode 100644 index 0000000000000000000000000000000000000000..e305b82c3d6732211d5033415df1a105476e9cf1 --- /dev/null +++ b/adulyasak2015/gurobi_feasi_result/large_feasi_result_1.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3269392edcbb7720882e77caadb88b4a9cf2fda04cf482cebd23baace4a531fd +size 102 diff --git a/adulyasak2015/gurobi_feasi_result/large_feasi_result_2.json b/adulyasak2015/gurobi_feasi_result/large_feasi_result_2.json new file mode 100644 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@@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:14d69b67d716fa56028ddc806bceb14da45ed4b1b2190c889e798f0711ecfb47 +size 39960 diff --git a/adulyasak2015/instance_schema.json b/adulyasak2015/instance_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..0073a72c182279444371c998e1a2ab4b825e9825 --- /dev/null +++ b/adulyasak2015/instance_schema.json @@ -0,0 +1,21 @@ +{ + "n": " Number of customers to be served.", + "T": " Number of time periods in the planning horizon.", + "m": " Number of identical vehicles available at the plant.", + "Q": " Maximum load capacity of each vehicle.", + "C": " Maximum production quantity at the plant per period.", + "f": " Fixed cost incurred whenever production takes place in a period.", + "u": " Cost per unit of product produced at the plant.", + "epsilon": " Half-width of the demand uncertainty interval as a fraction of nominal demand.", + "alpha_hat": " Multiplier used to compute the per-unit penalty cost for unmet customer demand.", + "coordinates": " Spatial coordinates of each node, where index 0 is the plant and indices 1 through n are customers.", + "transportation_costs": " Symmetric travel cost between each pair of nodes, where index 0 is the plant.", + "h": " Per-unit inventory holding cost at each node per period, where index 0 is the plant.", + "L": " Maximum inventory storage capacity at each node, where index 0 is the plant.", + "I0": " Inventory on hand at each node at the start of the planning horizon, where index 0 is the plant.", + "d_bar": " Nominal (expected) demand for each customer in each period.", + "sigma": " Per-unit penalty cost for unmet demand at each customer.", + "n_scenarios": " Number of demand scenarios considered.", + "scenario_probabilities": " Probability of each demand scenario being realized.", + "demand_scenarios": " Realized demand for each customer in each period under each scenario." +} \ No newline at end of file diff --git a/adulyasak2015/mathematical_formulation.md b/adulyasak2015/mathematical_formulation.md new file mode 100644 index 0000000000000000000000000000000000000000..6838780bf90b0635f83781cbd63256fb0eac7c0a --- /dev/null +++ b/adulyasak2015/mathematical_formulation.md @@ -0,0 +1,109 @@ +# Original Formulation: Stochastic Production Routing Problem (SPRP) + +*Source: Benders Decomposition for Production Routing Under Demand Uncertainty, Yossiri Adulyasak, Jean-François Cordeau, Raf Jans, Operations Research, 2015.* + +The paper studies the Stochastic Production Routing Problem (SPRP). The canonical model below is the *two-stage SPRP formulation (2-BF)*, Model (1)–(19), introduced first in §2.2 as the definition of the problem; its notation $(x,y,z,p,I,q,e)$ is carried into every subsequent section. The multistage variant (M-BF, adding nonanticipativity constraints (20)–(23)) is given under Variants. + +## Sets and Indices + +$$\begin{align*} +&\Omega && \text{finite set of demand scenarios, indexed by } \omega\\ +&G=(N,E) && \text{complete undirected graph}\\ +&N=\{0,\ldots,n\} && \text{set of nodes; node } 0 \text{ is the production plant}\\ +&N_c=N\setminus\{0\} && \text{set of customers}\\ +&E=\{(i,j): i,j\in N,\ i= self.best_obj: + return + if self.sense == "maximize" and objective_value <= self.best_obj: + return + + elapsed = time.time() - self.start_time + + # Throttle writes + if self.best_obj is not None and elapsed - self._last_log_time < self.min_interval: + self.best_obj = objective_value + return + + self.best_obj = objective_value + self._last_log_time = elapsed + + with open(self.log_path, "a") as f: + f.write(json.dumps({"time": round(elapsed, 3), + "objective_value": objective_value}) + "\n") diff --git a/adulyasak2015/solution_schema.json b/adulyasak2015/solution_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..c42f83d07c440f54d40814dc77de5a3045e27876 --- /dev/null +++ b/adulyasak2015/solution_schema.json @@ -0,0 +1,7 @@ +{ + "objective_value": " Total expected cost represented by the complete submitted two-stage witness.", + "model_variables": " Sparse complete first- and second-stage solution. Omitted model variables are exactly zero.", + "y": " Whether production is set up at the plant in each period (1 = yes, 0 = no).", + "z": " Whether a node is visited by a vehicle in a given period (1 = yes), where node 0 indicates the vehicle is dispatched from the plant.", + "x": " Number of times the edge between two nodes is traversed by a vehicle in a given period (1 for customer-customer edges, up to 2 for plant-customer edges)." +} diff --git a/adulyasak2015/solverless_models/14d69b67d716fa56028ddc806bceb14da45ed4b1b2190c889e798f0711ecfb47.npz b/adulyasak2015/solverless_models/14d69b67d716fa56028ddc806bceb14da45ed4b1b2190c889e798f0711ecfb47.npz new file mode 100644 index 0000000000000000000000000000000000000000..b4d6f703be1172a0fc8113b4d5d4ccafcb9bb536 --- /dev/null +++ b/adulyasak2015/solverless_models/14d69b67d716fa56028ddc806bceb14da45ed4b1b2190c889e798f0711ecfb47.npz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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b/adulyasak2015/solverless_models/fb4ba36fa7ea76e5257c30f766f3837a294b653c03c51a5f7d0f5bcd0a7da3ea.npz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:94729d9ff30bb96c3a61b9cabae88d69a9a1515ea8e7c6b5670bf4fd7f57a03e +size 17956953 diff --git a/adulyasak2015/solverless_witness.py b/adulyasak2015/solverless_witness.py new file mode 100644 index 0000000000000000000000000000000000000000..6e8d1c0a2a83a5aa314a7405d6b50f6d5dacaf8c --- /dev/null +++ b/adulyasak2015/solverless_witness.py @@ -0,0 +1,155 @@ +#!/usr/bin/env python3 +"""Solver-free validation of complete sparse optimization witnesses.""" + +from __future__ import annotations + +import hashlib +from pathlib import Path +from typing import Any + +import numpy as np + +WITNESS_FIELD = "model_variables" + + +def _digest(path: str | Path) -> str: + value = hashlib.sha256() + with open(path, "rb") as handle: + for block in iter(lambda: handle.read(1024 * 1024), b""): + value.update(block) + return value.hexdigest() + + +def _strings(values: np.ndarray) -> list[str]: + return [str(value) for value in values.tolist()] + + +def validate_complete_witness( + instance_path: str | Path, + solution: dict[str, Any], + case_directory: str | Path, + *, + absolute_tolerance: float = 1e-5, + relative_tolerance: float = 1e-7, +) -> tuple[float | None, list[str], dict[str, float]]: + """Return recomputed objective, validation errors, and diagnostics.""" + errors: list[str] = [] + diagnostics: dict[str, float] = {} + witness = solution.get(WITNESS_FIELD) + if not isinstance(witness, dict): + return None, [f"{WITNESS_FIELD} must be an object"], diagnostics + + digest = _digest(instance_path) + snapshot = Path(case_directory) / "solverless_models" / f"{digest}.npz" + if not snapshot.is_file(): + return None, [f"no trusted solverless model for instance sha256={digest}"], diagnostics + + with np.load(snapshot, allow_pickle=False) as model: + if str(model["instance_sha256"].item()) != digest: + return None, ["trusted model instance digest mismatch"], diagnostics + names = _strings(model["var_names"]) + name_to_index = {name: index for index, name in enumerate(names)} + values = np.zeros(len(names), dtype=np.float64) + unknown: list[str] = [] + for name, raw in witness.items(): + index = name_to_index.get(str(name)) + if index is None: + if len(unknown) < 5: + unknown.append(str(name)) + continue + if isinstance(raw, bool) or not isinstance(raw, (int, float)): + errors.append(f"model variable {name!r} must be a finite number") + continue + value = float(raw) + if not np.isfinite(value): + errors.append(f"model variable {name!r} must be finite") + continue + values[index] = value + if unknown: + errors.append("unknown model variable(s): " + ", ".join(unknown)) + + lower = model["lower_bounds"] + upper = model["upper_bounds"] + scale = np.maximum(1.0, np.maximum(np.abs(lower), np.abs(upper))) + scale[~np.isfinite(scale)] = 1.0 + bound_tol = absolute_tolerance + relative_tolerance * scale + low_bad = np.flatnonzero(values < lower - bound_tol) + high_bad = np.flatnonzero(values > upper + bound_tol) + if low_bad.size: + diagnostics["max_lower_bound_violation"] = float(np.max(lower[low_bad] - values[low_bad])) + for index in low_bad[:5]: + errors.append(f"lower bound violated: {names[index]}={values[index]:.12g} < {lower[index]:.12g}") + if high_bad.size: + diagnostics["max_upper_bound_violation"] = float(np.max(values[high_bad] - upper[high_bad])) + for index in high_bad[:5]: + errors.append(f"upper bound violated: {names[index]}={values[index]:.12g} > {upper[index]:.12g}") + + integer_mask = model["integer_mask"].astype(bool) + integer_indices = np.flatnonzero(integer_mask) + fractionality = np.abs(values[integer_mask] - np.rint(values[integer_mask])) + bad_local = np.flatnonzero(fractionality > absolute_tolerance) + if bad_local.size: + diagnostics["max_integrality_violation"] = float(np.max(fractionality[bad_local])) + for local_index in bad_local[:5]: + index = integer_indices[local_index] + errors.append(f"integrality violated: {names[index]}={values[index]:.12g}") + + row_count = int(model["constraint_count"].item()) + rows = model["row_indices"] + columns = model["column_indices"] + coefficients = model["coefficients"] + activity = np.bincount(rows, weights=coefficients * values[columns], minlength=row_count) + absolute_activity = np.bincount(rows, weights=np.abs(coefficients * values[columns]), minlength=row_count) + rhs = model["rhs"] + senses = model["senses"] + row_tol = absolute_tolerance + relative_tolerance * np.maximum.reduce( + [np.ones(row_count), np.abs(rhs), absolute_activity] + ) + violation = np.zeros(row_count, dtype=np.float64) + violation[senses == -1] = np.maximum(0.0, activity[senses == -1] - rhs[senses == -1]) + violation[senses == 0] = np.abs(activity[senses == 0] - rhs[senses == 0]) + violation[senses == 1] = np.maximum(0.0, rhs[senses == 1] - activity[senses == 1]) + bad_rows = np.flatnonzero(violation > row_tol) + if bad_rows.size: + diagnostics["max_constraint_violation"] = float(np.max(violation[bad_rows])) + constraint_names = _strings(model["constraint_names"]) + sense_text = {-1: "<=", 0: "==", 1: ">="} + for row in bad_rows[:10]: + errors.append( + f"constraint violated: {constraint_names[row]}: {activity[row]:.12g} " + f"{sense_text[int(senses[row])]} {rhs[row]:.12g} (excess {violation[row]:.6g})" + ) + + objective = float(model["objective_constant"].item()) + objective += float(np.dot(model["linear_objective"], values)) + q_coefficients = model["quadratic_coefficients"] + if q_coefficients.size: + objective += float(np.sum( + q_coefficients * values[model["quadratic_rows"]] * values[model["quadratic_columns"]] + )) + diagnostics["recomputed_objective"] = objective + return objective, errors, diagnostics + + +def require_linked_variables( + solution: dict[str, Any], expected: dict[str, float], *, tolerance: float = 1e-6 +) -> list[str]: + """Ensure public fields and the complete vector describe one plan.""" + witness = solution.get(WITNESS_FIELD) + if not isinstance(witness, dict): + return [f"{WITNESS_FIELD} must be an object"] + errors: list[str] = [] + for name, expected_value in expected.items(): + raw = witness.get(name, 0.0) + try: + actual = float(raw) + except (TypeError, ValueError): + errors.append(f"linked model variable {name!r} is not numeric") + continue + if not np.isfinite(actual) or abs(actual - float(expected_value)) > tolerance: + errors.append( + f"public decision and model witness disagree for {name}: {expected_value} versus {actual}" + ) + if len(errors) >= 10: + break + return errors diff --git a/alba2013/feasibility_check.py b/alba2013/feasibility_check.py new file mode 100644 index 0000000000000000000000000000000000000000..54c62a1c041dcdd9e2a6d931dbce9716d639b7f9 --- /dev/null +++ b/alba2013/feasibility_check.py @@ -0,0 +1,611 @@ +#!/usr/bin/env python3 +""" +Feasibility checker for the Double Traveling Salesman Problem with Multiple Stacks (DTSPMS). + +Checks constraints from the mathematical formulation (Alba Martinez et al. 2013): + Constraint 1 (Eq 2): Out-degree assignment — each vertex has exactly one successor in each tour. + Constraint 2 (Eq 3): In-degree assignment — each vertex has exactly one predecessor in each tour. + Constraint 3 (Eq 4): Subtour elimination — each tour is a single Hamiltonian cycle. + Constraint 4 (Eq 5): Binary domain — x variables are 0 or 1. + Constraint 5 (Eq 6): Loading feasibility — LIFO constraint across stacks linking pickup/delivery tours. + Constraint 6 (Eq 1): Objective consistency — reported objective_value must equal the recomputed + total routing cost (pickup distance + delivery distance) summed over the tours. + +Constraints 7-10 are strengthened valid inequalities (cuts) implied by constraint 5 and are not +checked separately. +""" + +import argparse +import json +from collections import defaultdict + +# FRONTIEROR_RELEASE_CONTRACT_V2 +_FRONTIEROR_REQUIRED_SOLUTION_FIELDS = ('delivery_tour', 'objective_value', 'pickup_tour') +_FRONTIEROR_NONEMPTY_SOLUTION_FIELDS = () +_FRONTIEROR_DERIVED_FIELD_RULES = () +_FRONTIEROR_CONDITIONAL_FIELD_RULES = () + + +def _frontieror_validate_solution_contract(solution): + """Return a structured rejection when the public solution contract is absent.""" + violations = [] + if not isinstance(solution, dict): + violations.append("solution must be a JSON object") + else: + missing = [ + field + for field in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS + if field not in solution or solution[field] is None + ] + if missing: + violations.append( + "missing required solution field(s): " + ", ".join(missing) + ) + + for discriminator, expected_value, fields in _FRONTIEROR_CONDITIONAL_FIELD_RULES: + if solution.get(discriminator) != expected_value: + continue + conditional_missing = [ + field + for field in fields + if field not in solution or solution[field] is None + ] + if conditional_missing: + violations.append( + "missing conditionally required solution field(s): " + + ", ".join(conditional_missing) + ) + + empty = [ + field + for field in _FRONTIEROR_NONEMPTY_SOLUTION_FIELDS + if field in solution and solution[field] in (None, [], {}) + ] + if empty: + violations.append( + "empty required decision witness field(s): " + ", ".join(empty) + ) + + for target, operation, source, source_key in _FRONTIEROR_DERIVED_FIELD_RULES: + try: + reported = float(solution[target]) + if operation == "length": + expected = float(len(solution[source])) + elif operation == "mapping_value": + expected = float(solution[source][source_key]) + else: + raise ValueError("unknown derived-field operation") + except (KeyError, TypeError, ValueError, OverflowError): + violations.append( + f"cannot derive {target} from decision witness field {source}" + ) + continue + if ( + reported != reported + or expected != expected + or reported in (float("inf"), float("-inf")) + or expected in (float("inf"), float("-inf")) + or abs(reported - expected) > 1e-6 + ): + violations.append( + f"{target}={reported} does not match the decision-derived " + f"value {expected}" + ) + + if "objective_value" in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS: + objective = solution.get("objective_value") + if ( + isinstance(objective, bool) + or not isinstance(objective, (int, float)) + or objective != objective + or objective in (float("inf"), float("-inf")) + ): + violations.append("objective_value must be a finite JSON number") + + if not violations: + return None + return { + "feasible": False, + "violated_constraints": ["solution_schema"], + "violations": violations, + "violation_magnitudes": [], + } + + +def _frontieror_write_unexpected_rejection(error): + """Turn malformed candidate/checker exceptions into a normal rejection.""" + import json as _frontieror_json + import sys as _frontieror_sys + + result_path = None + for index, argument in enumerate(_frontieror_sys.argv): + if argument == "--result_path" and index + 1 < len(_frontieror_sys.argv): + result_path = _frontieror_sys.argv[index + 1] + break + if argument.startswith("--result_path="): + result_path = argument.split("=", 1)[1] + break + if not result_path: + return False + try: + with open(result_path, "w") as result_handle: + _frontieror_json.dump( + { + "feasible": False, + "violated_constraints": ["malformed_solution"], + "violations": [ + "checker rejected malformed candidate: " + + type(error).__name__ + + ": " + + str(error)[:500] + ], + "violation_magnitudes": [], + }, + result_handle, + indent=2, + ) + except Exception: + return False + return True + + + +def load_json(path): + with open(path, 'r') as f: + return json.load(f) + + +def build_x_from_tour(tour, n): + """ + Build the x variable dictionary from a tour sequence. + tour is a list of vertices [v0, v1, ..., v_{n}] representing the order of visit. + The tour is a cycle: v_{n} -> v0 is implied. + Returns dict {(i,j): 1} for arcs used. + """ + x = {} + for idx in range(len(tour)): + i = tour[idx] + j = tour[(idx + 1) % len(tour)] + x[(i, j)] = 1 + return x + + +def check_feasibility(instance, solution): + tol = 1e-5 + eps = 1e-5 + + n = instance['parameters']['num_requests_n'] + s = instance['parameters']['num_stacks_s'] + l_cap = instance['parameters']['stack_height_l'] + + V = list(range(n + 1)) # {0, 1, ..., n} + + pickup_tour = solution['pickup_tour'] + delivery_tour = solution['delivery_tour'] + + violations = [] + violation_magnitudes = [] + violated_constraint_set = set() + + # Build x variables from tours + x_P = build_x_from_tour(pickup_tour, n) + x_D = build_x_from_tour(delivery_tour, n) + + # ========================================================================= + # Constraint 1 (Eq 2): Out-degree — sum_j x^T_{ij} = 1 for each i in V^T + # ========================================================================= + for label, x_vals, tour in [("pickup", x_P, pickup_tour), ("delivery", x_D, delivery_tour)]: + for i in V: + out_sum = sum(v for (a, b), v in x_vals.items() if a == i) + rhs = 1.0 + violation_amount = abs(out_sum - rhs) + if violation_amount > tol: + normalizer = max(abs(rhs), eps) + violated_constraint_set.add(1) + violations.append( + f"Constraint 1 (out-degree): Vertex {i} in {label} tour has " + f"out-degree {out_sum}, expected 1" + ) + violation_magnitudes.append({ + "constraint": 1, + "lhs": float(out_sum), + "rhs": float(rhs), + "raw_excess": float(violation_amount), + "normalizer": float(normalizer), + "ratio": float(violation_amount / normalizer) + }) + + # ========================================================================= + # Constraint 2 (Eq 3): In-degree — sum_i x^T_{ij} = 1 for each j in V^T + # ========================================================================= + for label, x_vals, tour in [("pickup", x_P, pickup_tour), ("delivery", x_D, delivery_tour)]: + for j in V: + in_sum = sum(v for (a, b), v in x_vals.items() if b == j) + rhs = 1.0 + violation_amount = abs(in_sum - rhs) + if violation_amount > tol: + normalizer = max(abs(rhs), eps) + violated_constraint_set.add(2) + violations.append( + f"Constraint 2 (in-degree): Vertex {j} in {label} tour has " + f"in-degree {in_sum}, expected 1" + ) + violation_magnitudes.append({ + "constraint": 2, + "lhs": float(in_sum), + "rhs": float(rhs), + "raw_excess": float(violation_amount), + "normalizer": float(normalizer), + "ratio": float(violation_amount / normalizer) + }) + + # ========================================================================= + # Constraint 3 (Eq 4): Subtour elimination — tour must be a single cycle + # visiting all vertices (i.e., a Hamiltonian cycle on V^T). + # We check: (a) tour visits all n+1 vertices, (b) no repeated vertices, + # (c) forms a single connected cycle. + # ========================================================================= + for label, tour in [("pickup", pickup_tour), ("delivery", delivery_tour)]: + tour_set = set(tour) + v_set = set(V) + + # Check all vertices are visited + missing = v_set - tour_set + extra = tour_set - v_set + repeated = len(tour) - len(tour_set) + + if missing: + # The tour doesn't visit all vertices => subtours exist + # Violation: the tour only covers |tour_set| vertices out of n+1 + # This means at least one subtour exists among the missing vertices + # For the subtour constraint: sum_{i in S, j in S} x_{ij} <= |S| - 1 + # If S = tour_set (without depot potentially), a subtour of size |S| + # uses |S| arcs inside S, violating |S| - 1 bound by 1 + violation_amount = 1.0 # at minimum one extra arc in subtour + rhs_val = len(tour_set) - 1.0 if len(tour_set) >= 2 else 0.0 + lhs_val = rhs_val + 1.0 # subtour uses |S| arcs = |S|-1+1 + normalizer = max(abs(rhs_val), eps) + violated_constraint_set.add(3) + violations.append( + f"Constraint 3 (subtour elimination): {label} tour missing vertices " + f"{sorted(missing)}; tour has subtour(s)" + ) + violation_magnitudes.append({ + "constraint": 3, + "lhs": float(lhs_val), + "rhs": float(rhs_val), + "raw_excess": float(violation_amount), + "normalizer": float(normalizer), + "ratio": float(violation_amount / normalizer) + }) + + if repeated > 0: + violation_amount = float(repeated) + rhs_val = float(n + 1) # expected number of unique vertices + lhs_val = float(len(tour)) # actual tour length + normalizer = max(abs(rhs_val), eps) + violated_constraint_set.add(3) + violations.append( + f"Constraint 3 (subtour elimination): {label} tour has {repeated} " + f"repeated vertex visit(s); tour length {len(tour)} but only " + f"{len(tour_set)} unique vertices" + ) + violation_magnitudes.append({ + "constraint": 3, + "lhs": float(lhs_val), + "rhs": float(rhs_val), + "raw_excess": float(violation_amount), + "normalizer": float(normalizer), + "ratio": float(violation_amount / normalizer) + }) + + if extra: + violated_constraint_set.add(3) + violations.append( + f"Constraint 3 (subtour elimination): {label} tour visits invalid " + f"vertices {sorted(extra)} not in V^T" + ) + violation_magnitudes.append({ + "constraint": 3, + "lhs": float(len(extra)), + "rhs": 0.0, + "raw_excess": float(len(extra)), + "normalizer": eps, + "ratio": float(len(extra) / eps) + }) + + # Check single-cycle property (no disconnected subtours) + if not missing and not extra and repeated == 0: + # Build adjacency from tour + adj = {} + for idx in range(len(tour)): + adj[tour[idx]] = tour[(idx + 1) % len(tour)] + # Follow from depot + visited = set() + current = 0 + while current not in visited: + visited.add(current) + current = adj.get(current, -1) + if len(visited) < len(V): + # Multiple cycles detected + num_subtours = 0 + remaining = set(V) - visited + while remaining: + start = next(iter(remaining)) + cycle = set() + cur = start + while cur not in cycle: + cycle.add(cur) + cur = adj.get(cur, -1) + remaining -= cycle + num_subtours += 1 + + violation_amount = float(num_subtours) + rhs_val = 0.0 + normalizer = eps + violated_constraint_set.add(3) + violations.append( + f"Constraint 3 (subtour elimination): {label} tour has " + f"{num_subtours + 1} disconnected cycle(s) instead of 1" + ) + violation_magnitudes.append({ + "constraint": 3, + "lhs": float(num_subtours + 1), + "rhs": 1.0, + "raw_excess": float(num_subtours), + "normalizer": max(1.0, eps), + "ratio": float(num_subtours / max(1.0, eps)) + }) + + # ========================================================================= + # Constraint 4 (Eq 5): Binary domain — x^T_{ij} in {0,1} + # Also: x^T_{ii} = 0 (variable fixing from comment 4) + # ========================================================================= + for label, x_vals in [("pickup", x_P), ("delivery", x_D)]: + for (i, j), val in x_vals.items(): + if val not in (0, 1): + violation_amount = min(abs(val - 0), abs(val - 1)) + if violation_amount > tol: + violated_constraint_set.add(4) + violations.append( + f"Constraint 4 (binary domain): x^{label[0].upper()}" + f"_{{{i},{j}}} = {val}, not binary" + ) + violation_magnitudes.append({ + "constraint": 4, + "lhs": float(val), + "rhs": 1.0, + "raw_excess": float(violation_amount), + "normalizer": max(1.0, eps), + "ratio": float(violation_amount / max(1.0, eps)) + }) + if i == j and val > tol: + violated_constraint_set.add(4) + violations.append( + f"Constraint 4 (binary domain): Self-loop x^{label[0].upper()}" + f"_{{{i},{i}}} = {val}, must be 0" + ) + violation_magnitudes.append({ + "constraint": 4, + "lhs": float(val), + "rhs": 0.0, + "raw_excess": float(val), + "normalizer": eps, + "ratio": float(val / eps) + }) + + # ========================================================================= + # Constraint 5 (Eq 6): Loading feasibility — LIFO constraint + # For a given pickup tour order and delivery tour order, there must exist + # a feasible assignment of items to s stacks (each with capacity l) such + # that LIFO is respected: if item i is picked up before item j and both + # are in the same stack, then j must be delivered before i. + # ========================================================================= + # Extract customer ordering from tours (exclude depot 0) + p_customers = [v for v in pickup_tour if v != 0] + d_customers = [v for v in delivery_tour if v != 0] + + # Check basic: both should contain exactly the customer set {1..n} + p_set = set(p_customers) + d_set = set(d_customers) + expected_customers = set(range(1, n + 1)) + + if p_set != expected_customers or d_set != expected_customers: + # Already caught by constraints 1-3, but note for loading + pass + else: + # Build position maps + p_pos = {v: i for i, v in enumerate(p_customers)} + d_pos = {v: i for i, v in enumerate(d_customers)} + + # Try to assign items to stacks respecting LIFO + # Use backtracking to check feasibility + items_in_pickup_order = list(p_customers) # items in pickup order + + feasible = _check_loading_feasibility(items_in_pickup_order, p_pos, d_pos, s, l_cap) + + if not feasible: + # Compute violation: the loading is infeasible + # For the path constraint (6): LHS = |A(Q)| + |A(F)|, RHS = |A(Q)| + |A(F)| - 1 + # With Q = full pickup path, F = full delivery path + q = len(p_customers) # arcs in pickup path + f = len(d_customers) # arcs in delivery path + lhs_val = float(q + f) # all arcs are used (integer solution) + rhs_val = float(q + f - 1) + violation_amount = lhs_val - rhs_val # = 1.0 + normalizer = max(abs(rhs_val), eps) + violated_constraint_set.add(5) + violations.append( + f"Constraint 5 (loading feasibility): No feasible LIFO loading exists " + f"for the given pickup order {pickup_tour} and delivery order " + f"{delivery_tour} with {s} stack(s) of capacity {l_cap}" + ) + violation_magnitudes.append({ + "constraint": 5, + "lhs": float(lhs_val), + "rhs": float(rhs_val), + "raw_excess": float(violation_amount), + "normalizer": float(normalizer), + "ratio": float(violation_amount / normalizer) + }) + + # ========================================================================= + # Constraint 6 (Eq 1): Objective consistency — reported objective_value must + # equal recomputed total routing cost (sum of pickup arc costs + sum of + # delivery arc costs). All variables that determine the obj (pickup_tour, + # delivery_tour) are in the solution, so a full recompute is exact. + # Tier C defense against LLM solutions that fabricate objective_value. + # ========================================================================= + reported_obj_raw = solution.get("objective_value") + pickup_dm = instance.get("pickup_region", {}).get("distance_matrix") + delivery_dm = instance.get("delivery_region", {}).get("distance_matrix") + if reported_obj_raw is not None and pickup_dm is not None and delivery_dm is not None: + try: + reported_obj = float(reported_obj_raw) + except (TypeError, ValueError): + reported_obj = None + if reported_obj is not None: + try: + pickup_cost = sum( + float(pickup_dm[pickup_tour[i]][pickup_tour[(i + 1) % len(pickup_tour)]]) + for i in range(len(pickup_tour)) + ) + delivery_cost = sum( + float(delivery_dm[delivery_tour[i]][delivery_tour[(i + 1) % len(delivery_tour)]]) + for i in range(len(delivery_tour)) + ) + true_obj = pickup_cost + delivery_cost + except (IndexError, TypeError, ValueError): + true_obj = None + + if true_obj is not None: + abs_diff = abs(reported_obj - true_obj) + # 0.1% relative tolerance with 1e-3 absolute floor. + obj_tol = max(1e-3, 1e-3 * abs(true_obj)) + if abs_diff > obj_tol: + normalizer = max(abs(true_obj), eps) + violated_constraint_set.add(6) + violations.append( + f"Constraint 6 (objective consistency): reported objective_value=" + f"{reported_obj} differs from recomputed pickup_cost+delivery_cost=" + f"{true_obj} (|diff|={abs_diff:.6g}, tol={obj_tol:.6g})" + ) + violation_magnitudes.append({ + "constraint": 6, + "lhs": float(reported_obj), + "rhs": float(true_obj), + "raw_excess": float(abs_diff), + "normalizer": float(normalizer), + "ratio": float(abs_diff / normalizer) + }) + + # ========================================================================= + # Build result + # ========================================================================= + violated_list = sorted(violated_constraint_set) + feasible = len(violated_list) == 0 + + result = { + "feasible": feasible, + "violated_constraints": violated_list, + "violations": violations, + "violation_magnitudes": violation_magnitudes + } + + return result + + +def _check_loading_feasibility(items_in_pickup_order, p_pos, d_pos, s, l_cap): + """ + Check if items can be assigned to s stacks (each capacity l_cap) respecting LIFO. + + LIFO rule: if item i is picked up before item j and both are in the same stack, + then item j must be delivered before item i (j is on top). + + Uses backtracking. + """ + stacks = [[] for _ in range(s)] + + def can_place(item, stack_idx): + stack = stacks[stack_idx] + if len(stack) >= l_cap: + return False + # Check LIFO: for each item w already in the stack, + # if w was picked up before item (p_pos[w] < p_pos[item]), + # then item must be delivered before w (d_pos[item] < d_pos[w]) + # if item was picked up before w (shouldn't happen since we process in pickup order), + # then w must be delivered before item + for w in stack: + if p_pos[w] < p_pos[item]: + # w picked before item => item on top => item delivered before w + if d_pos[item] > d_pos[w]: + return False + elif p_pos[item] < p_pos[w]: + # item picked before w => w on top => w delivered before item + if d_pos[w] > d_pos[item]: + return False + return True + + def backtrack(idx): + if idx == len(items_in_pickup_order): + return True + item = items_in_pickup_order[idx] + for k in range(s): + if can_place(item, k): + stacks[k].append(item) + if backtrack(idx + 1): + return True + stacks[k].pop() + # Pruning: if stack k is empty, no point trying further empty stacks + if len(stacks[k]) == 0: + break + return False + + return backtrack(0) + + +def main(): + parser = argparse.ArgumentParser( + description="Feasibility checker for DTSPMS solutions" + ) + parser.add_argument("--instance_path", type=str, required=True, + help="Path to the JSON file containing the data instance") + parser.add_argument("--solution_path", type=str, required=True, + help="Path to the JSON file containing the candidate solution") + parser.add_argument("--result_path", type=str, required=True, + help="Path to write the JSON file containing the feasibility result") + args = parser.parse_args() + + instance = load_json(args.instance_path) + solution = load_json(args.solution_path) + _frontieror_contract_result = _frontieror_validate_solution_contract(solution) + if _frontieror_contract_result is not None: + with open(args.result_path, "w") as _frontieror_result_handle: + json.dump( + _frontieror_contract_result, + _frontieror_result_handle, + indent=2, + ) + return + + result = check_feasibility(instance, solution) + + with open(args.result_path, 'w') as f: + json.dump(result, f, indent=2) + + if result["feasible"]: + print(f"FEASIBLE: Solution satisfies all constraints.") + else: + print(f"INFEASIBLE: Violated constraints: {result['violated_constraints']}") + for v in result["violations"]: + print(f" - {v}") + + +if __name__ == "__main__": + try: + main() + except Exception as _frontieror_unexpected_error: + if not _frontieror_write_unexpected_rejection( + _frontieror_unexpected_error + ): + raise diff --git a/alba2013/gurobi_code.py b/alba2013/gurobi_code.py new file mode 100644 index 0000000000000000000000000000000000000000..ec5f9093f5a4a20a077adc9daf135fce3ec6fdab --- /dev/null +++ b/alba2013/gurobi_code.py @@ -0,0 +1,988 @@ +#!/usr/bin/env python3 +""" +Gurobi implementation of the DTSPMS (Double Traveling Salesman Problem with Multiple Stacks). + +Based on: Alba Martinez et al. (2013), "A Branch-and-Cut Algorithm for the Double Traveling +Salesman Problem with Multiple Stacks", INFORMS Journal on Computing 25(1):41-55. + +This implements the ILP formulation (1)-(5) from the paper, with dynamic separation of: + - Subtour elimination constraints (4) via callback + - Infeasible path / tournament constraints (7) via callback + - Lifted tournament constraints (8) via callback (heuristic) + - Position-based constraints (9) via callback + - Lifted depot-endpoint constraints (10) via callback (heuristic) + +The loading feasibility check (PDTC) follows Section 3 of the paper exactly. +""" + +import argparse +import json +import math +import time +import itertools +from collections import defaultdict, deque + +import gurobipy as gp +from gurobipy import GRB +import os as _os, sys as _sys +# Walk up from this file's directory to find repo root (containing scripts/). +_repo = _os.path.dirname(_os.path.abspath(__file__)) +while _repo != _os.path.dirname(_repo) and not _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _repo = _os.path.dirname(_repo) +if _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _sys.path.insert(0, _repo) +try: + from scripts.utils.gurobi_log_helper import install_gurobi_logger +except ImportError: + def install_gurobi_logger(log_path): # no-op fallback when scripts/ unavailable + pass + + +# ============================================================================= +# DATA LOADING +# ============================================================================= + +def load_instance(path): + """Load a DTSPMS instance from JSON.""" + with open(path, 'r') as f: + data = json.load(f) + return data + + +def build_distance_matrices(data): + """Extract distance matrices for pickup and delivery regions.""" + n = data['parameters']['num_requests_n'] + s = data['parameters']['num_stacks_s'] + l = data['parameters']['stack_height_l'] + + c_P = [] + for row in data['pickup_region']['distance_matrix']: + c_P.append(list(row)) + + c_D = [] + for row in data['delivery_region']['distance_matrix']: + c_D.append(list(row)) + + return n, s, l, c_P, c_D + + +# ============================================================================= +# LOADING FEASIBILITY CHECK (PDTC) - Section 3 of the paper +# ============================================================================= + +def check_loading_feasibility(pickup_path, delivery_path, s, l): + """ + Check if a pair of (partial) paths admits a feasible loading plan. + + pickup_path: ordered list of customer vertices visited in pickup tour (no depot) + delivery_path: ordered list of customer vertices visited in delivery tour (no depot) + s: number of stacks + l: max height of each stack + + Returns: (feasible, cliques) + feasible: True if loading is feasible + cliques: list of cliques of size > s found (for lifted constraints) + """ + # Find common customers in both paths + I = [] + pickup_set = set(pickup_path) + delivery_set = set(delivery_path) + I = list(pickup_set & delivery_set) + + if len(I) == 0: + return True, [] + + # Step 1: Trivial check - if |I| <= s, feasible + if len(I) <= s: + return True, [] + + # Build position maps + pickup_pos = {} + for idx, v in enumerate(pickup_path): + if v not in pickup_pos: + pickup_pos[v] = idx + + delivery_pos = {} + for idx, v in enumerate(delivery_path): + if v not in delivery_pos: + delivery_pos[v] = idx + + # Step 2: Build precedence graph + # Arc (i, j) exists if i precedes j in BOTH pickup and delivery paths + # This means i and j CANNOT be in the same stack (LIFO violation) + precedence = defaultdict(set) + for i in I: + for j in I: + if i != j: + if pickup_pos[i] < pickup_pos[j] and delivery_pos[i] < delivery_pos[j]: + precedence[i].add(j) + + # Step 3: Lower bound via longest path (CPM) + # The precedence graph is a DAG; find longest path + # Topological order by pickup position + sorted_items = sorted(I, key=lambda v: pickup_pos[v]) + + # Compute longest path length and track predecessors for clique extraction + dist = {v: 0 for v in I} + pred = {v: None for v in I} + + for v in sorted_items: + for w in I: + if w in precedence[v]: + if dist[w] < dist[v] + 1: + dist[w] = dist[v] + 1 + pred[w] = v + + max_clique_size = max(dist.values()) + 1 if dist else 1 + + # Extract all cliques (longest paths) of maximum length + cliques = [] + max_dist = max(dist.values()) if dist else 0 + + # Find all endpoints of longest paths + for end_v in I: + if dist[end_v] == max_dist: + # Trace back the clique + clique = [] + v = end_v + while v is not None: + clique.append(v) + v = pred[v] + clique.reverse() + if clique not in cliques: + cliques.append(clique) + + if max_clique_size > s: + return False, cliques + + # Step 4: Greedy assignment using CPM output + # Compute T_min and T_max for each vertex + # T_min(i) = length of longest path ending at i (from any source) + 1 (1-indexed stack) + # T_max(i) = s - (length of longest path starting from i to any sink) + + # Forward pass: T_min + t_min = {} + for v in sorted_items: + t_min[v] = dist[v] + 1 # 1-indexed + + # Backward pass: longest path from v to any sink + # Reverse topological order + rev_sorted = list(reversed(sorted_items)) + dist_from = {v: 0 for v in I} + for v in rev_sorted: + for w in I: + if w in precedence[v]: + if dist_from[v] < dist_from[w] + 1: + dist_from[v] = dist_from[w] + 1 + + t_max = {} + for v in I: + t_max[v] = s - dist_from[v] + + # Assign items on longest path first (T_min == T_max) + stacks = [[] for _ in range(s)] # stacks[k] is list of items in stack k (bottom to top) + + fixed_items = set() + for v in I: + if t_min[v] == t_max[v]: + stack_idx = t_min[v] - 1 # 0-indexed + if stack_idx < 0 or stack_idx >= s: + # Should not happen if clique size <= s + break + stacks[stack_idx].append(v) + if len(stacks[stack_idx]) > l: + # Greedy fails, go to enumeration + return _enumerate_loading(I, precedence, pickup_pos, delivery_pos, s, l), cliques + fixed_items.add(v) + + # Check LIFO for fixed items + for k in range(s): + for i_idx in range(len(stacks[k])): + for j_idx in range(i_idx + 1, len(stacks[k])): + vi = stacks[k][i_idx] + vj = stacks[k][j_idx] + # vi loaded before vj (vi is lower in stack) + # For LIFO: vj must be delivered before vi + if pickup_pos[vi] < pickup_pos[vj]: + if delivery_pos[vi] < delivery_pos[vj]: + # LIFO violation + return _enumerate_loading(I, precedence, pickup_pos, delivery_pos, s, l), cliques + + # Remaining items sorted by T_min, breaking ties by vertex index + remaining = [v for v in I if v not in fixed_items] + remaining.sort(key=lambda v: (t_min[v], v)) + + for v in remaining: + assigned = False + for k in range(s): + # Check if v can go into stack k + if len(stacks[k]) >= l: + continue + # Check LIFO: v is placed on top of stack k + # For each item w already in stack k: + # if w was picked up before v, then v must be delivered before w + # if v was picked up before w, then w must be delivered before v + valid = True + for w in stacks[k]: + if pickup_pos[w] < pickup_pos[v]: + # w loaded before v, v on top -> v must be delivered before w + if delivery_pos[v] > delivery_pos[w]: + valid = False + break + elif pickup_pos[v] < pickup_pos[w]: + # v loaded before w, but w is already in stack -> w was loaded after v + # w is on top relative to v, so w must be delivered before v + if delivery_pos[w] > delivery_pos[v]: + valid = False + break + if valid: + stacks[k].append(v) + assigned = True + break + if not assigned: + return _enumerate_loading(I, precedence, pickup_pos, delivery_pos, s, l), cliques + + return True, cliques + + +def _enumerate_loading(I, precedence, pickup_pos, delivery_pos, s, l): + """ + Step 5: Complete enumeration of loading assignments. + Returns True if a feasible loading exists, False otherwise. + """ + # Sort items same as greedy + # Compute t_min for sorting + sorted_items_by_pickup = sorted(I, key=lambda v: pickup_pos[v]) + dist = {v: 0 for v in I} + for v in sorted_items_by_pickup: + for w in I: + if w in precedence[v]: + if dist[w] < dist[v] + 1: + dist[w] = dist[v] + 1 + + t_min = {v: dist[v] + 1 for v in I} + items_order = sorted(I, key=lambda v: (t_min[v], v)) + + # DFS enumeration + stacks = [[] for _ in range(s)] + + def backtrack(idx): + if idx == len(items_order): + return True + v = items_order[idx] + for k in range(s): + if len(stacks[k]) >= l: + continue + # Check LIFO + valid = True + for w in stacks[k]: + if pickup_pos[w] < pickup_pos[v]: + if delivery_pos[v] > delivery_pos[w]: + valid = False + break + elif pickup_pos[v] < pickup_pos[w]: + if delivery_pos[w] > delivery_pos[v]: + valid = False + break + if valid: + stacks[k].append(v) + if backtrack(idx + 1): + return True + stacks[k].pop() + return False + + return backtrack(0) + + +# ============================================================================= +# SUBTOUR SEPARATION +# ============================================================================= + +def find_subtours(n, x_vals): + """ + Find subtours in the solution defined by x_vals. + x_vals: dict mapping (i,j) -> value + Returns list of subtours (each a set of vertices), excluding the full tour. + n+1 vertices: 0..n + """ + # Build adjacency from integer solution + adj = defaultdict(list) + for (i, j), val in x_vals.items(): + if val > 0.5: + adj[i].append(j) + + visited = set() + subtours = [] + all_vertices = set(range(n + 1)) + + for start in range(n + 1): + if start in visited: + continue + # Trace the cycle/path from start + tour = set() + current = start + while current not in tour and current not in visited: + tour.add(current) + nexts = adj.get(current, []) + if nexts: + current = nexts[0] + else: + break + visited |= tour + if len(tour) < n + 1 and len(tour) >= 2: + subtours.append(tour) + + return subtours + + +def find_fractional_subtours(n, x_vals, tol=1e-4): + """ + Separate subtour elimination constraints from fractional solution + using max-flow / min-cut approach. For simplicity, we use a direct + approach checking connected components in the support graph. + + Returns list of violated subtour sets S where sum_{i,j in S} x_{ij} > |S| - 1. + """ + # Build capacity graph + capacity = defaultdict(float) + for (i, j), val in x_vals.items(): + if val > tol: + capacity[(i, j)] += val + + # For each vertex i != 0, compute min-cut between 0 and i + # If min-cut < 1 (approx 2 due to in+out degree), there's a violated subtour + violated = [] + vertices = set(range(n + 1)) + + for t in range(1, n + 1): + # BFS to find min-cut using residual graph + cut_val, S = min_cut_bfs(0, t, n, capacity) + if cut_val < 1.0 - tol and len(S) >= 2 and len(S) <= n: + # Check if this S actually gives a violated constraint + flow_in_S = sum(capacity.get((i, j), 0.0) for i in S for j in S if i != j) + if flow_in_S > len(S) - 1 + tol: + violated.append(S) + + return violated + + +def min_cut_bfs(source, sink, n, capacity): + """ + Compute min s-t cut using max-flow (Edmonds-Karp / BFS augmenting paths). + Returns (cut_value, S) where S is the source side of the min cut. + """ + # Build residual graph + residual = defaultdict(float) + nodes = set(range(n + 1)) + + for (i, j), cap in capacity.items(): + residual[(i, j)] += cap + + total_flow = 0.0 + + while True: + # BFS to find augmenting path + parent = {source: None} + visited = {source} + queue = deque([source]) + found = False + + while queue and not found: + u = queue.popleft() + for v in nodes: + if v not in visited and residual.get((u, v), 0.0) > 1e-8: + parent[v] = u + visited.add(v) + if v == sink: + found = True + break + queue.append(v) + + if not found: + break + + # Find bottleneck + path_flow = float('inf') + v = sink + while parent[v] is not None: + u = parent[v] + path_flow = min(path_flow, residual[(u, v)]) + v = u + + # Update residual + v = sink + while parent[v] is not None: + u = parent[v] + residual[(u, v)] -= path_flow + residual[(v, u)] += path_flow + v = u + + total_flow += path_flow + + # Find S (reachable from source in residual graph) + S = set() + queue = deque([source]) + S.add(source) + while queue: + u = queue.popleft() + for v in nodes: + if v not in S and residual.get((u, v), 0.0) > 1e-8: + S.add(v) + queue.append(v) + + return total_flow, S + + +# ============================================================================= +# PATH ENUMERATION FOR INFEASIBLE PATH SEPARATION +# ============================================================================= + +def enumerate_fractional_paths(n, x_vals, max_path_len=None, tol=1e-4): + """ + Enumerate fractional paths in a graph defined by x_vals. + A path starts from any vertex i=1..n and extends greedily. + + Returns list of (path, arc_sum, tournament_sum) tuples, sorted by: + - nondecreasing path length + - nonincreasing tournament sum + """ + if max_path_len is None: + max_path_len = n + + paths = [] + + for start in range(1, n + 1): + # BFS/DFS to find paths + # We use a simple greedy extension + _extend_path(start, [], 0.0, 0.0, set(), x_vals, n, max_path_len, paths, tol) + + # Sort: nondecreasing length, then nonincreasing tournament sum + paths.sort(key=lambda p: (len(p[0]) - 1, -p[2])) + return paths + + +def _extend_path(current, path_so_far, arc_sum, tourn_sum, visited, x_vals, n, max_len, results, tol): + """Recursively extend a path and collect valid paths.""" + new_path = path_so_far + [current] + new_visited = visited | {current} + path_len = len(new_path) - 1 # number of arcs + + if path_len >= 1: + # Compute tournament sum: sum of x_{p_j, p_h} for j < h (forward arcs) + t_sum = 0.0 + for j in range(len(new_path)): + for h in range(j + 1, len(new_path)): + val = x_vals.get((new_path[j], new_path[h]), 0.0) + t_sum += val + + # Keep path if sum > length - 1 (could contribute to violation) + if t_sum > path_len - 1 + tol: + results.append((list(new_path), arc_sum, t_sum)) + + if path_len >= max_len: + return + + # Extend to next vertex + for next_v in range(1, n + 1): + if next_v not in new_visited: + edge_val = x_vals.get((current, next_v), 0.0) + if edge_val > tol: + new_arc_sum = arc_sum + edge_val + _extend_path(next_v, new_path, new_arc_sum, 0.0, new_visited, + x_vals, n, max_len, results, tol) + + +def enumerate_depot_paths(n, x_vals, from_depot=True, tol=1e-4): + """ + Enumerate paths starting from (or ending at) the depot. + For position-based constraints (9). + """ + max_path_len = n + paths = [] + + if from_depot: + # Paths: 0 -> v1 -> v2 -> ... -> vk + for next_v in range(1, n + 1): + edge_val = x_vals.get((0, next_v), 0.0) + if edge_val > tol: + _extend_depot_path(next_v, [0, next_v], edge_val, {0, next_v}, + x_vals, n, max_path_len, paths, tol) + else: + # Paths ending at depot: v1 -> v2 -> ... -> vk -> 0 + # We enumerate paths ending at 0 + for prev_v in range(1, n + 1): + edge_val = x_vals.get((prev_v, 0), 0.0) + if edge_val > tol: + # Extend backward from prev_v + _extend_depot_path_backward(prev_v, [prev_v, 0], edge_val, {prev_v, 0}, + x_vals, n, max_path_len, paths, tol) + + return paths + + +def _extend_depot_path(current, path_so_far, arc_sum, visited, x_vals, n, max_len, results, tol): + """Extend a depot path forward.""" + path_len = len(path_so_far) - 1 + if path_len >= 1: + # Compute tournament sum + t_sum = 0.0 + for j in range(len(path_so_far)): + for h in range(j + 1, len(path_so_far)): + t_sum += x_vals.get((path_so_far[j], path_so_far[h]), 0.0) + results.append((list(path_so_far), arc_sum, t_sum)) + + if path_len >= max_len: + return + + for next_v in range(1, n + 1): + if next_v not in visited: + edge_val = x_vals.get((current, next_v), 0.0) + if edge_val > tol: + _extend_depot_path(next_v, path_so_far + [next_v], arc_sum + edge_val, + visited | {next_v}, x_vals, n, max_len, results, tol) + + +def _extend_depot_path_backward(current, path_so_far, arc_sum, visited, x_vals, n, max_len, results, tol): + """Extend a path backward (for paths ending at depot).""" + path_len = len(path_so_far) - 1 + if path_len >= 1: + t_sum = 0.0 + for j in range(len(path_so_far)): + for h in range(j + 1, len(path_so_far)): + t_sum += x_vals.get((path_so_far[j], path_so_far[h]), 0.0) + results.append((list(path_so_far), arc_sum, t_sum)) + + if path_len >= max_len: + return + + for prev_v in range(1, n + 1): + if prev_v not in visited: + edge_val = x_vals.get((prev_v, current), 0.0) + if edge_val > tol: + _extend_depot_path_backward(prev_v, [prev_v] + path_so_far, arc_sum + edge_val, + visited | {prev_v}, x_vals, n, max_len, results, tol) + + +# ============================================================================= +# CUT GENERATION HELPERS +# ============================================================================= + +def compute_tournament_lhs(path, x_vars_dict): + """ + Compute the LHS of the tournament constraint (7) for a given path. + Sum of x_{p_j, p_h} for all j < h in the path. + """ + total = 0.0 + for j in range(len(path)): + for h in range(j + 1, len(path)): + var = x_vars_dict.get((path[j], path[h])) + if var is not None: + total += var + return total + + +def add_tournament_cut(model, pickup_path, delivery_path, x_P, x_D, where): + """Add tournament constraint (7) as a lazy constraint.""" + rhs = (len(pickup_path) - 1) + (len(delivery_path) - 1) - 1 + + lhs_expr = 0 + for j in range(len(pickup_path)): + for h in range(j + 1, len(pickup_path)): + key = (pickup_path[j], pickup_path[h]) + if key in x_P: + lhs_expr += x_P[key] + + for j in range(len(delivery_path)): + for h in range(j + 1, len(delivery_path)): + key = (delivery_path[j], delivery_path[h]) + if key in x_D: + lhs_expr += x_D[key] + + model.cbLazy(lhs_expr <= rhs) + + +def add_lifted_tournament_cut(model, pickup_path, delivery_path, clique, x_P, x_D, where): + """Add lifted tournament constraint (8) as a lazy constraint.""" + rhs = (len(pickup_path) - 1) + (len(delivery_path) - 1) - 1 + + lhs_expr = 0 + + # Forward arcs in pickup path + for j in range(len(pickup_path)): + for h in range(j + 1, len(pickup_path)): + key = (pickup_path[j], pickup_path[h]) + if key in x_P: + lhs_expr += x_P[key] + + # Forward arcs in delivery path + for j in range(len(delivery_path)): + for h in range(j + 1, len(delivery_path)): + key = (delivery_path[j], delivery_path[h]) + if key in x_D: + lhs_expr += x_D[key] + + # Reverse arcs within segments between consecutive clique vertices (pickup) + clique_set = set(clique) + pickup_clique_positions = [i for i, v in enumerate(pickup_path) if v in clique_set] + for ci in range(len(pickup_clique_positions)): + start_pos = pickup_clique_positions[ci] + end_pos = pickup_clique_positions[ci + 1] if ci + 1 < len(pickup_clique_positions) else len(pickup_path) + segment = pickup_path[start_pos:end_pos] + # Add reverse arcs within this segment (j > h, i.e., backward arcs) + for j in range(len(segment)): + for h in range(j): + key = (segment[j], segment[h]) + if key in x_P: + lhs_expr += x_P[key] + + # Reverse arcs within segments between consecutive clique vertices (delivery) + delivery_clique_positions = [i for i, v in enumerate(delivery_path) if v in clique_set] + for ci in range(len(delivery_clique_positions)): + start_pos = delivery_clique_positions[ci] + end_pos = delivery_clique_positions[ci + 1] if ci + 1 < len(delivery_clique_positions) else len(delivery_path) + segment = delivery_path[start_pos:end_pos] + for j in range(len(segment)): + for h in range(j): + key = (segment[j], segment[h]) + if key in x_D: + lhs_expr += x_D[key] + + model.cbLazy(lhs_expr <= rhs) + + +# ============================================================================= +# GUROBI CALLBACK +# ============================================================================= + +def dtspms_callback(model, where): + """Gurobi callback for lazy constraint generation.""" + if where == GRB.Callback.MIPSOL: + # Integer solution found - check for subtours and loading feasibility + n = model._n + s = model._s + l = model._l + x_P = model._x_P + x_D = model._x_D + + # Get solution values + x_P_vals = {} + x_D_vals = {} + for (i, j), var in x_P.items(): + x_P_vals[(i, j)] = model.cbGetSolution(var) + for (i, j), var in x_D.items(): + x_D_vals[(i, j)] = model.cbGetSolution(var) + + # Check subtours in pickup + subtours_P = find_subtours(n, x_P_vals) + for S in subtours_P: + expr = gp.quicksum(x_P[(i, j)] for i in S for j in S if i != j and (i, j) in x_P) + model.cbLazy(expr <= len(S) - 1) + + # Check subtours in delivery + subtours_D = find_subtours(n, x_D_vals) + for S in subtours_D: + expr = gp.quicksum(x_D[(i, j)] for i in S for j in S if i != j and (i, j) in x_D) + model.cbLazy(expr <= len(S) - 1) + + if subtours_P or subtours_D: + return + + # No subtours - extract the tours + pickup_tour = extract_tour(n, x_P_vals) + delivery_tour = extract_tour(n, x_D_vals) + + if pickup_tour is None or delivery_tour is None: + return + + # Check loading feasibility + pickup_customers = pickup_tour[1:] # exclude depot + delivery_customers = delivery_tour[1:] + + feasible, cliques = check_loading_feasibility( + pickup_customers, delivery_customers, s, l + ) + + if not feasible: + # Add tournament constraint (7) + add_tournament_cut(model, pickup_customers, delivery_customers, x_P, x_D, where) + + # Also add reverse cut for symmetric costs + add_tournament_cut(model, list(reversed(pickup_customers)), + list(reversed(delivery_customers)), x_P, x_D, where) + + # Add lifted tournament constraints (8) for cliques > s + for clique in cliques: + if len(clique) > s: + add_lifted_tournament_cut(model, pickup_customers, delivery_customers, + clique, x_P, x_D, where) + + elif where == GRB.Callback.MIPNODE: + # Fractional solution at a node - separate subtours and infeasible paths + if model.cbGet(GRB.Callback.MIPNODE_STATUS) != GRB.OPTIMAL: + return + + n = model._n + s = model._s + l = model._l + x_P = model._x_P + x_D = model._x_D + tol = 1e-4 + + # Get relaxation values + x_P_vals = {} + x_D_vals = {} + for (i, j), var in x_P.items(): + x_P_vals[(i, j)] = model.cbGetNodeRel(var) + for (i, j), var in x_D.items(): + x_D_vals[(i, j)] = model.cbGetNodeRel(var) + + # Separate subtour elimination constraints + cut_added = False + + violated_P = find_fractional_subtours(n, x_P_vals, tol) + for S in violated_P: + expr = gp.quicksum(x_P[(i, j)] for i in S for j in S if i != j and (i, j) in x_P) + model.cbCut(expr <= len(S) - 1) + cut_added = True + + violated_D = find_fractional_subtours(n, x_D_vals, tol) + for S in violated_D: + expr = gp.quicksum(x_D[(i, j)] for i in S for j in S if i != j and (i, j) in x_D) + model.cbCut(expr <= len(S) - 1) + cut_added = True + + if cut_added: + return + + # Separate tournament constraints (7) on fractional paths + # Limit path enumeration to avoid excessive computation + max_path_len = min(n, 6) # **INFERRED ASSUMPTION**: limit path length for tractability + + pickup_paths = enumerate_fractional_paths(n, x_P_vals, max_path_len, tol) + delivery_paths = enumerate_fractional_paths(n, x_D_vals, max_path_len, tol) + + # Check pairs for violations + for p_path, p_arc_sum, p_tourn_sum in pickup_paths: + for d_path, d_arc_sum, d_tourn_sum in delivery_paths: + rhs = (len(p_path) - 1) + (len(d_path) - 1) - 1 + total_sum = p_tourn_sum + d_tourn_sum + + if total_sum > rhs + tol: + # Check loading feasibility + feasible, cliques = check_loading_feasibility( + p_path, d_path, s, l + ) + if not feasible: + # Add tournament cut + lhs_expr = 0 + for j in range(len(p_path)): + for h in range(j + 1, len(p_path)): + key = (p_path[j], p_path[h]) + if key in x_P: + lhs_expr += x_P[key] + for j in range(len(d_path)): + for h in range(j + 1, len(d_path)): + key = (d_path[j], d_path[h]) + if key in x_D: + lhs_expr += x_D[key] + model.cbCut(lhs_expr <= rhs) + + # Reverse cut + rev_lhs = 0 + rev_p = list(reversed(p_path)) + rev_d = list(reversed(d_path)) + for j in range(len(rev_p)): + for h in range(j + 1, len(rev_p)): + key = (rev_p[j], rev_p[h]) + if key in x_P: + rev_lhs += x_P[key] + for j in range(len(rev_d)): + for h in range(j + 1, len(rev_d)): + key = (rev_d[j], rev_d[h]) + if key in x_D: + rev_lhs += x_D[key] + model.cbCut(rev_lhs <= rhs) + return # Stop after first violated pair found + + +def extract_tour(n, x_vals): + """Extract a Hamiltonian tour from integer solution values.""" + adj = {} + for (i, j), val in x_vals.items(): + if val > 0.5: + adj[i] = j + + tour = [0] + current = 0 + for _ in range(n): + next_v = adj.get(current) + if next_v is None: + return None + tour.append(next_v) + current = next_v + + if adj.get(current) != 0: + return None + + return tour + + +# ============================================================================= +# MAIN MODEL +# ============================================================================= + +def solve_dtspms(instance_path, solution_path, time_limit): + """Build and solve the DTSPMS model using Gurobi.""" + data = load_instance(instance_path) + n, s, l, c_P, c_D = build_distance_matrices(data) + + print(f"DTSPMS Instance: n={n}, s={s}, l={l}") + print(f"Time limit: {time_limit} seconds") + + model = gp.Model("DTSPMS") + model.setParam("Threads", 1) + model.setParam("TimeLimit", time_limit) + model.setParam("LazyConstraints", 1) + model.setParam("OutputFlag", 1) + + # Create variables x^P_{ij} and x^D_{ij} + x_P = {} + x_D = {} + V = list(range(n + 1)) # 0..n + + for i in V: + for j in V: + if i != j: # x^T_{ii} = 0 (variable fixing, comment 4) + x_P[(i, j)] = model.addVar(vtype=GRB.BINARY, name=f"xP_{i}_{j}", + obj=c_P[i][j]) + x_D[(i, j)] = model.addVar(vtype=GRB.BINARY, name=f"xD_{i}_{j}", + obj=c_D[i][j]) + + model.update() + + # Objective: minimize total routing cost (1) + model.setAttr("ModelSense", GRB.MINIMIZE) + + # Constraints (2): each vertex has exactly one successor (pickup) + for i in V: + model.addConstr( + gp.quicksum(x_P[(i, j)] for j in V if j != i) == 1, + name=f"outP_{i}" + ) + + # Constraints (3): each vertex has exactly one predecessor (pickup) + for j in V: + model.addConstr( + gp.quicksum(x_P[(i, j)] for i in V if i != j) == 1, + name=f"inP_{j}" + ) + + # Constraints (2): each vertex has exactly one successor (delivery) + for i in V: + model.addConstr( + gp.quicksum(x_D[(i, j)] for j in V if j != i) == 1, + name=f"outD_{i}" + ) + + # Constraints (3): each vertex has exactly one predecessor (delivery) + for j in V: + model.addConstr( + gp.quicksum(x_D[(i, j)] for i in V if i != j) == 1, + name=f"inD_{j}" + ) + + # 2-vertex subtour elimination: x^T_{ij} + x^T_{ji} <= 1 (Section 5.1) + for i in V: + for j in V: + if i < j: + model.addConstr(x_P[(i, j)] + x_P[(j, i)] <= 1, name=f"sec2P_{i}_{j}") + model.addConstr(x_D[(i, j)] + x_D[(j, i)] <= 1, name=f"sec2D_{i}_{j}") + + # Simple position-based constraints (Section 5.1) + # Added when n >= (s-1)*l + 2 + if n >= (s - 1) * l + 2: + for i in range(1, n + 1): + model.addConstr(x_P[(0, i)] + x_D[(0, i)] <= 1, name=f"pos_out_{i}") + model.addConstr(x_P[(i, 0)] + x_D[(i, 0)] <= 1, name=f"pos_in_{i}") + + model.update() + + # Store data for callback + model._x_P = x_P + model._x_D = x_D + model._n = n + model._s = s + model._l = l + + # Optimize with callback + model.optimize(dtspms_callback) + + # Extract solution + result = {"problem": "DTSPMS", "instance": instance_path} + + if model.SolCount > 0: + obj_val = model.ObjVal + result["objective_value"] = obj_val + result["status"] = "optimal" if model.Status == GRB.OPTIMAL else "feasible" + result["gap"] = model.MIPGap if hasattr(model, 'MIPGap') else 0.0 + + # Extract tours + x_P_sol = {} + x_D_sol = {} + for (i, j), var in x_P.items(): + x_P_sol[(i, j)] = var.X + for (i, j), var in x_D.items(): + x_D_sol[(i, j)] = var.X + + pickup_tour = extract_tour(n, x_P_sol) + delivery_tour = extract_tour(n, x_D_sol) + + result["pickup_tour"] = pickup_tour + result["delivery_tour"] = delivery_tour + + # Compute individual tour costs + if pickup_tour: + p_cost = sum(c_P[pickup_tour[i]][pickup_tour[i + 1]] for i in range(len(pickup_tour) - 1)) + p_cost += c_P[pickup_tour[-1]][pickup_tour[0]] + result["pickup_cost"] = p_cost + if delivery_tour: + d_cost = sum(c_D[delivery_tour[i]][delivery_tour[i + 1]] for i in range(len(delivery_tour) - 1)) + d_cost += c_D[delivery_tour[-1]][delivery_tour[0]] + result["delivery_cost"] = d_cost + else: + result["objective_value"] = None + result["status"] = "infeasible_or_no_solution" + + result["solve_time"] = model.Runtime + + # Write solution + with open(solution_path, 'w') as f: + json.dump(result, f, indent=2) + + print(f"\nSolution written to {solution_path}") + if model.SolCount > 0: + print(f"Objective value: {result['objective_value']}") + print(f"Status: {result['status']}") + print(f"Pickup tour: {result.get('pickup_tour')}") + print(f"Delivery tour: {result.get('delivery_tour')}") + + return result + + +# ============================================================================= +# MAIN +# ============================================================================= + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Solve DTSPMS using Gurobi (branch-and-cut)") + parser.add_argument("--instance_path", type=str, required=True, + help="Path to the JSON instance file") + parser.add_argument("--solution_path", type=str, default="gurobi_solution_1.json", + help="Path for the output solution JSON file") + parser.add_argument("--time_limit", type=int, default=3600, + help="Maximum solver runtime in seconds") + parser.add_argument("--log_path", type=str, default=None, help="Path to log incumbent solutions") + args = parser.parse_args() + install_gurobi_logger(args.log_path) + + solve_dtspms(args.instance_path, args.solution_path, args.time_limit) diff --git a/alba2013/gurobi_feasi_result/large_feasi_result_1.json b/alba2013/gurobi_feasi_result/large_feasi_result_1.json new file mode 100644 index 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sha256:4ae7a80111ba7861b3313e3e4247407a6a84b97abca6e0f7cbeb3436664607a1 +size 19226 diff --git a/alba2013/instance/tiny_instance.json b/alba2013/instance/tiny_instance.json new file mode 100644 index 0000000000000000000000000000000000000000..99ef0189f9e0d02a8b257d24f29f6faaae22fb74 --- /dev/null +++ b/alba2013/instance/tiny_instance.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b0ef2179b3ecfe83be5d2a8c2017d0c1f401cb6ba72938cad31218b70803b04e +size 5649 diff --git a/alba2013/instance_schema.json b/alba2013/instance_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..1978ab462ec0ce03aa0f356a6a275b37c7f955f0 --- /dev/null +++ b/alba2013/instance_schema.json @@ -0,0 +1,22 @@ +{ + "parameters": { + "num_stacks_s": " Number of stacks in the vehicle's loading area.", + "stack_height_l": " Maximum number of items each stack can hold.", + "num_requests_n": " Number of customer requests to be served.", + "num_pickup_vertices": " Total number of vertices in the pickup region, including the depot.", + "num_delivery_vertices": " Total number of vertices in the delivery region, including the depot.", + "coordinate_range": " Minimum and maximum coordinate values for vertex locations.", + "distance_metric": " Method used to compute travel distances between vertices." + }, + "pickup_region": { + "depot_index": " Index of the depot vertex in the pickup region.", + "coordinates": " Location of each vertex in the pickup region, with fields vertex_id (int), x (int), and y (int).", + "distance_matrix": " Travel cost between each pair of vertices in the pickup region." + }, + "delivery_region": { + "depot_index": " Index of the depot vertex in the delivery region.", + "coordinates": " Location of each vertex in the delivery region, with fields vertex_id (int), x (int), and y (int).", + "distance_matrix": " Travel cost between each pair of vertices in the delivery region." + }, + "requests": " Each customer request specifying request_id (int), pickup_vertex (int), delivery_vertex (int), and item_size (int)." +} diff --git a/alba2013/mathematical_formulation.md b/alba2013/mathematical_formulation.md new file mode 100644 index 0000000000000000000000000000000000000000..e2cd16bcb3bb976402b69970cae40a0164b5d0dc --- /dev/null +++ b/alba2013/mathematical_formulation.md @@ -0,0 +1,42 @@ +# Original Formulation: Double Traveling Salesman Problem with Multiple Stacks (DTSPMS) + +*Source: “A Branch-and-Cut Algorithm for the Double Traveling Salesman Problem with Multiple Stacks,” Alba Martı́nez, Cordeau, Dell’Amico, and Iori, INFORMS Journal on Computing, 2013.* + +## Sets and Parameters + +- $n$: number of customer requests. + +- $s$: number of stacks in the vehicle; $l$: maximum height of each stack. + +- $G^P = (V^P, A^P)$, $G^D = (V^D, A^D)$: complete directed graphs for the pickup and delivery regions. + +- $V^T = \{0^T\} \cup V^T_0$ for $T \in \{P,D\}$, where $0^T$ is the depot, $V^P_0 = \{1^P,\ldots,n^P\}$, $V^D_0 = \{1^D,\ldots,n^D\}$. + +- Each request $i \in \{1,\ldots,n\}$ is associated with pickup vertex $i^P$ and delivery vertex $i^D$. + +- $c^T_{ij} \ge 0$: routing cost of arc $(i,j) \in A^T$, $T \in \{P,D\}$; $c^T$ from $0^P$ to $0^D$ is zero. + +## Decision Variables + +- $x^T_{ij} \in \{0,1\}$, for $(i,j) \in A^T$, $T \in \{P,D\}$: equals $1$ iff vertex $j^T$ is visited immediately after $i^T$. + +## Objective + +$$\begin{equation} +\min \sum_{T \in \{P,D\}} \sum_{(i,j) \in A^T} c^T_{ij}\, x^T_{ij} \tag{1} +\end{equation}$$ + +## Constraints + +$$\begin{align} +\sum_{j \in V^T} x^T_{ij} &= 1 & i \in V^T,\; T \in \{P,D\} \tag{2} \\ +\sum_{i \in V^T} x^T_{ij} &= 1 & j \in V^T,\; T \in \{P,D\} \tag{3} \\ +\sum_{i \in S} \sum_{j \in S} x^T_{ij} &\le |S| - 1 & S \subsetneq V^T,\; |S| \ge 2,\; T \in \{P,D\} \tag{4} \\ +x^T_{ij} &\in \{0,1\} & (i,j) \in A^T,\; T \in \{P,D\} \tag{5} +\end{align}$$ + +## Infeasible Path (Loading) Constraint + +For any load-infeasible pair of paths $(Q,F)$ with $Q = \{p_1,\ldots,p_q\} \subseteq V^P_0$ and $F = \{d_1,\ldots,d_f\} \subseteq V^D_0$: $$\begin{equation} +\sum_{j=1}^{q-1} x^P_{p_j, p_{j+1}} + \sum_{j=1}^{f-1} x^D_{d_j, d_{j+1}} \le |A(Q)| + |A(F)| - 1 \tag{6} +\end{equation}$$ diff --git a/alba2013/problem_description.txt b/alba2013/problem_description.txt new file mode 100644 index 0000000000000000000000000000000000000000..b2ac9239ed93ac1ef287c8ef4f8fb56d59051073 --- /dev/null +++ b/alba2013/problem_description.txt @@ -0,0 +1,9 @@ +# Problem Description + +A single vehicle must serve n customer requests, where each request consists of picking up one unit-size item at a location in a pickup region and delivering it to a corresponding location in a separate delivery region. The vehicle must complete all pickups before beginning any deliveries. Specifically, the vehicle starts at a pickup depot, visits each of the n pickup locations exactly once, returns to the pickup depot, then travels to a delivery depot, visits each of the n delivery locations exactly once, and returns to the delivery depot. The travel cost from the pickup depot to the delivery depot is zero. + +The pickup region is described by a complete directed graph whose vertices are the pickup depot plus the n pickup locations, and the delivery region is described by a separate complete directed graph whose vertices are the delivery depot plus the n delivery locations. Each arc in each graph has a given routing cost of zero or greater. The input provides the full distance matrix for each region, computed as rounded Euclidean distances from the coordinates of the vertices. + +The vehicle has a loading area divided into s stacks, each of which can hold at most l items. Items are placed on top of a stack during pickup and removed from the top of a stack during delivery, following a last-in-first-out policy. This means that if one request is picked up before another and both items are loaded into the same stack, then the later-picked-up item must be delivered before the earlier-picked-up item. + +The planner must determine a cyclic route through the pickup region that starts and ends at the pickup depot and visits every pickup location exactly once, a cyclic route through the delivery region that starts and ends at the delivery depot and visits every delivery location exactly once, and an assignment of each item to a stack, such that the loading plan satisfies the stack capacities and the last-in-first-out unloading requirement. Each pickup location and the pickup depot must have exactly one arc entering it and exactly one arc leaving it within the pickup route, and likewise each delivery location and the delivery depot must have exactly one arc entering it and exactly one arc leaving it within the delivery route. Both routes must form single connected cycles, meaning no subset of two or more vertices may form a closed loop disconnected from the rest of the route. Any pair of partial pickup and delivery paths for which there is no way to load the items common to both paths into the s stacks of height l while respecting the last-in-first-out policy is load-incompatible and may not appear simultaneously in the chosen routes; that is, for any such pair of sub-paths, at least one arc from their combined arc set must be absent from the solution. The goal is to minimize the total routing cost, which is the sum of all arc costs traversed in the pickup route plus all arc costs traversed in the delivery route. diff --git a/alba2013/solution_logger.py b/alba2013/solution_logger.py new file mode 100644 index 0000000000000000000000000000000000000000..5393fa935ce71df9da95cee04fe5199077cb8461 --- /dev/null +++ b/alba2013/solution_logger.py @@ -0,0 +1,61 @@ +""" +Convergence logger for optimization algorithms. + +Records incumbent solutions with timestamps to a JSONL file. +This module is provided to LLM-generated programs — they only need to call +`log(objective_value)` whenever a better feasible solution is found. + +Usage in generated code: + from solution_logger import SolutionLogger + logger = SolutionLogger(log_path, sense="minimize") # or "maximize" + # ... inside algorithm loop: + logger.log(objective_value) +""" + +import json +import time + + +class SolutionLogger: + def __init__(self, log_path, sense="minimize"): + """ + Args: + log_path: Path to the JSONL output file. + sense: "minimize" or "maximize". + """ + self.log_path = log_path + self.sense = sense + self.start_time = time.time() + self.best_obj = None + self.min_interval = 0.1 # seconds, avoid excessive writes + + self._last_log_time = 0.0 + # Clear the file + with open(self.log_path, "w") as f: + pass + + def log(self, objective_value): + """Record a new incumbent if it improves on the best known.""" + if objective_value is None: + return + + # Check if this is an improvement + if self.best_obj is not None: + if self.sense == "minimize" and objective_value >= self.best_obj: + return + if self.sense == "maximize" and objective_value <= self.best_obj: + return + + elapsed = time.time() - self.start_time + + # Throttle writes + if self.best_obj is not None and elapsed - self._last_log_time < self.min_interval: + self.best_obj = objective_value + return + + self.best_obj = objective_value + self._last_log_time = elapsed + + with open(self.log_path, "a") as f: + f.write(json.dumps({"time": round(elapsed, 3), + "objective_value": objective_value}) + "\n") diff --git a/alba2013/solution_schema.json b/alba2013/solution_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..5f333f52da086e35bc67bd7021021374f12cfe77 --- /dev/null +++ b/alba2013/solution_schema.json @@ -0,0 +1,5 @@ +{ + "objective_value": " Total routing distance traveled across the pickup tour and the delivery tour combined.", + "pickup_tour": " Ordered sequence of pickup-region vertices visited by the vehicle, starting and ending at the pickup depot.", + "delivery_tour": " Ordered sequence of delivery-region vertices visited by the vehicle, starting and ending at the delivery depot." +} diff --git a/alfieri2020/feasibility_check.py b/alfieri2020/feasibility_check.py new file mode 100644 index 0000000000000000000000000000000000000000..1f0b5121388f66f4a384340dfa4eee5124f4bfa3 --- /dev/null +++ b/alfieri2020/feasibility_check.py @@ -0,0 +1,637 @@ +""" +feasibility_check.py +==================== +Feasibility checker for Graph Coloring Problem (GCP) solutions. + +Checks hard constraints from the mathematical model (math_model.txt): + Constraint (2): sum_{c in C} x_{i,c} = 1, for all i in V (assignment) + Constraint (3): x_{i,c} + x_{j,c} <= z_c, for all (i,j) in E, c in C (conflict) + Constraint (4): x_{i,c} in {0,1}, for all i in V, c in C (binary x) + Constraint (5): z_c in {0,1}, for all c in C (binary z) + Constraint (15): objective consistency -- reported objective_value + must equal the number of distinct colors used in + `coloring` (full recompute), or fall within + [1, greedy_upper_bound] for solutions without + explicit `coloring` (lower/upper bound check). + +Constraints (7)-(8) belong to GCP-SC, (10)-(11) to RMP, (13)-(14) to MWISP. +These are checked only when the solution provides the corresponding variables. + +Gurobi solutions provide a `coloring` dict -> constraints 2-5 are checked. +Efficient (CG-MLPH) solutions provide only an LP bound with no decision +variable values -> no variable-level constraints can be verified. +""" + +import argparse +import json +import sys + +# FRONTIEROR_RELEASE_CONTRACT_V2 +_FRONTIEROR_REQUIRED_SOLUTION_FIELDS = ('coloring', 'objective_value') +_FRONTIEROR_NONEMPTY_SOLUTION_FIELDS = () +_FRONTIEROR_DERIVED_FIELD_RULES = () +_FRONTIEROR_CONDITIONAL_FIELD_RULES = () + + +def _frontieror_validate_solution_contract(solution): + """Return a structured rejection when the public solution contract is absent.""" + violations = [] + if not isinstance(solution, dict): + violations.append("solution must be a JSON object") + else: + missing = [ + field + for field in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS + if field not in solution or solution[field] is None + ] + if missing: + violations.append( + "missing required solution field(s): " + ", ".join(missing) + ) + + for discriminator, expected_value, fields in _FRONTIEROR_CONDITIONAL_FIELD_RULES: + if solution.get(discriminator) != expected_value: + continue + conditional_missing = [ + field + for field in fields + if field not in solution or solution[field] is None + ] + if conditional_missing: + violations.append( + "missing conditionally required solution field(s): " + + ", ".join(conditional_missing) + ) + + empty = [ + field + for field in _FRONTIEROR_NONEMPTY_SOLUTION_FIELDS + if field in solution and solution[field] in (None, [], {}) + ] + if empty: + violations.append( + "empty required decision witness field(s): " + ", ".join(empty) + ) + + for target, operation, source, source_key in _FRONTIEROR_DERIVED_FIELD_RULES: + try: + if operation == "assignment_key_set": + reported_set = set(solution[target]) + expected_set = {entry[source_key] for entry in solution[source]} + if reported_set != expected_set: + violations.append( + f"{target}={sorted(reported_set)} does not match the " + f"decision-derived value {sorted(expected_set)}" + ) + continue + + reported = float(solution[target]) + if operation == "length": + expected = float(len(solution[source])) + elif operation == "mapping_value": + expected = float(solution[source][source_key]) + else: + raise ValueError("unknown derived-field operation") + except (KeyError, TypeError, ValueError, OverflowError): + violations.append( + f"cannot derive {target} from decision witness field {source}" + ) + continue + if ( + reported != reported + or expected != expected + or reported in (float("inf"), float("-inf")) + or expected in (float("inf"), float("-inf")) + or abs(reported - expected) > 1e-6 + ): + violations.append( + f"{target}={reported} does not match the decision-derived " + f"value {expected}" + ) + + if "objective_value" in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS: + objective = solution.get("objective_value") + if ( + isinstance(objective, bool) + or not isinstance(objective, (int, float)) + or objective != objective + or objective in (float("inf"), float("-inf")) + ): + violations.append("objective_value must be a finite JSON number") + + if not violations: + return None + return { + "feasible": False, + "violated_constraints": ["solution_schema"], + "violations": violations, + "violation_magnitudes": [], + } + + +def _frontieror_write_unexpected_rejection(error): + """Turn malformed candidate/checker exceptions into a normal rejection.""" + import json as _frontieror_json + import sys as _frontieror_sys + + result_path = None + for index, argument in enumerate(_frontieror_sys.argv): + if argument == "--result_path" and index + 1 < len(_frontieror_sys.argv): + result_path = _frontieror_sys.argv[index + 1] + break + if argument.startswith("--result_path="): + result_path = argument.split("=", 1)[1] + break + if not result_path: + return False + try: + with open(result_path, "w") as result_handle: + _frontieror_json.dump( + { + "feasible": False, + "violated_constraints": ["malformed_solution"], + "violations": [ + "checker rejected malformed candidate: " + + type(error).__name__ + + ": " + + str(error)[:500] + ], + "violation_magnitudes": [], + }, + result_handle, + indent=2, + ) + except Exception: + return False + return True + + +TOL = 1e-5 +EPS = 1e-5 + +# Constraint index used for objective-value consistency. Chosen as the +# next unused index after the highest model-constraint number (14, MWISP). +OBJ_CONSISTENCY_IDX = 15 + + +def greedy_coloring(n: int, edges: list) -> int: + """Compute greedy coloring upper bound. Returns number of colors used.""" + adj = [[] for _ in range(n)] + for u, v in edges: + adj[u].append(v) + adj[v].append(u) + colors = [-1] * n + for v in range(n): + used = {colors[u] for u in adj[v] if colors[u] != -1} + c = 0 + while c in used: + c += 1 + colors[v] = c + return max(colors) + 1 if n > 0 else 0 + + +def check_gurobi_solution(instance: dict, solution: dict) -> dict: + """Check constraints 2-5 of GCP-compact for a solution with coloring.""" + n = instance["num_nodes"] + edges = instance["edges"] + coloring = solution["coloring"] # dict: str(vertex) -> color + + violations = [] + violation_magnitudes = [] + violated_set = set() + + # Reconstruct x_{i,c} and z_c from coloring + # coloring maps vertex (as string) -> color index + color_assignment = {} + for key, val in coloring.items(): + color_assignment[int(key)] = int(val) + + # ------------------------------------------------------------------ + # Vertex index validation: indices must be in {0, ..., n-1} + # ------------------------------------------------------------------ + for i in color_assignment: + if i < 0 or i >= n: + violated_set.add(2) + violations.append( + f"Constraint (2): Vertex index {i} is out of range [0, {n-1}]" + ) + violation_magnitudes.append({ + "constraint": 2, + "lhs": 0.0, + "rhs": 1.0, + "raw_excess": 1.0, + "normalizer": 1.0, + "ratio": 1.0, + }) + + used_colors = set(color_assignment.values()) + num_colors = max(used_colors) + 1 if used_colors else 0 + + # Color set size as determined by greedy_coloring (same as gurobi_code.py) + model_num_colors = greedy_coloring(n, edges) + + # ------------------------------------------------------------------ + # Color label validation: colors may take any non-negative integer + # labels — the labels themselves are arbitrary (e.g., colors + # {5, 10, 20} uses 3 distinct colors, same as {0, 1, 2}). What matters + # is the NUMBER of distinct colors used (= the objective value), which + # the paper's greedy upper bound caps. We skip the previous check that + # forced labels into [0, model_num_colors - 1], because that's a + # Gurobi formulation restriction, not a business rule. Negative or + # non-integer labels are still flagged. + # ------------------------------------------------------------------ + for i, c in color_assignment.items(): + if not isinstance(c, int) or c < 0: + violated_set.add(4) + violations.append( + f"Constraint (4): Vertex {i} assigned invalid color {c} " + f"(must be a non-negative integer label)" + ) + violation_magnitudes.append({ + "constraint": 4, + "lhs": float(c) if isinstance(c, (int, float)) else 0.0, + "rhs": 0.0, + "raw_excess": 1.0, + "normalizer": max(1.0, EPS), + "ratio": 1.0, + }) + + # Also verify that the number of distinct colors used does not exceed + # the greedy upper bound (a valid instance-level sanity check). + num_distinct_used = len(set(color_assignment.values())) if color_assignment else 0 + if num_distinct_used > model_num_colors: + violated_set.add(4) + violations.append( + f"Constraint (4): number of distinct colors used ({num_distinct_used}) " + f"exceeds greedy upper bound ({model_num_colors})" + ) + violation_magnitudes.append({ + "constraint": 4, + "lhs": float(num_distinct_used), + "rhs": float(model_num_colors), + "raw_excess": float(num_distinct_used - model_num_colors), + "normalizer": max(float(model_num_colors), EPS), + "ratio": float(num_distinct_used - model_num_colors) / max(float(model_num_colors), EPS), + }) + + # z_c = 1 if color c is used, 0 otherwise + z = {c: 1 for c in used_colors} + + # ------------------------------------------------------------------ + # Constraint (2): sum_{c in C} x_{i,c} = 1 for all i in V + # Each vertex must be assigned exactly one color. + # ------------------------------------------------------------------ + for i in range(n): + if i not in color_assignment: + # Vertex not assigned any color: LHS=0, RHS=1 + lhs = 0.0 + rhs = 1.0 + violation_amount = abs(lhs - rhs) + if violation_amount > TOL: + violated_set.add(2) + violations.append( + f"Constraint (2): Vertex {i} is not assigned any color " + f"(sum x_{{i,c}} = {lhs}, expected 1)" + ) + normalizer = max(abs(rhs), EPS) + violation_magnitudes.append({ + "constraint": 2, + "lhs": lhs, + "rhs": rhs, + "raw_excess": violation_amount, + "normalizer": normalizer, + "ratio": violation_amount / normalizer, + }) + + # Check for vertices assigned multiple colors (not possible with dict, + # but check that values are valid non-negative integers) + for i in range(n): + if i in color_assignment: + c = color_assignment[i] + if not isinstance(c, int) or c < 0: + lhs = 0.0 + rhs = 1.0 + violation_amount = abs(lhs - rhs) + violated_set.add(2) + violations.append( + f"Constraint (2): Vertex {i} has invalid color {c}" + ) + normalizer = max(abs(rhs), EPS) + violation_magnitudes.append({ + "constraint": 2, + "lhs": lhs, + "rhs": rhs, + "raw_excess": violation_amount, + "normalizer": normalizer, + "ratio": violation_amount / normalizer, + }) + + # ------------------------------------------------------------------ + # Constraint (3): x_{i,c} + x_{j,c} <= z_c for all (i,j) in E, c in C + # For a coloring, this means: if adjacent vertices i,j share color c, + # then LHS = 2, RHS = z_c = 1, so violated (2 > 1). + # ------------------------------------------------------------------ + for u, v in edges: + if u in color_assignment and v in color_assignment: + if color_assignment[u] == color_assignment[v]: + c = color_assignment[u] + lhs = 2.0 # x_{u,c} + x_{v,c} = 1 + 1 + rhs = float(z.get(c, 0)) # z_c (1 if color used) + violation_amount = max(lhs - rhs, 0.0) + if violation_amount > TOL: + violated_set.add(3) + violations.append( + f"Constraint (3): Adjacent vertices {u} and {v} " + f"share color {c} (x_{{{u},{c}}} + x_{{{v},{c}}} = " + f"{lhs} > z_{{{c}}} = {rhs})" + ) + normalizer = max(abs(rhs), EPS) + violation_magnitudes.append({ + "constraint": 3, + "lhs": lhs, + "rhs": rhs, + "raw_excess": violation_amount, + "normalizer": normalizer, + "ratio": violation_amount / normalizer, + }) + + # ------------------------------------------------------------------ + # Constraint (4): x_{i,c} in {0,1} for all i in V, c in C + # Automatically satisfied by the coloring representation (each vertex + # maps to exactly one integer color, so x_{i,c} is 0 or 1). + # We verify integrality of the color values. + # ------------------------------------------------------------------ + for i in range(n): + if i in color_assignment: + c = color_assignment[i] + if not isinstance(c, (int, float)): + violated_set.add(4) + violations.append( + f"Constraint (4): Vertex {i} color value {c} is not binary" + ) + violation_magnitudes.append({ + "constraint": 4, + "lhs": float(c) if isinstance(c, (int, float)) else 0.0, + "rhs": 1.0, + "raw_excess": 1.0, + "normalizer": 1.0, + "ratio": 1.0, + }) + elif isinstance(c, float) and abs(c - round(c)) > TOL: + lhs = c + rhs = round(c) + violation_amount = abs(lhs - rhs) + violated_set.add(4) + violations.append( + f"Constraint (4): Vertex {i} color {c} is not integer" + ) + normalizer = max(abs(rhs), EPS) + violation_magnitudes.append({ + "constraint": 4, + "lhs": lhs, + "rhs": rhs, + "raw_excess": violation_amount, + "normalizer": normalizer, + "ratio": violation_amount / normalizer, + }) + + # ------------------------------------------------------------------ + # Constraint (5): z_c in {0,1} for all c in C + # Automatically satisfied: z_c is derived as 1 if color c is used, + # 0 otherwise. Always integral by construction. + # ------------------------------------------------------------------ + # No violations possible from the coloring representation. + + # ------------------------------------------------------------------ + # Symmetry-breaking constraint (z_c >= z_{c+1}) SKIPPED: this is a + # Gurobi-specific tie-breaking device to reduce the search tree when + # color labels are interchangeable. It is NOT a business rule — a + # valid coloring that uses colors {0, 2, 5} is just as legitimate as + # one that uses {0, 1, 2}. An LLM algorithm may output colors with + # arbitrary labels (e.g., from a heuristic that never renumbers); + # the adjacency + cluster-assignment constraints already guarantee + # a proper coloring, and the objective counts distinct colors used. + # ------------------------------------------------------------------ + + # ------------------------------------------------------------------ + # Constraint (15): objective-value consistency (Tier C defense). + # The GCP objective is sum_{c in C} z_c = number of distinct colors + # used. With the full coloring in hand we recompute this exactly and + # reject solutions whose reported `objective_value` disagrees. + # Tolerance: 0.5 (integer count — a mismatch of >= 1 fires). + # ------------------------------------------------------------------ + reported_obj = solution.get("objective_value") + if reported_obj is not None: + try: + reported = float(reported_obj) + except (TypeError, ValueError): + reported = None + if reported is not None: + true_obj = float(len(set(color_assignment.values()))) if color_assignment else 0.0 + abs_diff = abs(reported - true_obj) + tol = 0.5 # objective is an integer count + if abs_diff > tol: + violated_set.add(OBJ_CONSISTENCY_IDX) + violations.append( + f"Constraint ({OBJ_CONSISTENCY_IDX}): Objective consistency " + f"violated: reported objective_value={reported} differs from " + f"recomputed |{{c : exists i with x_{{i,c}}=1}}|={true_obj} " + f"(|diff|={abs_diff:.3g}, tol={tol:.3g})" + ) + normalizer = max(abs(true_obj), EPS) + violation_magnitudes.append({ + "constraint": OBJ_CONSISTENCY_IDX, + "lhs": reported, + "rhs": true_obj, + "raw_excess": abs_diff, + "normalizer": normalizer, + "ratio": abs_diff / normalizer, + }) + + feasible = len(violated_set) == 0 + return { + "feasible": feasible, + "violated_constraints": sorted(violated_set), + "violations": violations, + "violation_magnitudes": violation_magnitudes, + } + + +def check_efficient_solution(instance: dict, solution: dict) -> dict: + """ + Check feasibility of an efficient (CG-MLPH) solution. + + The efficient algorithm outputs only an LP lower bound and metadata + (objective_value, lp_lower_bound, num_cg_iterations, etc.) without + explicit decision variable values. Without x_s values, constraints + (7)-(8) of GCP-SC and (10)-(11) of RMP cannot be verified at the + variable level. + + We verify basic consistency: the objective value must be non-negative + (chromatic number >= 1 for non-empty graphs), and must not exceed + the greedy upper bound (a valid LP / IP solution can never exceed it). + """ + violations = [] + violation_magnitudes = [] + violated_set = set() + + n = instance["num_nodes"] + edges = instance["edges"] + obj = solution.get("objective_value") + + if obj is not None and n > 0: + # The LP lower bound should be >= 1 for a non-empty graph + # (at least one color is needed). This is implied by constraint (7)/(10): + # sum x_s >= 1 for each vertex, and objective = sum x_s >= 1. + # However, the LP relaxation can yield fractional values, so + # obj >= 1 is not strictly required. We only flag if obj < 0. + if obj < -TOL: + lhs = obj + rhs = 0.0 + violation_amount = abs(lhs) + violated_set.add(7) + violations.append( + f"Constraint (7): Objective value {obj} is negative, " + f"violating non-negativity implied by coverage constraints" + ) + normalizer = max(abs(rhs), EPS) + violation_magnitudes.append({ + "constraint": 7, + "lhs": lhs, + "rhs": rhs, + "raw_excess": violation_amount, + "normalizer": normalizer, + "ratio": violation_amount / normalizer, + }) + + # ------------------------------------------------------------------ + # Constraint (15): objective-value consistency (Tier C defense). + # Without decision variables we cannot fully recompute the LP / + # chromatic-number objective. Instead we apply the tightest + # data-derived envelope on the LP relaxation value: + # 1.0 <= obj <= greedy_coloring(V, E) + # The lower bound 1 holds for any non-empty graph: every vertex must + # be covered (sum_{s ∋ i} x_s >= 1), so the LP minimum is >= 1. + # The upper bound holds because the greedy coloring constructs an + # integer-feasible solution, which dominates the LP / IP optimum. + # ------------------------------------------------------------------ + if obj is not None and n > 0: + try: + reported = float(obj) + except (TypeError, ValueError): + reported = None + if reported is not None: + lower_bound = 1.0 + upper_bound = float(greedy_coloring(n, edges)) + # 0.1% relative slack on each side to absorb numerical noise. + lo_tol = max(1e-3, 1e-3 * abs(lower_bound)) + hi_tol = max(1e-3, 1e-3 * abs(upper_bound)) + if reported < lower_bound - lo_tol or reported > upper_bound + hi_tol: + if reported < lower_bound - lo_tol: + violation_amount = lower_bound - reported + bound_str = ( + f"below LP lower bound 1.0 " + f"(any non-empty graph requires >=1 color)" + ) + else: + violation_amount = reported - upper_bound + bound_str = ( + f"above greedy upper bound {upper_bound} " + f"(LP / IP optimum cannot exceed the constructive greedy)" + ) + violated_set.add(OBJ_CONSISTENCY_IDX) + violations.append( + f"Constraint ({OBJ_CONSISTENCY_IDX}): Objective consistency " + f"violated: reported objective_value={reported} is {bound_str} " + f"(envelope=[{lower_bound}, {upper_bound}])" + ) + normalizer = max(abs(upper_bound), EPS) + violation_magnitudes.append({ + "constraint": OBJ_CONSISTENCY_IDX, + "lhs": reported, + "rhs": upper_bound if reported > upper_bound else lower_bound, + "raw_excess": violation_amount, + "normalizer": normalizer, + "ratio": violation_amount / normalizer, + }) + + feasible = len(violated_set) == 0 + return { + "feasible": feasible, + "violated_constraints": sorted(violated_set), + "violations": violations, + "violation_magnitudes": violation_magnitudes, + } + + +def check_feasibility(instance: dict, solution: dict) -> dict: + """Dispatch the same way as main(): coloring -> gurobi check, else efficient check.""" + if "coloring" in solution and solution["coloring"] is not None: + return check_gurobi_solution(instance, solution) + return check_efficient_solution(instance, solution) + + +def main(): + parser = argparse.ArgumentParser( + description="Feasibility checker for Graph Coloring Problem solutions" + ) + parser.add_argument( + "--instance_path", + type=str, + required=True, + help="Path to the JSON file containing the data instance", + ) + parser.add_argument( + "--solution_path", + type=str, + required=True, + help="Path to the JSON file containing the candidate solution", + ) + parser.add_argument( + "--result_path", + type=str, + required=True, + help="Path to write the JSON file containing the feasibility result", + ) + args = parser.parse_args() + + with open(args.instance_path) as f: + instance = json.load(f) + + with open(args.solution_path) as f: + solution = json.load(f) + _frontieror_contract_result = _frontieror_validate_solution_contract(solution) + if _frontieror_contract_result is not None: + with open(args.result_path, "w") as _frontieror_result_handle: + json.dump( + _frontieror_contract_result, + _frontieror_result_handle, + indent=2, + ) + return + + # Detect solution type based on presence of coloring data + if "coloring" in solution and solution["coloring"] is not None: + result = check_gurobi_solution(instance, solution) + else: + result = check_efficient_solution(instance, solution) + + with open(args.result_path, "w") as f: + json.dump(result, f, indent=2) + + status = "FEASIBLE" if result["feasible"] else "INFEASIBLE" + print(f"Result: {status}") + if not result["feasible"]: + for v in result["violations"]: + print(f" - {v}") + print(f"Result written to {args.result_path}") + + +if __name__ == "__main__": + try: + main() + except Exception as _frontieror_unexpected_error: + if not _frontieror_write_unexpected_rejection( + _frontieror_unexpected_error + ): + raise diff --git a/alfieri2020/gurobi_code.py b/alfieri2020/gurobi_code.py new file mode 100644 index 0000000000000000000000000000000000000000..4f52f61b4066d5d96c88aa910a8862c127b45183 --- /dev/null +++ b/alfieri2020/gurobi_code.py @@ -0,0 +1,203 @@ +""" +gurobi_code.py +============== +Implements the GCP-compact formulation for the Graph Coloring Problem (GCP) +using Gurobi as the MIP solver. + +Paper: "Enhancing Column Generation by a Machine-Learning-Based + Pricing Heuristic for Graph Coloring" +Authors: Shen, Sun, Li, Eberhard, Ernst (2022, AAAI) + +Mathematical Model (GCP-compact): + min sum_{c in C} z_c (1) + s.t. sum_{c in C} x_{i,c} = 1, for all i in V (2) + x_{i,c} + x_{j,c} <= z_c, for all (i,j) in E, c (3) + x_{i,c} in {0,1}, for all i in V, c in C (4) + z_c in {0,1}, for all c in C (5) +""" + +import argparse +import json +import sys +import gurobipy as gp +from gurobipy import GRB +import os as _os, sys as _sys +# Walk up from this file's directory to find repo root (containing scripts/). +_repo = _os.path.dirname(_os.path.abspath(__file__)) +while _repo != _os.path.dirname(_repo) and not _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _repo = _os.path.dirname(_repo) +if _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _sys.path.insert(0, _repo) +try: + from scripts.utils.gurobi_log_helper import install_gurobi_logger +except ImportError: + def install_gurobi_logger(log_path): # no-op fallback when scripts/ unavailable + pass +def greedy_coloring(n: int, adj: list) -> int: + """ + Compute an upper bound on the chromatic number via greedy coloring. + Returns the number of colors used. + """ + colors = [-1] * n + for v in range(n): + used = {colors[u] for u in adj[v] if colors[u] != -1} + c = 0 + while c in used: + c += 1 + colors[v] = c + return max(colors) + 1 + + +def solve_gcp_compact(instance_path: str, solution_path: str, time_limit: int): + """ + Solve the Graph Coloring Problem using the GCP-compact MIP formulation. + """ + # ---------------------------------------------------------------- + # Load instance + # ---------------------------------------------------------------- + with open(instance_path) as f: + data = json.load(f) + + n = data["num_nodes"] + edges = data["edges"] + + # Build adjacency list + adj = [[] for _ in range(n)] + edge_set = set() + for u, v in edges: + adj[u].append(v) + adj[v].append(u) + edge_set.add((min(u, v), max(u, v))) + + E = list(edge_set) # undirected edge list (u < v) + V = list(range(n)) + + # ---------------------------------------------------------------- + # Determine number of colors C + # Upper bound: greedy chromatic number. + # NOTE (INFERRED ASSUMPTION): The paper does not specify how |C| is + # set in the GCP-compact formulation. Using a greedy coloring upper + # bound reduces the model size while preserving optimality. + # ---------------------------------------------------------------- + num_colors = greedy_coloring(n, adj) + C = list(range(num_colors)) + + # ---------------------------------------------------------------- + # Build Gurobi model + # ---------------------------------------------------------------- + model = gp.Model("GCP-compact") + model.setParam("Threads", 1) + model.setParam("TimeLimit", time_limit) + model.setParam("OutputFlag", 1) + + # Decision variables + # x[i, c] = 1 if vertex i is assigned color c (eq. 4) + x = model.addVars(n, num_colors, vtype=GRB.BINARY, name="x") + # z[c] = 1 if color c is used (eq. 5) + z = model.addVars(num_colors, vtype=GRB.BINARY, name="z") + + # Objective: minimize number of colors used (eq. 1) + model.setObjective(gp.quicksum(z[c] for c in C), GRB.MINIMIZE) + + # Constraint (2): each vertex receives exactly one color + for i in V: + model.addConstr( + gp.quicksum(x[i, c] for c in C) == 1, + name=f"assign_{i}" + ) + + # Constraint (3): adjacent vertices cannot share a color; + # if both endpoints use color c then z[c] must be 1 + for (i, j) in E: + for c in C: + model.addConstr( + x[i, c] + x[j, c] <= z[c], + name=f"conflict_{i}_{j}_{c}" + ) + + # ---------------------------------------------------------------- + # Symmetry-breaking constraints (INFERRED ASSUMPTION): + # The paper does not specify symmetry-breaking for the compact model. + # We impose z[0] >= z[1] >= ... >= z[num_colors-1] so that colors are + # used in order. This does not change the optimal objective value but + # substantially reduces the number of equivalent optimal solutions. + # ---------------------------------------------------------------- + for c in range(num_colors - 1): + model.addConstr(z[c] >= z[c + 1], name=f"sym_{c}") + + # ---------------------------------------------------------------- + # Solve + # ---------------------------------------------------------------- + model.optimize() + + # ---------------------------------------------------------------- + # Extract solution + # ---------------------------------------------------------------- + if model.SolCount > 0: + obj_val = model.ObjVal + coloring = {} + for i in V: + for c in C: + if x[i, c].X > 0.5: + coloring[str(i)] = c + break + + solution = { + "objective_value": obj_val, + "num_colors_used": int(round(obj_val)), + "coloring": coloring, + "solver_status": model.Status, + "is_optimal": model.Status == GRB.OPTIMAL, + } + else: + # No feasible solution found within time limit + solution = { + "objective_value": None, + "num_colors_used": None, + "coloring": None, + "solver_status": model.Status, + "is_optimal": False, + } + + # ---------------------------------------------------------------- + # Write solution + # ---------------------------------------------------------------- + with open(solution_path, "w") as f: + json.dump(solution, f, indent=2) + + print(f"Solution written to {solution_path}") + print(f"Objective value: {solution['objective_value']}") + return solution + + +def main(): + parser = argparse.ArgumentParser( + description="Solve GCP using GCP-compact MIP formulation via Gurobi" + ) + parser.add_argument( + "--instance_path", + type=str, + required=True, + help="Path to the JSON instance file" + ) + parser.add_argument( + "--solution_path", + type=str, + required=True, + help="Path where the solution JSON will be written" + ) + parser.add_argument( + "--time_limit", + type=int, + required=True, + help="Maximum solver runtime in seconds" + ) + parser.add_argument("--log_path", type=str, default=None, help="Path to log incumbent solutions") + args = parser.parse_args() + install_gurobi_logger(args.log_path) + + solve_gcp_compact(args.instance_path, args.solution_path, args.time_limit) + + +if __name__ == "__main__": + main() diff --git a/alfieri2020/gurobi_feasi_result/large_feasi_result_1.json b/alfieri2020/gurobi_feasi_result/large_feasi_result_1.json new file mode 100644 index 0000000000000000000000000000000000000000..e305b82c3d6732211d5033415df1a105476e9cf1 --- /dev/null +++ b/alfieri2020/gurobi_feasi_result/large_feasi_result_1.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3269392edcbb7720882e77caadb88b4a9cf2fda04cf482cebd23baace4a531fd +size 102 diff --git a/alfieri2020/gurobi_feasi_result/large_feasi_result_2.json b/alfieri2020/gurobi_feasi_result/large_feasi_result_2.json new file mode 100644 index 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b/alfieri2020/instance/large_instance_4.json new file mode 100644 index 0000000000000000000000000000000000000000..fa6a555d149122741bd5ba150026ac4d87e427e2 --- /dev/null +++ b/alfieri2020/instance/large_instance_4.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:273d23cd927695bfc07bcf4dff30b6498eecb6ace2a5c3129af1866aee168b04 +size 1384612 diff --git a/alfieri2020/instance/large_instance_5.json b/alfieri2020/instance/large_instance_5.json new file mode 100644 index 0000000000000000000000000000000000000000..1e640f18f4588ea6f7b9e7dedc86efc1b68343df --- /dev/null +++ b/alfieri2020/instance/large_instance_5.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8c75118103da8358a033d7d785c627223e0fdceeba69caafa8660251570ffb19 +size 1405622 diff --git a/alfieri2020/instance/tiny_instance.json b/alfieri2020/instance/tiny_instance.json new file mode 100644 index 0000000000000000000000000000000000000000..2fa89cb07f3e480ff3aa304eb625c43e9e502dc1 --- /dev/null +++ b/alfieri2020/instance/tiny_instance.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e2dd19b1f100ec6c49dc996fac40d64e02436468706025b4f428033de509cdd6 +size 2419 diff --git a/alfieri2020/instance_schema.json b/alfieri2020/instance_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..be846ce434c5e65b18fcdf012c21297e9425b2ee --- /dev/null +++ b/alfieri2020/instance_schema.json @@ -0,0 +1,5 @@ +{ + "num_nodes": " Total number of vertices in the graph.", + "num_edges": " Total number of edges in the graph.", + "edges": " Each entry is a pair of two connected vertices, with the lower-numbered vertex listed first." +} \ No newline at end of file diff --git a/alfieri2020/mathematical_formulation.md b/alfieri2020/mathematical_formulation.md new file mode 100644 index 0000000000000000000000000000000000000000..9a25f5e64677f00d9f4aba31af99ac621e8054e4 --- /dev/null +++ b/alfieri2020/mathematical_formulation.md @@ -0,0 +1,39 @@ +# Original Formulation: Graph Coloring Problem (GCP-compact) + +*Source: Enhancing Column Generation by a Machine-Learning-Based Pricing Heuristic for Graph Coloring, Yunzhuang Shen, Yuan Sun, Xiaodong Li, Andrew Eberhard, Andreas Ernst, 2022 (AAAI).* + +## Sets and Indices + +- $G=(\mathcal{V},\mathcal{E})$: an undirected graph. + +- $\mathcal{V}$: set of vertices, indexed by $i$ (and $j$). + +- $\mathcal{E}$: set of edges; an edge is an unordered pair $(i,j)$. + +- $\mathcal{C}$: set of candidate colors, indexed by $c$. + +## Parameters + +The instance is fully specified by the graph $G=(\mathcal{V},\mathcal{E})$. The color palette $\mathcal{C}$ is a candidate set of colors; its cardinality $|\mathcal{C}|$ is an upper bound on the chromatic number (in practice obtained by a greedy coloring upper bound). + +## Decision Variables + +- $x_{i,c}\in\{0,1\}$: $1$ if vertex $i$ is assigned color $c$, $0$ otherwise. + +- $z_c\in\{0,1\}$: $1$ if color $c$ is used (in use), $0$ otherwise. + +## Objective + +$$\begin{equation} +\min_{\mathbf{x},\mathbf{z}}\ \sum_{c\in\mathcal{C}} z_c +\tag{GCP-compact, 1} +\end{equation}$$ + +## Constraints + +$$\begin{align} +\sum_{c\in\mathcal{C}} x_{i,c} &= 1, & & i\in\mathcal{V}, \tag{2}\\[2pt] +x_{i,c} + x_{j,c} &\le z_c, & & (i,j)\in\mathcal{E};\ c\in\mathcal{C}, \tag{3}\\[2pt] +x_{i,c} &\in \{0,1\}, & & i\in\mathcal{V};\ c\in\mathcal{C}, \tag{4}\\[2pt] +z_c &\in \{0,1\}, & & c\in\mathcal{C}. \tag{5} +\end{align}$$ diff --git a/alfieri2020/problem_description.txt b/alfieri2020/problem_description.txt new file mode 100644 index 0000000000000000000000000000000000000000..9d3ac08139def4d9836c7cff1f758c9eb0e5dcc4 --- /dev/null +++ b/alfieri2020/problem_description.txt @@ -0,0 +1,3 @@ +# Problem Description + +Given an undirected graph consisting of a set of vertices and a set of edges, where each edge connects two distinct vertices, the task is to color every vertex using as few distinct colors as possible while ensuring that adjacent vertices never share the same color. The input data specifies the total number of vertices, the total number of edges, and the complete list of edges as unordered pairs of vertices. A palette of candidate colors is available, whose size is determined by a greedy coloring upper bound on the chromatic number. Each vertex must be assigned exactly one color from this palette. For every edge and every color, at most one of the two endpoints of that edge may be assigned that color; moreover, if either endpoint receives a particular color, that color is considered in use. The goal is to minimize the total number of colors that are in use, that is, the count of distinct colors appearing in the assignment across the entire graph. diff --git a/alfieri2020/solution_logger.py b/alfieri2020/solution_logger.py new file mode 100644 index 0000000000000000000000000000000000000000..5393fa935ce71df9da95cee04fe5199077cb8461 --- /dev/null +++ b/alfieri2020/solution_logger.py @@ -0,0 +1,61 @@ +""" +Convergence logger for optimization algorithms. + +Records incumbent solutions with timestamps to a JSONL file. +This module is provided to LLM-generated programs — they only need to call +`log(objective_value)` whenever a better feasible solution is found. + +Usage in generated code: + from solution_logger import SolutionLogger + logger = SolutionLogger(log_path, sense="minimize") # or "maximize" + # ... inside algorithm loop: + logger.log(objective_value) +""" + +import json +import time + + +class SolutionLogger: + def __init__(self, log_path, sense="minimize"): + """ + Args: + log_path: Path to the JSONL output file. + sense: "minimize" or "maximize". + """ + self.log_path = log_path + self.sense = sense + self.start_time = time.time() + self.best_obj = None + self.min_interval = 0.1 # seconds, avoid excessive writes + + self._last_log_time = 0.0 + # Clear the file + with open(self.log_path, "w") as f: + pass + + def log(self, objective_value): + """Record a new incumbent if it improves on the best known.""" + if objective_value is None: + return + + # Check if this is an improvement + if self.best_obj is not None: + if self.sense == "minimize" and objective_value >= self.best_obj: + return + if self.sense == "maximize" and objective_value <= self.best_obj: + return + + elapsed = time.time() - self.start_time + + # Throttle writes + if self.best_obj is not None and elapsed - self._last_log_time < self.min_interval: + self.best_obj = objective_value + return + + self.best_obj = objective_value + self._last_log_time = elapsed + + with open(self.log_path, "a") as f: + f.write(json.dumps({"time": round(elapsed, 3), + "objective_value": objective_value}) + "\n") diff --git a/alfieri2020/solution_schema.json b/alfieri2020/solution_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..171eba25ce54ad3781b62970dd9823f6e93f44bb --- /dev/null +++ b/alfieri2020/solution_schema.json @@ -0,0 +1,4 @@ +{ + "objective_value": " Total number of distinct colors used across all vertices.", + "coloring": " Color assigned to each vertex, represented as a zero-based integer." +} \ No newline at end of file diff --git a/amaldi2013/feasibility_check.py b/amaldi2013/feasibility_check.py new file mode 100644 index 0000000000000000000000000000000000000000..7d4c7e3b70984a475947c89842ee5c2f6f32967c --- /dev/null +++ b/amaldi2013/feasibility_check.py @@ -0,0 +1,546 @@ +""" +Feasibility checker for the Minimum Hyperplanes Clustering Problem (Min-HCP). + +Based on: Amaldi, Dhyani, and Ceselli (2013), +"Column Generation for the Minimum Hyperplanes Clustering Problem", +INFORMS Journal on Computing. + +Checks the ORIGINAL geometric problem (circle containment, i.e., point must +lie within epsilon of its assigned hyperplane in Euclidean distance). +Does NOT enforce the big-M linearization (Eq. 11-12) or unit-norm constraint +(Eq. 13) — those are MILP-formulation artifacts, not original-problem rules. + +This patched version additionally enforces: + Constraint 2 (Eq. 2, objective consistency): the reported objective_value + must equal sum_j y_j == number of hyperplanes present in the solution + (since every hyperplane listed in `hyperplanes` has y_j = 1). Protects + against LLM score-gaming exploits that fabricate the objective. +""" + +import argparse +import json +import math +import numpy as np + +# FRONTIEROR_RELEASE_CONTRACT_V2 +_FRONTIEROR_REQUIRED_SOLUTION_FIELDS = ('hyperplanes', 'objective_value', 'point_assignments') +_FRONTIEROR_NONEMPTY_SOLUTION_FIELDS = ('point_assignments',) +_FRONTIEROR_DERIVED_FIELD_RULES = () +_FRONTIEROR_CONDITIONAL_FIELD_RULES = () + + +def _frontieror_validate_solution_contract(solution): + """Return a structured rejection when the public solution contract is absent.""" + violations = [] + if not isinstance(solution, dict): + violations.append("solution must be a JSON object") + else: + missing = [ + field + for field in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS + if field not in solution or solution[field] is None + ] + if missing: + violations.append( + "missing required solution field(s): " + ", ".join(missing) + ) + + for discriminator, expected_value, fields in _FRONTIEROR_CONDITIONAL_FIELD_RULES: + if solution.get(discriminator) != expected_value: + continue + conditional_missing = [ + field + for field in fields + if field not in solution or solution[field] is None + ] + if conditional_missing: + violations.append( + "missing conditionally required solution field(s): " + + ", ".join(conditional_missing) + ) + + empty = [ + field + for field in _FRONTIEROR_NONEMPTY_SOLUTION_FIELDS + if field in solution and solution[field] in (None, [], {}) + ] + if empty: + violations.append( + "empty required decision witness field(s): " + ", ".join(empty) + ) + + for target, operation, source, source_key in _FRONTIEROR_DERIVED_FIELD_RULES: + try: + reported = float(solution[target]) + if operation == "length": + expected = float(len(solution[source])) + elif operation == "mapping_value": + expected = float(solution[source][source_key]) + else: + raise ValueError("unknown derived-field operation") + except (KeyError, TypeError, ValueError, OverflowError): + violations.append( + f"cannot derive {target} from decision witness field {source}" + ) + continue + if ( + reported != reported + or expected != expected + or reported in (float("inf"), float("-inf")) + or expected in (float("inf"), float("-inf")) + or abs(reported - expected) > 1e-6 + ): + violations.append( + f"{target}={reported} does not match the decision-derived " + f"value {expected}" + ) + + if "objective_value" in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS: + objective = solution.get("objective_value") + if ( + isinstance(objective, bool) + or not isinstance(objective, (int, float)) + or objective != objective + or objective in (float("inf"), float("-inf")) + ): + violations.append("objective_value must be a finite JSON number") + + if not violations: + return None + return { + "feasible": False, + "violated_constraints": ["solution_schema"], + "violations": violations, + "violation_magnitudes": [], + } + + +def _frontieror_write_unexpected_rejection(error): + """Turn malformed candidate/checker exceptions into a normal rejection.""" + import json as _frontieror_json + import sys as _frontieror_sys + + result_path = None + for index, argument in enumerate(_frontieror_sys.argv): + if argument == "--result_path" and index + 1 < len(_frontieror_sys.argv): + result_path = _frontieror_sys.argv[index + 1] + break + if argument.startswith("--result_path="): + result_path = argument.split("=", 1)[1] + break + if not result_path: + return False + try: + with open(result_path, "w") as result_handle: + _frontieror_json.dump( + { + "feasible": False, + "violated_constraints": ["malformed_solution"], + "violations": [ + "checker rejected malformed candidate: " + + type(error).__name__ + + ": " + + str(error)[:500] + ], + "violation_magnitudes": [], + }, + result_handle, + indent=2, + ) + except Exception: + return False + return True + + + +def load_json(path): + with open(path, "r") as f: + return json.load(f) + + +def write_json(path, data): + with open(path, "w") as f: + json.dump(data, f, indent=2) + + +def check_feasibility(instance, solution): + tol = 1e-5 + eps = 1e-5 + + violations = [] + violation_magnitudes = [] + violated_constraints_set = set() + + # ---- Check if solution contains actual solution data ---- + if "hyperplanes" not in solution or solution.get("hyperplanes") is None: + return { + "feasible": None, + "violated_constraints": [], + "violations": ["No solution data in solution file"], + "violation_magnitudes": [] + } + + # ---- Extract instance data ---- + n = instance["n"] + d = instance["d"] + epsilon = instance["epsilon"] + points = np.array(instance["points"]) # n x d + K = math.ceil(n / d) # upper bound on number of hyperplanes + + # ---- Extract solution data ---- + hyperplanes = solution["hyperplanes"] + num_hyperplanes = len(hyperplanes) + + # Reconstruct D_ij and y_j from solution. + # j ranges over [0, num_hyperplanes-1] (the hyperplanes present in solution). + # The model uses K potential hyperplane slots; unused slots have y_j=0. + # We check constraints only for the hyperplanes in the solution (y_j=1) + # plus verify that any hyperplane slot beyond those has D_ij=0 (trivially true). + + # Build D matrix: D[i][j] = 1 if point i is assigned to hyperplane j + D = np.zeros((n, num_hyperplanes), dtype=int) + for j, hp in enumerate(hyperplanes): + for i in hp["assigned_points"]: + if 0 <= i < n: + D[i, j] = 1 + + # y_j = 1 for all hyperplanes in the solution + y = np.ones(num_hyperplanes, dtype=int) + + # ---------------------------------------------------------------- + # Constraint 1 (original geometric rule): for each assigned point i, + # Euclidean distance from point_i to its assigned hyperplane j must be + # <= epsilon. Distance = |a_i . w_j - w_j^0| / ||w_j||_2. + # This replaces the big-M formulation constraints (Eq. 11-12), which + # are MILP linearization artifacts, not original-problem rules. + # ---------------------------------------------------------------- + constraint_1_violated = False + for j, hp in enumerate(hyperplanes): + w = np.array(hp["w"]) + w0 = hp["w0"] + w_norm = float(np.linalg.norm(w)) + if w_norm < tol: + # Degenerate hyperplane (zero normal) — any assignment is invalid + if int(D[:, j].sum()) > 0: + if not constraint_1_violated: + constraint_1_violated = True + violated_constraints_set.add(1) + violation_magnitudes.append({ + "constraint": 1, + "lhs": float(w_norm), + "rhs": float(tol), + "raw_excess": float(tol - w_norm), + "normalizer": max(tol, eps), + "ratio": float((tol - w_norm) / max(tol, eps)) + }) + violations.append( + f"Constraint 1 violated: hyperplane {j} has zero normal vector " + f"but has assigned points" + ) + continue + for i in range(n): + if D[i, j] == 1: + dist = abs(np.dot(points[i], w) - w0) / w_norm + if dist > epsilon + tol: + violation_amount = dist - epsilon + normalizer = max(abs(epsilon), eps) + ratio = violation_amount / normalizer + if not constraint_1_violated: + constraint_1_violated = True + violated_constraints_set.add(1) + violation_magnitudes.append({ + "constraint": 1, + "lhs": float(dist), + "rhs": float(epsilon), + "raw_excess": float(violation_amount), + "normalizer": float(normalizer), + "ratio": float(ratio) + }) + + if constraint_1_violated: + count = sum(1 for vm in violation_magnitudes if vm["constraint"] == 1) + violations.append( + f"Constraint 1 (original distance rule) violated: {count} (point, " + f"hyperplane) pair(s) where distance > epsilon" + ) + + # ---------------------------------------------------------------- + # Constraint 2 (Eq. 2, objective consistency): the reported + # objective_value must equal sum_j y_j == len(hyperplanes), since + # every hyperplane listed in the solution has y_j = 1. + # This rejects exploits that fabricate the reported objective. + # ---------------------------------------------------------------- + reported_obj = solution.get("objective_value") + if reported_obj is not None: + try: + reported = float(reported_obj) + except (TypeError, ValueError): + reported = None + if reported is not None: + true_obj = float(num_hyperplanes) + abs_diff = abs(reported - true_obj) + # obj is an integer count of hyperplanes; mismatch of >= 1 fires + obj_tol = 0.5 + if abs_diff > obj_tol: + normalizer = max(abs(true_obj), eps, 1.0) + ratio = abs_diff / normalizer + violated_constraints_set.add(2) + violation_magnitudes.append({ + "constraint": 2, + "lhs": float(reported), + "rhs": float(true_obj), + "raw_excess": float(abs_diff), + "normalizer": float(normalizer), + "ratio": float(ratio) + }) + violations.append( + f"Constraint 2 (Eq.2, objective consistency) violated: " + f"reported objective_value={reported} differs from recomputed " + f"sum_j(y_j)=len(hyperplanes)={true_obj} " + f"(|diff|={abs_diff:.3g}, tol={obj_tol:.3g})" + ) + + # ---------------------------------------------------------------- + # Constraint 3 (Eq. 5): sum_j D_ij >= 1 for 1 <= i <= n + # Each point must be assigned to at least one hyperplane. + # ---------------------------------------------------------------- + constraint_3_violated = False + for i in range(n): + lhs_val = float(np.sum(D[i, :])) + rhs_val = 1.0 + violation_amount = rhs_val - lhs_val # >= constraint: RHS - LHS + if violation_amount > tol: + normalizer = max(abs(rhs_val), eps) + ratio = violation_amount / normalizer + if not constraint_3_violated: + constraint_3_violated = True + violated_constraints_set.add(3) + violation_magnitudes.append({ + "constraint": 3, + "lhs": float(lhs_val), + "rhs": float(rhs_val), + "raw_excess": float(violation_amount), + "normalizer": float(normalizer), + "ratio": float(ratio) + }) + + if constraint_3_violated: + uncovered = [i for i in range(n) if np.sum(D[i, :]) < 1] + violations.append( + f"Constraint 3 (Eq.5) violated: {len(uncovered)} point(s) not assigned " + f"to any hyperplane: {uncovered[:10]}{'...' if len(uncovered) > 10 else ''}" + ) + + # ---------------------------------------------------------------- + # Constraint 4 (Eq. 6): D_ij <= y_j for 1 <= i <= n, 1 <= j <= K + # A point can only be assigned to a hyperplane that is in use. + # Since all hyperplanes in the solution have y_j=1, this is automatically + # satisfied for all j in the solution. We still check explicitly. + # ---------------------------------------------------------------- + constraint_4_violated = False + for j in range(num_hyperplanes): + for i in range(n): + lhs_val = float(D[i, j]) + rhs_val = float(y[j]) + violation_amount = lhs_val - rhs_val # <= constraint: LHS - RHS + if violation_amount > tol: + normalizer = max(abs(rhs_val), eps) + ratio = violation_amount / normalizer + if not constraint_4_violated: + constraint_4_violated = True + violated_constraints_set.add(4) + violation_magnitudes.append({ + "constraint": 4, + "lhs": float(lhs_val), + "rhs": float(rhs_val), + "raw_excess": float(violation_amount), + "normalizer": float(normalizer), + "ratio": float(ratio) + }) + + if constraint_4_violated: + violations.append( + "Constraint 4 (Eq.6) violated: point(s) assigned to unused hyperplane(s)" + ) + + # ---------------------------------------------------------------- + # Constraint 5 (Eq. 7): w_j in R^d, w_j^0 in R for 1 <= j <= K + # Variable domain constraint - check that dimensions match. + # ---------------------------------------------------------------- + constraint_5_violated = False + for j, hp in enumerate(hyperplanes): + w = hp["w"] + w0 = hp["w0"] + lhs_val = float(len(w)) + rhs_val = float(d) + violation_amount = abs(lhs_val - rhs_val) + if violation_amount > tol: + normalizer = max(abs(rhs_val), eps) + ratio = violation_amount / normalizer + if not constraint_5_violated: + constraint_5_violated = True + violated_constraints_set.add(5) + violation_magnitudes.append({ + "constraint": 5, + "lhs": float(lhs_val), + "rhs": float(rhs_val), + "raw_excess": float(violation_amount), + "normalizer": float(normalizer), + "ratio": float(ratio) + }) + + if constraint_5_violated: + violations.append( + f"Constraint 5 (Eq.7) violated: hyperplane normal vector dimension " + f"does not match d={d}" + ) + + # ---------------------------------------------------------------- + # Constraint 6 (Eq. 8): D_ij in {0,1} for 1 <= i <= n, 1 <= j <= K + # Binary constraint on assignment variables. + # ---------------------------------------------------------------- + constraint_6_violated = False + for j in range(num_hyperplanes): + for i in range(n): + val = D[i, j] + if val not in (0, 1): + violation_amount = min(abs(val - 0), abs(val - 1)) + if violation_amount > tol: + normalizer = max(1.0, eps) + ratio = violation_amount / normalizer + if not constraint_6_violated: + constraint_6_violated = True + violated_constraints_set.add(6) + violation_magnitudes.append({ + "constraint": 6, + "lhs": float(val), + "rhs": float(round(val)), + "raw_excess": float(violation_amount), + "normalizer": float(normalizer), + "ratio": float(ratio) + }) + + if constraint_6_violated: + violations.append( + "Constraint 6 (Eq.8) violated: D_ij values are not binary" + ) + + # Check for invalid point indices in assigned_points + for j, hp in enumerate(hyperplanes): + invalid_pts = [i for i in hp["assigned_points"] if i < 0 or i >= n] + if invalid_pts: + if 6 not in violated_constraints_set: + violated_constraints_set.add(6) + violation_magnitudes.append({ + "constraint": 6, + "lhs": float(len(invalid_pts)), + "rhs": 0.0, + "raw_excess": float(len(invalid_pts)), + "normalizer": max(0.0, eps), + "ratio": float(len(invalid_pts)) / eps + }) + violations.append( + f"Constraint 6 (Eq.8) violated: hyperplane {j} has {len(invalid_pts)} " + f"invalid point indices out of range [0, {n-1}]" + ) + + # ---------------------------------------------------------------- + # Constraint 7 (Eq. 9): y_j in {0,1} for 1 <= j <= K + # Binary constraint on hyperplane usage variables. + # Since all hyperplanes in solution have y_j=1, check trivially satisfied. + # ---------------------------------------------------------------- + constraint_7_violated = False + for j in range(num_hyperplanes): + val = y[j] + if val not in (0, 1): + violation_amount = min(abs(val - 0), abs(val - 1)) + if violation_amount > tol: + normalizer = max(1.0, eps) + ratio = violation_amount / normalizer + if not constraint_7_violated: + constraint_7_violated = True + violated_constraints_set.add(7) + violation_magnitudes.append({ + "constraint": 7, + "lhs": float(val), + "rhs": float(round(val)), + "raw_excess": float(violation_amount), + "normalizer": float(normalizer), + "ratio": float(ratio) + }) + + if constraint_7_violated: + violations.append( + "Constraint 7 (Eq.9) violated: y_j values are not binary" + ) + + # ---------------------------------------------------------------- + # Constraint 8 SKIPPED: ||w_j||_2 = 1 (Eq. 13) is a formulation-specific + # normalization used to make the big-M linearization (Eqs. 11-12) exact. + # Since we use direct Euclidean distance (|a_i·w - w_0| / ||w||_2) for the + # containment check, w may have any non-zero norm; the distance is invariant + # to the scale of (w, w_0). Not checked. + # ---------------------------------------------------------------- + + # ---- Assemble result ---- + feasible = len(violated_constraints_set) == 0 + + result = { + "feasible": feasible, + "violated_constraints": sorted(violated_constraints_set), + "violations": violations, + "violation_magnitudes": violation_magnitudes + } + + return result + + +def main(): + parser = argparse.ArgumentParser( + description="Feasibility checker for Min-HCP (Amaldi et al. 2013)" + ) + parser.add_argument("--instance_path", type=str, required=True, + help="Path to the JSON file containing the data instance") + parser.add_argument("--solution_path", type=str, required=True, + help="Path to the JSON file containing the candidate solution") + parser.add_argument("--result_path", type=str, required=True, + help="Path to write the JSON file containing the feasibility result") + + args = parser.parse_args() + + instance = load_json(args.instance_path) + solution = load_json(args.solution_path) + _frontieror_contract_result = _frontieror_validate_solution_contract(solution) + if _frontieror_contract_result is not None: + with open(args.result_path, "w") as _frontieror_result_handle: + json.dump( + _frontieror_contract_result, + _frontieror_result_handle, + indent=2, + ) + return + + result = check_feasibility(instance, solution) + + write_json(args.result_path, result) + + if result["feasible"] is None: + print("NO SOLUTION - Cannot check feasibility.") + for v in result["violations"]: + print(f" - {v}") + elif result["feasible"]: + print("FEASIBLE - No constraint violations found.") + else: + print(f"INFEASIBLE - Violated constraints: {result['violated_constraints']}") + for v in result["violations"]: + print(f" - {v}") + + +if __name__ == "__main__": + try: + main() + except Exception as _frontieror_unexpected_error: + if not _frontieror_write_unexpected_rejection( + _frontieror_unexpected_error + ): + raise diff --git a/amaldi2013/gurobi_code.py b/amaldi2013/gurobi_code.py new file mode 100644 index 0000000000000000000000000000000000000000..0fb54dd1501d307295624c2ca0656270304dad98 --- /dev/null +++ b/amaldi2013/gurobi_code.py @@ -0,0 +1,231 @@ +""" +Gurobi implementation of the Minimum Hyperplanes Clustering Problem (Min-HCP). + +Based on: Amaldi, Dhyani, and Ceselli (2013), +"Column Generation for the Minimum Hyperplanes Clustering Problem", +INFORMS Journal on Computing. + +This implements the MINLP formulation (Eqs. 2-9) with the unit-norm +simplification (Eqs. 11-13), yielding constraints that are linear +except for the quadratic norm constraint ||w_j||_2 = 1. + +Gurobi handles the nonconvex quadratic constraint via NonConvex=2. +""" + +import argparse +import json +import math +import numpy as np +import gurobipy as gp +from gurobipy import GRB +import os as _os, sys as _sys +# Walk up from this file's directory to find repo root (containing scripts/). +_repo = _os.path.dirname(_os.path.abspath(__file__)) +while _repo != _os.path.dirname(_repo) and not _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _repo = _os.path.dirname(_repo) +if _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _sys.path.insert(0, _repo) +try: + from scripts.utils.gurobi_log_helper import install_gurobi_logger +except ImportError: + def install_gurobi_logger(log_path): # no-op fallback when scripts/ unavailable + pass +def load_instance(path): + """Load a Min-HCP instance from a JSON file.""" + with open(path, "r") as f: + data = json.load(f) + return data + + +def compute_big_M(points): + """Compute big-M as the maximum inter-point Euclidean distance (Eq. 10).""" + n = len(points) + max_dist = 0.0 + for i in range(n): + for j in range(i + 1, n): + dist = math.sqrt(sum((points[i][l] - points[j][l]) ** 2 for l in range(len(points[i])))) + if dist > max_dist: + max_dist = dist + return max_dist + + +def solve_min_hcp(instance_path, solution_path, time_limit): + """Solve Min-HCP using Gurobi with the MINLP formulation.""" + data = load_instance(instance_path) + + points = np.array(data["points"]) + n = data["n"] + d = data["d"] + epsilon = data["epsilon"] + + # Upper bound on number of hyperplanes: K = ceil(n/d) + K = math.ceil(n / d) + + # Big-M constant (Eq. 10): largest inter-point Euclidean distance + # Use precomputed value if available, otherwise compute + if "big_M" in data: + M = data["big_M"] + else: + M = compute_big_M(points.tolist()) + + print(f"Instance: n={n}, d={d}, epsilon={epsilon:.6f}, K={K}, M={M:.6f}") + + # Create model + model = gp.Model("Min-HCP") + model.setParam("Threads", 1) + model.setParam("TimeLimit", time_limit) + # Allow nonconvex quadratic constraints (for ||w_j||_2 = 1) + model.setParam("NonConvex", 2) + # Reduce output verbosity + model.setParam("OutputFlag", 1) + + # ---- Decision Variables ---- + # w[j,l]: normal vector component l of hyperplane j (Eq. 7) + w = {} + for j in range(K): + for l in range(d): + w[j, l] = model.addVar(lb=-GRB.INFINITY, ub=GRB.INFINITY, + vtype=GRB.CONTINUOUS, name=f"w_{j}_{l}") + + # w0[j]: offset of hyperplane j (Eq. 7) + w0 = {} + for j in range(K): + w0[j] = model.addVar(lb=-GRB.INFINITY, ub=GRB.INFINITY, + vtype=GRB.CONTINUOUS, name=f"w0_{j}") + + # D[i,j]: assignment of point i to hyperplane j (Eq. 8) + D = {} + for i in range(n): + for j in range(K): + D[i, j] = model.addVar(vtype=GRB.BINARY, name=f"D_{i}_{j}") + + # y[j]: whether hyperplane j is used (Eq. 9) + y = {} + for j in range(K): + y[j] = model.addVar(vtype=GRB.BINARY, name=f"y_{j}") + + model.update() + + # ---- Objective: minimize number of hyperplanes (Eq. 2) ---- + model.setObjective(gp.quicksum(y[j] for j in range(K)), GRB.MINIMIZE) + + # ---- Constraints ---- + + # Constraint (11): -(a_i * w_j - w_j^0) <= epsilon + M*(1 - D_ij) + # Equivalent to: w_j^0 - a_i * w_j <= epsilon + M*(1 - D_ij) + for i in range(n): + for j in range(K): + lhs = w0[j] - gp.quicksum(points[i, l] * w[j, l] for l in range(d)) + model.addConstr(lhs <= epsilon + M * (1 - D[i, j]), + name=f"dist_neg_{i}_{j}") + + # Constraint (12): (a_i * w_j - w_j^0) <= epsilon + M*(1 - D_ij) + for i in range(n): + for j in range(K): + lhs = gp.quicksum(points[i, l] * w[j, l] for l in range(d)) - w0[j] + model.addConstr(lhs <= epsilon + M * (1 - D[i, j]), + name=f"dist_pos_{i}_{j}") + + # Constraint (5): each point assigned to at least one hyperplane + for i in range(n): + model.addConstr(gp.quicksum(D[i, j] for j in range(K)) >= 1, + name=f"cover_{i}") + + # Constraint (6): D_ij <= y_j + for i in range(n): + for j in range(K): + model.addConstr(D[i, j] <= y[j], name=f"link_{i}_{j}") + + # Constraint (13): ||w_j||_2 = 1 (unit norm) + # This is a nonconvex quadratic constraint: sum_l w[j,l]^2 = 1 + for j in range(K): + model.addConstr( + gp.quicksum(w[j, l] * w[j, l] for l in range(d)) == 1.0, + name=f"norm_{j}" + ) + + # ---- Symmetry breaking: order hyperplanes by index of first assigned point ---- + # (helps solver performance) + for j in range(K - 1): + model.addConstr(y[j] >= y[j + 1], name=f"symbreak_{j}") + + print(f"Model has {model.NumVars} variables, solving...") + + # ---- Solve ---- + model.optimize() + + # ---- Extract solution ---- + result = { + "problem": "Min-HCP", + "instance": instance_path, + "solver": "Gurobi", + "status": model.Status, + } + + if model.SolCount > 0: + obj_val = model.ObjVal + result["objective_value"] = obj_val + + # Extract hyperplane parameters and assignments + hyperplanes = [] + assignments = [[] for _ in range(n)] + + for j in range(K): + if y[j].X > 0.5: + wj = [w[j, l].X for l in range(d)] + w0j = w0[j].X + assigned_points = [i for i in range(n) if D[i, j].X > 0.5] + hyperplanes.append({ + "w": wj, + "w0": w0j, + "assigned_points": assigned_points + }) + for i in assigned_points: + assignments[i].append(len(hyperplanes) - 1) + + result["num_hyperplanes"] = len(hyperplanes) + result["hyperplanes"] = hyperplanes + result["point_assignments"] = assignments + + print(f"\nSolution found: {len(hyperplanes)} hyperplanes (objective = {obj_val})") + print(f"Solver status: {model.Status}") + if model.Status == GRB.TIME_LIMIT: + print("(Time limit reached - returning best feasible solution)") + if hasattr(model, 'MIPGap'): + try: + print(f"MIP Gap: {model.MIPGap * 100:.2f}%") + except Exception: + pass + else: + result["objective_value"] = None + result["num_hyperplanes"] = None + print("No feasible solution found.") + + # Write solution + with open(solution_path, "w") as f: + json.dump(result, f, indent=2) + + print(f"Solution written to {solution_path}") + return result + + +def main(): + parser = argparse.ArgumentParser( + description="Solve Min-HCP using Gurobi (MINLP with unit-norm constraint)" + ) + parser.add_argument("--instance_path", type=str, required=True, + help="Path to the JSON instance file") + parser.add_argument("--solution_path", type=str, required=True, + help="Path for the output solution JSON file") + parser.add_argument("--time_limit", type=int, required=True, + help="Maximum solver runtime in seconds") + + parser.add_argument("--log_path", type=str, default=None, help="Path to log incumbent solutions") + args = parser.parse_args() + install_gurobi_logger(args.log_path) + + solve_min_hcp(args.instance_path, args.solution_path, args.time_limit) + + +if __name__ == "__main__": + main() diff --git a/amaldi2013/gurobi_feasi_result/large_feasi_result_1.json b/amaldi2013/gurobi_feasi_result/large_feasi_result_1.json new file mode 100644 index 0000000000000000000000000000000000000000..e305b82c3d6732211d5033415df1a105476e9cf1 --- /dev/null +++ b/amaldi2013/gurobi_feasi_result/large_feasi_result_1.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3269392edcbb7720882e77caadb88b4a9cf2fda04cf482cebd23baace4a531fd +size 102 diff --git a/amaldi2013/gurobi_feasi_result/large_feasi_result_2.json b/amaldi2013/gurobi_feasi_result/large_feasi_result_2.json new file mode 100644 index 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/dev/null +++ b/amaldi2013/instance/tiny_instance.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:eeda52a04c6fef67e28c25ad06b6a1714e47de5a8beb2fe579db5b7c39f1691e +size 8528 diff --git a/amaldi2013/instance_schema.json b/amaldi2013/instance_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..56bbddb25f33f05125abbce84ab7c004bff7abe0 --- /dev/null +++ b/amaldi2013/instance_schema.json @@ -0,0 +1,8 @@ +{ + "n": " Number of points to be clustered.", + "d": " Number of coordinate dimensions for each point.", + "epsilon": " Maximum allowed Euclidean distance from any point to its assigned hyperplane.", + "big_M": " Largest pairwise Euclidean distance among all points, used to deactivate distance constraints for unassigned point-hyperplane pairs.", + "upper_bound_K": " Maximum number of candidate hyperplanes available for covering the points.", + "points": " Coordinates of each point in d-dimensional space." +} \ No newline at end of file diff --git a/amaldi2013/mathematical_formulation.md b/amaldi2013/mathematical_formulation.md new file mode 100644 index 0000000000000000000000000000000000000000..5e8666db179c57993ffcbf10fe0ced628abb2178 --- /dev/null +++ b/amaldi2013/mathematical_formulation.md @@ -0,0 +1,61 @@ +# Original Formulation: Minimum Hyperplanes Clustering Problem (Min-HCP) + +*Source: Column Generation for the Minimum Hyperplanes Clustering Problem, Edoardo Amaldi, Kanika Dhyani, and Alberto Ceselli, 2013 (INFORMS Journal on Computing, 25(3), pp. 446–460).* + +## Sets and Indices + +- $i \in \{1,\dots,n\}$ : index of the points. + +- $j \in \{1,\dots,K\}$ : index of the potential hyperplanes. + +- $d$ : dimension of the ambient Euclidean space $\mathbb{R}^d$. + +- $K = \lceil n/d \rceil$ : upper bound on the optimal number $k$ of hyperplanes (any $d$ points in $d$ dimensions can be fitted with a hyperplane). + +## Parameters + +- $\mathbf{a}_i \in \mathbb{R}^d$ : coordinates of the $i$-th point, $1 \le i \le n$. + +- $\epsilon > 0$ : maximum allowed tolerance (Euclidean point-to-hyperplane distance). + +- $M > 0$ : large enough constant, taken as the largest inter-point Euclidean distance: $$\begin{equation*} + M = \max_{\substack{1 \le i_1,\, i_2 \le n \\ i_1 \neq i_2}} \sqrt{\sum_{l=1}^{d} (a_{i_1 l} - a_{i_2 l})^2}. \tag{10} + \end{equation*}$$ + +## Decision Variables + +- $\mathbf{w}_j \in \mathbb{R}^d$, $w_j^0 \in \mathbb{R}$ : parameters (normal vector and offset) of hyperplane $\mathscr{H}_j = \{\mathbf{p} \in \mathbb{R}^d \mid \mathbf{p}\,\mathbf{w}_j = w_j^0\}$, $1 \le j \le K$. + +- $D_{ij} \in \{0,1\}$ : $=1$ if point $i$ is assigned to the $j$-th $\epsilon$-$h$-cluster. + +- $y_j \in \{0,1\}$ : $=1$ if the $\epsilon$-$h$-cluster (hyperplane) $j$ appears in the solution. + +## Objective + +$$\begin{equation} + \min \; \sum_{j=1}^{K} y_j \tag{2} +\end{equation}$$ + +## Constraints + +$$\begin{align} + \frac{-(\mathbf{a}_i \mathbf{w}_j - w_j^0)}{\|\mathbf{w}_j\|_2} &\le \epsilon + M(1 - D_{ij}), + & 1 \le i \le n,\; 1 \le j \le K, \tag{3} \\[4pt] + \frac{(\mathbf{a}_i \mathbf{w}_j - w_j^0)}{\|\mathbf{w}_j\|_2} &\le \epsilon + M(1 - D_{ij}), + & 1 \le i \le n,\; 1 \le j \le K, \tag{4} \\[4pt] + \sum_{j=1}^{K} D_{ij} &\ge 1, & 1 \le i \le n, \tag{5} \\[4pt] + D_{ij} &\le y_j, & 1 \le i \le n,\; 1 \le j \le K, \tag{6} \\[4pt] + \mathbf{w}_j \in \mathbb{R}^d,\; w_j^0 &\in \mathbb{R}, & 1 \le j \le K, \tag{7} \\[4pt] + D_{ij} &\in \{0,1\}, & 1 \le i \le n,\; 1 \le j \le K, \tag{8} \\[4pt] + y_j &\in \{0,1\}, & 1 \le j \le K. \tag{9} +\end{align}$$ + +## Linearized Distance Constraints (same formulation section) + +$$\begin{align} + -(\mathbf{a}_i \mathbf{w}_j - w_j^0) &\le \epsilon + M(1 - D_{ij}), + & 1 \le i \le n,\; 1 \le j \le K, \tag{11} \\[4pt] + (\mathbf{a}_i \mathbf{w}_j - w_j^0) &\le \epsilon + M(1 - D_{ij}), + & 1 \le i \le n,\; 1 \le j \le K, \tag{12} \\[4pt] + \|\mathbf{w}_j\|_2 &= 1, & 1 \le j \le K. \tag{13} +\end{align}$$ diff --git a/amaldi2013/problem_description.txt b/amaldi2013/problem_description.txt new file mode 100644 index 0000000000000000000000000000000000000000..8070758b0c7a22ab58e391d7d9f3eb8943a2b6e6 --- /dev/null +++ b/amaldi2013/problem_description.txt @@ -0,0 +1,7 @@ +# Problem Description + +Given a set of n points in d-dimensional Euclidean space, where each point is specified by its d real-valued coordinates, and a maximum allowed tolerance epsilon, the task is to cover the points using as few hyperplanes as possible. A hyperplane in d-dimensional space is any flat (d-1)-dimensional affine subspace, and the Euclidean distance from a point to a hyperplane is the standard shortest distance from that point to any point on the hyperplane. + +Every point must be covered by at least one hyperplane, meaning its Euclidean distance to that hyperplane is at most epsilon. Each point may be covered by any number of hyperplanes, but must be covered by at least one. Hyperplanes are to be chosen freely in the ambient space (their positions and orientations are decisions of the problem). + +The goal is to determine the minimum number of hyperplanes, together with their positions and orientations, such that every one of the n points lies within Euclidean distance epsilon of at least one chosen hyperplane. diff --git a/amaldi2013/solution_logger.py b/amaldi2013/solution_logger.py new file mode 100644 index 0000000000000000000000000000000000000000..5393fa935ce71df9da95cee04fe5199077cb8461 --- /dev/null +++ b/amaldi2013/solution_logger.py @@ -0,0 +1,61 @@ +""" +Convergence logger for optimization algorithms. + +Records incumbent solutions with timestamps to a JSONL file. +This module is provided to LLM-generated programs — they only need to call +`log(objective_value)` whenever a better feasible solution is found. + +Usage in generated code: + from solution_logger import SolutionLogger + logger = SolutionLogger(log_path, sense="minimize") # or "maximize" + # ... inside algorithm loop: + logger.log(objective_value) +""" + +import json +import time + + +class SolutionLogger: + def __init__(self, log_path, sense="minimize"): + """ + Args: + log_path: Path to the JSONL output file. + sense: "minimize" or "maximize". + """ + self.log_path = log_path + self.sense = sense + self.start_time = time.time() + self.best_obj = None + self.min_interval = 0.1 # seconds, avoid excessive writes + + self._last_log_time = 0.0 + # Clear the file + with open(self.log_path, "w") as f: + pass + + def log(self, objective_value): + """Record a new incumbent if it improves on the best known.""" + if objective_value is None: + return + + # Check if this is an improvement + if self.best_obj is not None: + if self.sense == "minimize" and objective_value >= self.best_obj: + return + if self.sense == "maximize" and objective_value <= self.best_obj: + return + + elapsed = time.time() - self.start_time + + # Throttle writes + if self.best_obj is not None and elapsed - self._last_log_time < self.min_interval: + self.best_obj = objective_value + return + + self.best_obj = objective_value + self._last_log_time = elapsed + + with open(self.log_path, "a") as f: + f.write(json.dumps({"time": round(elapsed, 3), + "objective_value": objective_value}) + "\n") diff --git a/amaldi2013/solution_schema.json b/amaldi2013/solution_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..84b8c62f37aa3087c2e4360b3a1b1d5bb56e3836 --- /dev/null +++ b/amaldi2013/solution_schema.json @@ -0,0 +1,11 @@ +{ + "objective_value": " Total number of hyperplanes used to cover all points.", + "hyperplanes": [ + { + "w": " Unit-norm normal vector defining the orientation of this hyperplane.", + "w0": " Scalar offset defining the position of this hyperplane.", + "assigned_points": " Indices of points assigned to this hyperplane." + } + ], + "point_assignments": " For each point, the list of hyperplane indices (into the hyperplanes array) to which it is assigned." +} \ No newline at end of file diff --git a/araujo2015/feasibility_check.py b/araujo2015/feasibility_check.py new file mode 100644 index 0000000000000000000000000000000000000000..43d41d01fc5ba4939351ea2b59071d48a783cc8c --- /dev/null +++ b/araujo2015/feasibility_check.py @@ -0,0 +1,508 @@ +""" +Feasibility checker for the Capacitated Lot Sizing Problem with Setup Times (CLST). +Regular formulation (CL) from de Araujo et al. (2015). + +Checks constraints (2)-(5) from the mathematical formulation, plus a +Tier C objective-consistency check (constraint 6) that recomputes the +reported objective_value from the solution variables. +""" + +import argparse +import json +import math + +# FRONTIEROR_RELEASE_CONTRACT_V2 +_FRONTIEROR_REQUIRED_SOLUTION_FIELDS = ('inventory', 'objective_value', 'production', 'setup') +_FRONTIEROR_NONEMPTY_SOLUTION_FIELDS = () +_FRONTIEROR_DERIVED_FIELD_RULES = () +_FRONTIEROR_CONDITIONAL_FIELD_RULES = () + + +def _frontieror_validate_solution_contract(solution): + """Return a structured rejection when the public solution contract is absent.""" + violations = [] + if not isinstance(solution, dict): + violations.append("solution must be a JSON object") + else: + missing = [ + field + for field in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS + if field not in solution or solution[field] is None + ] + if missing: + violations.append( + "missing required solution field(s): " + ", ".join(missing) + ) + + for discriminator, expected_value, fields in _FRONTIEROR_CONDITIONAL_FIELD_RULES: + if solution.get(discriminator) != expected_value: + continue + conditional_missing = [ + field + for field in fields + if field not in solution or solution[field] is None + ] + if conditional_missing: + violations.append( + "missing conditionally required solution field(s): " + + ", ".join(conditional_missing) + ) + + empty = [ + field + for field in _FRONTIEROR_NONEMPTY_SOLUTION_FIELDS + if field in solution and solution[field] in (None, [], {}) + ] + if empty: + violations.append( + "empty required decision witness field(s): " + ", ".join(empty) + ) + + for target, operation, source, source_key in _FRONTIEROR_DERIVED_FIELD_RULES: + try: + reported = float(solution[target]) + if operation == "length": + expected = float(len(solution[source])) + elif operation == "mapping_value": + expected = float(solution[source][source_key]) + else: + raise ValueError("unknown derived-field operation") + except (KeyError, TypeError, ValueError, OverflowError): + violations.append( + f"cannot derive {target} from decision witness field {source}" + ) + continue + if ( + reported != reported + or expected != expected + or reported in (float("inf"), float("-inf")) + or expected in (float("inf"), float("-inf")) + or abs(reported - expected) > 1e-6 + ): + violations.append( + f"{target}={reported} does not match the decision-derived " + f"value {expected}" + ) + + if "objective_value" in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS: + objective = solution.get("objective_value") + if ( + isinstance(objective, bool) + or not isinstance(objective, (int, float)) + or objective != objective + or objective in (float("inf"), float("-inf")) + ): + violations.append("objective_value must be a finite JSON number") + + if not violations: + return None + return { + "feasible": False, + "violated_constraints": ["solution_schema"], + "violations": violations, + "violation_magnitudes": [], + } + + +def _frontieror_write_unexpected_rejection(error): + """Turn malformed candidate/checker exceptions into a normal rejection.""" + import json as _frontieror_json + import sys as _frontieror_sys + + result_path = None + for index, argument in enumerate(_frontieror_sys.argv): + if argument == "--result_path" and index + 1 < len(_frontieror_sys.argv): + result_path = _frontieror_sys.argv[index + 1] + break + if argument.startswith("--result_path="): + result_path = argument.split("=", 1)[1] + break + if not result_path: + return False + try: + with open(result_path, "w") as result_handle: + _frontieror_json.dump( + { + "feasible": False, + "violated_constraints": ["malformed_solution"], + "violations": [ + "checker rejected malformed candidate: " + + type(error).__name__ + + ": " + + str(error)[:500] + ], + "violation_magnitudes": [], + }, + result_handle, + indent=2, + ) + except Exception: + return False + return True + + + +def load_json(path): + with open(path, "r") as f: + return json.load(f) + + +def expand_param(raw, num_items, num_periods): + """Expand scalar-per-item to per-item-per-period if needed.""" + if isinstance(raw[0], list): + return raw + return [[raw[i]] * num_periods for i in range(num_items)] + + +def check_feasibility(instance, solution): + # 1e-5 was too tight for Gurobi LP numerical noise: when binary y=0 the + # constraint x <= UB*y = 0 was tripped by tiny x values (~3e-5). LP + # tolerance is typically 1e-6..1e-5, so accept up to 1e-4 absolute slack. + tol = 1e-4 + eps = 1e-5 + + nI = instance["num_items"] + nT = instance["num_periods"] + + d = instance["demand"] + st = expand_param(instance["setup_time"], nI, nT) + vt = expand_param(instance["variable_time"], nI, nT) + cap = instance["capacity"] + + # Compute sd[i][t] = sum of demand from period t to end + sd = [[0] * nT for _ in range(nI)] + for i in range(nI): + sd[i][nT - 1] = d[i][nT - 1] + for t in range(nT - 2, -1, -1): + sd[i][t] = sd[i][t + 1] + d[i][t] + + # Extract solution variables + x = solution["production"] # x[i][t], 0-indexed + y = solution["setup"] # y[i][t], 0-indexed + + # inventory[i] has nT+1 entries: [s_{i0}, s_{i,end of period 0}, ..., s_{i,end of period nT-1}] + inv = solution["inventory"] + + # Some solutions store initial_inventory separately + if "initial_inventory" in solution: + s0 = solution["initial_inventory"] + else: + s0 = [inv[i][0] for i in range(nI)] + + # Build full inventory array: s[i][0] = s_{i0}, s[i][t+1] = end-of-period t + s = [] + for i in range(nI): + if len(inv[i]) == nT + 1: + s.append(inv[i]) + else: + # If inventory has nT entries starting from end-of-period 0 + s.append([s0[i]] + inv[i]) + + violated_constraints = set() + violations = [] + violation_magnitudes = [] + + # ========================================================================= + # Constraint (2): Demand balance + # s_{i,t-1} + x_{it} = d_{it} + s_{it} for all i in I, t in T + # + # In 0-indexed code: s[i][t] + x[i][t] = d[i][t] + s[i][t+1] + # where s[i][0] = s_{i0} and s[i][t+1] = end-of-period t inventory + # ========================================================================= + for i in range(nI): + for t in range(nT): + lhs = s[i][t] + x[i][t] + rhs = d[i][t] + s[i][t + 1] + violation_amount = abs(lhs - rhs) + if violation_amount > tol: + normalizer = max(abs(rhs), eps) + ratio = violation_amount / normalizer + violated_constraints.add(2) + violations.append( + f"Demand balance violated for item {i}, period {t}: " + f"s_prev({s[i][t]}) + x({x[i][t]}) = {lhs} != d({d[i][t]}) + s({s[i][t+1]}) = {rhs}" + ) + violation_magnitudes.append({ + "constraint": 2, + "lhs": lhs, + "rhs": rhs, + "raw_excess": violation_amount, + "normalizer": normalizer, + "ratio": ratio, + }) + + # ========================================================================= + # Constraint (3): Capacity + # sum_i (st_{it} * y_{it} + vt_{it} * x_{it}) <= cap_t for all t in T + # ========================================================================= + for t in range(nT): + lhs = sum(st[i][t] * y[i][t] + vt[i][t] * x[i][t] for i in range(nI)) + rhs = cap[t] + violation_amount = lhs - rhs # <= constraint: violation if lhs > rhs + if violation_amount > tol: + normalizer = max(abs(rhs), eps) + ratio = violation_amount / normalizer + violated_constraints.add(3) + violations.append( + f"Capacity exceeded in period {t}: usage={lhs:.4f} > capacity={rhs}" + ) + violation_magnitudes.append({ + "constraint": 3, + "lhs": lhs, + "rhs": rhs, + "raw_excess": violation_amount, + "normalizer": normalizer, + "ratio": ratio, + }) + + # ========================================================================= + # Constraint (4): Setup forcing / variable upper bound + # x_{it} <= min((cap_t - st_{it}) / vt_{it}, sd_{it|T|}) * y_{it} + # for all i in I, t in T + # ========================================================================= + for i in range(nI): + for t in range(nT): + if vt[i][t] > 0: + ub_cap = (cap[t] - st[i][t]) / vt[i][t] + else: + ub_cap = float("inf") + ub_val = max(min(ub_cap, sd[i][t]), 0) * y[i][t] + lhs = x[i][t] + rhs = ub_val + violation_amount = lhs - rhs # <= constraint + if violation_amount > tol: + normalizer = max(abs(rhs), eps) + ratio = violation_amount / normalizer + violated_constraints.add(4) + violations.append( + f"Setup forcing violated for item {i}, period {t}: " + f"x={lhs:.4f} > UB*y={rhs:.4f} (y={y[i][t]})" + ) + violation_magnitudes.append({ + "constraint": 4, + "lhs": lhs, + "rhs": rhs, + "raw_excess": violation_amount, + "normalizer": normalizer, + "ratio": ratio, + }) + + # ========================================================================= + # Constraint (5): Variable domains and nonnegativity + # y_{it} in {0,1} + # x_{it} >= 0 + # s_{it} >= 0 for all t + # s_{i0} >= 0 + # s_{i|T|} = 0 (ending inventory is zero) + # ========================================================================= + + # (5a) y_{it} in {0,1} + for i in range(nI): + for t in range(nT): + val = y[i][t] + if val not in (0, 1) and abs(val - round(val)) > tol: + lhs = abs(val - round(val)) + rhs = 0.0 + violation_amount = lhs + normalizer = max(abs(rhs), eps) + ratio = violation_amount / normalizer + violated_constraints.add(5) + violations.append( + f"Binary constraint violated for y[{i}][{t}]: value={val}" + ) + violation_magnitudes.append({ + "constraint": 5, + "lhs": lhs, + "rhs": rhs, + "raw_excess": violation_amount, + "normalizer": normalizer, + "ratio": ratio, + }) + + # (5b) x_{it} >= 0 + for i in range(nI): + for t in range(nT): + if x[i][t] < -tol: + lhs = 0.0 # RHS of >= constraint is 0 + rhs_val = 0.0 + violation_amount = -x[i][t] # how much below 0 + normalizer = max(abs(rhs_val), eps) + ratio = violation_amount / normalizer + violated_constraints.add(5) + violations.append( + f"Non-negativity violated for x[{i}][{t}]: value={x[i][t]:.6f}" + ) + violation_magnitudes.append({ + "constraint": 5, + "lhs": x[i][t], + "rhs": rhs_val, + "raw_excess": violation_amount, + "normalizer": normalizer, + "ratio": ratio, + }) + + # (5c) s_{it} >= 0 for all t (including s_{i0}) + for i in range(nI): + for t in range(nT + 1): + if s[i][t] < -tol: + violation_amount = -s[i][t] + rhs_val = 0.0 + normalizer = max(abs(rhs_val), eps) + ratio = violation_amount / normalizer + violated_constraints.add(5) + violations.append( + f"Non-negativity violated for s[{i}][{t}]: value={s[i][t]:.6f}" + ) + violation_magnitudes.append({ + "constraint": 5, + "lhs": s[i][t], + "rhs": rhs_val, + "raw_excess": violation_amount, + "normalizer": normalizer, + "ratio": ratio, + }) + + # (5d) s_{i|T|} = 0 (ending inventory must be zero) + for i in range(nI): + ending_inv = s[i][nT] + violation_amount = abs(ending_inv) + if violation_amount > tol: + rhs_val = 0.0 + normalizer = max(abs(rhs_val), eps) + ratio = violation_amount / normalizer + violated_constraints.add(5) + violations.append( + f"Ending inventory not zero for item {i}: s[{i}][{nT}]={ending_inv:.6f}" + ) + violation_magnitudes.append({ + "constraint": 5, + "lhs": ending_inv, + "rhs": rhs_val, + "raw_excess": violation_amount, + "normalizer": normalizer, + "ratio": ratio, + }) + + # ========================================================================= + # Constraint (6): Objective consistency (Tier C anti-exploit check) + # reported objective_value must equal the recomputed sum + # sum_i fc_i * s_{i0} + # + sum_i sum_t (sc_{it} * y_{it} + vc_{it} * x_{it} + hc_{it} * s_{it}) + # from the solution variables, within a small tolerance. + # + # All variables that determine the objective (x, y, inventory including + # s_{i0}) are present in the solution schema, so a full recompute applies. + # ========================================================================= + reported_obj = solution.get("objective_value") + if reported_obj is not None: + try: + reported = float(reported_obj) + except (TypeError, ValueError): + reported = None + if reported is not None and math.isfinite(reported): + sc = expand_param(instance["setup_cost"], nI, nT) + vc = expand_param(instance["variable_cost"], nI, nT) + hc = expand_param(instance["holding_cost"], nI, nT) + fc = instance["initial_inventory_cost"] + + true_obj = 0.0 + for i in range(nI): + true_obj += float(fc[i]) * float(s[i][0]) + for t in range(nT): + true_obj += ( + float(sc[i][t]) * float(y[i][t]) + + float(vc[i][t]) * float(x[i][t]) + + float(hc[i][t]) * float(s[i][t + 1]) + ) + + abs_diff = abs(reported - true_obj) + # 0.1% relative tolerance with a generous 1e-3 absolute floor. + obj_tol = max(1e-3, 1e-3 * abs(true_obj)) + if abs_diff > obj_tol: + normalizer = max(abs(true_obj), eps) + ratio = abs_diff / normalizer + violated_constraints.add(6) + violations.append( + f"Objective consistency violated: reported objective_value=" + f"{reported} differs from recomputed " + f"sum_i fc_i*s_i0 + sum_{{i,t}}(sc*y + vc*x + hc*s)=" + f"{true_obj} (|diff|={abs_diff:.3g}, tol={obj_tol:.3g})" + ) + violation_magnitudes.append({ + "constraint": 6, + "lhs": reported, + "rhs": true_obj, + "raw_excess": abs_diff, + "normalizer": normalizer, + "ratio": ratio, + }) + elif reported is not None: + # Non-finite reported value (inf/nan) — flag as obj inconsistency. + violated_constraints.add(6) + violations.append( + f"Objective consistency violated: reported objective_value=" + f"{reported_obj} is not finite" + ) + violation_magnitudes.append({ + "constraint": 6, + "lhs": reported, + "rhs": 0.0, + "raw_excess": float("inf"), + "normalizer": eps, + "ratio": float("inf"), + }) + + feasible = len(violated_constraints) == 0 + return { + "feasible": feasible, + "violated_constraints": sorted(violated_constraints), + "violations": violations, + "violation_magnitudes": violation_magnitudes, + } + + +def main(): + parser = argparse.ArgumentParser( + description="Feasibility checker for CLST (de Araujo et al. 2015)" + ) + parser.add_argument("--instance_path", type=str, required=True, + help="Path to the JSON instance file") + parser.add_argument("--solution_path", type=str, required=True, + help="Path to the JSON solution file") + parser.add_argument("--result_path", type=str, required=True, + help="Path to write the JSON feasibility result") + args = parser.parse_args() + + instance = load_json(args.instance_path) + solution = load_json(args.solution_path) + _frontieror_contract_result = _frontieror_validate_solution_contract(solution) + if _frontieror_contract_result is not None: + with open(args.result_path, "w") as _frontieror_result_handle: + json.dump( + _frontieror_contract_result, + _frontieror_result_handle, + indent=2, + ) + return + + result = check_feasibility(instance, solution) + + with open(args.result_path, "w") as f: + json.dump(result, f, indent=2) + + if result["feasible"]: + print("Solution is FEASIBLE.") + else: + print(f"Solution is INFEASIBLE. Violated constraints: {result['violated_constraints']}") + for v in result["violations"]: + print(f" - {v}") + + +if __name__ == "__main__": + try: + main() + except Exception as _frontieror_unexpected_error: + if not _frontieror_write_unexpected_rejection( + _frontieror_unexpected_error + ): + raise diff --git a/araujo2015/gurobi_code.py b/araujo2015/gurobi_code.py new file mode 100644 index 0000000000000000000000000000000000000000..ea18cf4ccc736d550612e42dc6e891cb166662fa --- /dev/null +++ b/araujo2015/gurobi_code.py @@ -0,0 +1,209 @@ +""" +Gurobi implementation of the Capacitated Lot Sizing Problem with Setup Times (CLST). +Regular formulation (CL) from de Araujo et al. (2015), +"Period Decompositions for the Capacitated Lot Sizing Problem with Setup Times", +INFORMS Journal on Computing 27(3):431-448. + +Formulation (CL): equations (1)-(5) from the paper. +""" + +import argparse +import json +import gurobipy as gp +from gurobipy import GRB +import os as _os, sys as _sys +# Walk up from this file's directory to find repo root (containing scripts/). +_repo = _os.path.dirname(_os.path.abspath(__file__)) +while _repo != _os.path.dirname(_repo) and not _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _repo = _os.path.dirname(_repo) +if _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _sys.path.insert(0, _repo) +try: + from scripts.utils.gurobi_log_helper import install_gurobi_logger +except ImportError: + def install_gurobi_logger(log_path): # no-op fallback when scripts/ unavailable + pass +def load_instance(path): + with open(path, 'r') as f: + data = json.load(f) + return data + + +def solve_clst(instance, time_limit): + num_items = instance["num_items"] + num_periods = instance["num_periods"] + I = range(num_items) + T = range(num_periods) # 0-indexed: periods 0..num_periods-1 + + d = instance["demand"] # d[i][t] + + # Costs: may be per-item (time-invariant) or per-item-per-period + # The paper notes data sets have time-invariant costs. + # Instance JSON has scalar per-item costs; expand to per-item-per-period. + sc_raw = instance["setup_cost"] + hc_raw = instance["holding_cost"] + vc_raw = instance["variable_cost"] + st_raw = instance["setup_time"] + vt_raw = instance["variable_time"] + cap_raw = instance["capacity"] + fc_raw = instance["initial_inventory_cost"] + + # Build per-item-per-period parameters + # setup_cost, holding_cost, variable_cost may be scalar per item or list per item per period + def expand_param(raw, num_items, num_periods): + """If raw is a flat list of length num_items, expand to [i][t]. Otherwise assume already [i][t].""" + if isinstance(raw[0], list): + return raw + return [[raw[i]] * num_periods for i in range(num_items)] + + sc = expand_param(sc_raw, num_items, num_periods) + hc = expand_param(hc_raw, num_items, num_periods) + vc = expand_param(vc_raw, num_items, num_periods) + st = expand_param(st_raw, num_items, num_periods) + vt = expand_param(vt_raw, num_items, num_periods) + + # capacity may be a single list of length num_periods + cap = cap_raw + + fc = fc_raw # per-item initial inventory cost + + # Compute sd_{i,t,|T|} = sum of demand from period t to end + # sd[i][t] = sum(d[i][k] for k in range(t, num_periods)) + sd = [[0] * num_periods for _ in I] + for i in I: + sd[i][num_periods - 1] = d[i][num_periods - 1] + for t in range(num_periods - 2, -1, -1): + sd[i][t] = sd[i][t + 1] + d[i][t] + + # Create model + model = gp.Model("CLST_CL") + model.setParam("Threads", 1) + model.setParam("TimeLimit", time_limit) + model.setParam("OutputFlag", 1) + + # Decision variables + # x[i,t]: production quantity + x = {} + for i in I: + for t in T: + x[i, t] = model.addVar(lb=0, vtype=GRB.CONTINUOUS, name=f"x_{i}_{t}") + + # y[i,t]: setup binary + y = {} + for i in I: + for t in T: + y[i, t] = model.addVar(vtype=GRB.BINARY, name=f"y_{i}_{t}") + + # s[i,t]: inventory at end of period t, t=0..num_periods-1 + # s[i,-1] = s_{i0}: initial inventory + s = {} + for i in I: + s[i, -1] = model.addVar(lb=0, vtype=GRB.CONTINUOUS, name=f"s_{i}_0") + for t in T: + s[i, t] = model.addVar(lb=0, vtype=GRB.CONTINUOUS, name=f"s_{i}_{t+1}") + + model.update() + + # Objective (1): min sum_i fc_i * s_{i0} + sum_i sum_t (sc_{it}*y_{it} + vc_{it}*x_{it} + hc_{it}*s_{it}) + obj = gp.LinExpr() + for i in I: + obj += fc[i] * s[i, -1] + for t in T: + obj += sc[i][t] * y[i, t] + vc[i][t] * x[i, t] + hc[i][t] * s[i, t] + model.setObjective(obj, GRB.MINIMIZE) + + # Constraint (2): s_{i,t-1} + x_{it} = d_{it} + s_{it} + for i in I: + for t in T: + model.addConstr( + s[i, t - 1] + x[i, t] == d[i][t] + s[i, t], + name=f"demand_balance_{i}_{t}" + ) + + # Constraint (3): sum_i (st_{it}*y_{it} + vt_{it}*x_{it}) <= cap_t + for t in T: + model.addConstr( + gp.quicksum(st[i][t] * y[i, t] + vt[i][t] * x[i, t] for i in I) <= cap[t], + name=f"capacity_{t}" + ) + + # Constraint (4): x_{it} <= min((cap_t - st_{it}) / vt_{it}, sd_{it|T|}) * y_{it} + for i in I: + for t in T: + if vt[i][t] > 0: + ub = min((cap[t] - st[i][t]) / vt[i][t], sd[i][t]) + else: + ub = sd[i][t] + # Ensure non-negative upper bound + ub = max(ub, 0) + model.addConstr( + x[i, t] <= ub * y[i, t], + name=f"setup_forcing_{i}_{t}" + ) + + # Constraint (5): s_{i,|T|} = 0 (ending inventory is zero) + for i in I: + model.addConstr(s[i, num_periods - 1] == 0, name=f"ending_inv_{i}") + + # Optimize + model.optimize() + + # Extract solution + result = {} + if model.SolCount > 0: + result["objective_value"] = model.ObjVal + result["status"] = model.Status + result["mip_gap"] = model.MIPGap if hasattr(model, 'MIPGap') else None + + # Extract variable values + production = [[0.0] * num_periods for _ in I] + setup = [[0] * num_periods for _ in I] + inventory = [[0.0] * (num_periods + 1) for _ in I] # includes initial + + for i in I: + inventory[i][0] = s[i, -1].X + for t in T: + production[i][t] = x[i, t].X + setup[i][t] = round(y[i, t].X) + inventory[i][t + 1] = s[i, t].X + + result["production"] = production + result["setup"] = setup + result["inventory"] = inventory + else: + result["objective_value"] = None + result["status"] = model.Status + result["mip_gap"] = None + + return result + + +def main(): + parser = argparse.ArgumentParser( + description="Solve CLST using CL formulation with Gurobi" + ) + parser.add_argument("--instance_path", type=str, required=True, + help="Path to the JSON instance file") + parser.add_argument("--solution_path", type=str, required=True, + help="Path for the output solution JSON file") + parser.add_argument("--time_limit", type=int, required=True, + help="Maximum solver runtime in seconds") + parser.add_argument("--log_path", type=str, default=None, help="Path to log incumbent solutions") + args = parser.parse_args() + install_gurobi_logger(args.log_path) + + instance = load_instance(args.instance_path) + result = solve_clst(instance, args.time_limit) + + with open(args.solution_path, 'w') as f: + json.dump(result, f, indent=2) + + print(f"Solution written to {args.solution_path}") + if result["objective_value"] is not None: + print(f"Objective value: {result['objective_value']}") + else: + print("No feasible solution found.") + + +if __name__ == "__main__": + main() diff --git a/araujo2015/gurobi_feasi_result/large_feasi_result_1.json b/araujo2015/gurobi_feasi_result/large_feasi_result_1.json new file mode 100644 index 0000000000000000000000000000000000000000..e305b82c3d6732211d5033415df1a105476e9cf1 --- /dev/null +++ b/araujo2015/gurobi_feasi_result/large_feasi_result_1.json @@ -0,0 +1,3 @@ +version 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+++ b/araujo2015/instance/tiny_instance.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d37b1c9133d1a8969c8fc4c9e80fcfc75791e1bcbcc4a6874ab79f91afd2b61b +size 1394 diff --git a/araujo2015/instance_schema.json b/araujo2015/instance_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..6890e63f9ccf267ac744091cc3253827353f5ec9 --- /dev/null +++ b/araujo2015/instance_schema.json @@ -0,0 +1,12 @@ +{ + "num_items": " Number of items produced on the machine.", + "num_periods": " Number of discrete time periods in the planning horizon.", + "demand": " Quantity of each item required by customers in each period.", + "setup_cost": " Fixed cost incurred each time the machine is set up to produce each item in a period.", + "holding_cost": " Per-unit cost for storing each item in inventory at the end of a period.", + "variable_cost": " Per-unit cost of producing each item.", + "setup_time": " Time consumed from the period's capacity each time the machine is set up to produce each item.", + "variable_time": " Time consumed from the period's capacity to produce one unit of each item.", + "capacity": " Total available production time on the machine in each period.", + "initial_inventory_cost": " Per-unit cost of initial stock available for each item at the start of the first period." +} diff --git a/araujo2015/mathematical_formulation.md b/araujo2015/mathematical_formulation.md new file mode 100644 index 0000000000000000000000000000000000000000..822c429db73809856c6c9f1dd0bbed94d71e7afe --- /dev/null +++ b/araujo2015/mathematical_formulation.md @@ -0,0 +1,62 @@ +# Original Formulation: Capacitated Lot Sizing Problem with Setup Times (CLST) + +*Source: Period Decompositions for the Capacitated Lot Sizing Problem with Setup Times, Silvio Alexandre de Araujo, Bert De Reyck, Zeger Degraeve, Ioannis Fragkos, Raf Jans, 2015.* + +This is the *regular* formulation (CL) presented in Section 3.1, which defines the problem the paper studies and whose notation ($x_{it}, y_{it}, s_{it}$) is carried into all subsequent reformulations (SP, SPt, FL, FLp), decompositions, and experiments. + +## Sets and Indices + +- $I$: set of items, $= \{1, \dots, |I|\}$, indexed by $i$. + +- $T$: set of time periods, $= \{1, \dots, |T|\}$, indexed by $t$ (and $k$). + +## Parameters + +- $d_{it}$: demand of item $i$ in period $t$, $\forall i \in I,\ \forall t \in T$. + +- $sd_{itk}$: sum of demand of item $i$, from period $t$ until $k$, $\forall i \in I,\ \forall t \in T,\ \forall k \in T: k \geq t$ (i.e. $sd_{itk} = \sum_{u=t}^{k} d_{iu}$). + +- $hc_{it}$: unit holding cost for item $i$ in period $t$, $\forall i \in I,\ \forall t \in T$. + +- $sc_{it}$: setup cost for item $i$ in period $t$, $\forall i \in I,\ \forall t \in T$. + +- $vc_{it}$: variable production cost for item $i$ in period $t$, $\forall i \in I,\ \forall t \in T$. + +- $fc_{i}$: unit cost for initial inventory for item $i$, $\forall i \in I$. + +- $st_{it}$: setup time for item $i$ in period $t$, $\forall i \in I,\ \forall t \in T$. + +- $vt_{it}$: variable production time for item $i$ in period $t$, $\forall i \in I,\ \forall t \in T$. + +- $cap_{t}$: time capacity in period $t$, $\forall t \in T$. + +## Decision Variables + +- $x_{it}$: production quantity of item $i$ in period $t$, $\forall i \in I,\ \forall t \in T$. + +- $y_{it}$: $= 1$ if setup for item $i$ in period $t$, $0$ otherwise, $\forall i \in I,\ \forall t \in T$. + +- $s_{it}$: inventory for item $i$ at the end of period $t$, $\forall i \in I,\ \forall t \in T$. + +- $s_{i0}$: amount of initial inventory for item $i$, $\forall i \in I$. + +## Objective + +$$\begin{equation} +\min \left\{ \sum_{i \in I} fc_i\, s_{i0} + + \sum_{i \in I} \sum_{t \in T} \left( sc_{it}\, y_{it} + vc_{it}\, x_{it} + hc_{it}\, s_{it} \right) \right\} +\tag{1} +\end{equation}$$ + +## Constraints + +$$\begin{align} +& s_{i,t-1} + x_{it} = d_{it} + s_{it} + && \forall\, i \in I,\ \forall\, t \in T \tag{2} \\[4pt] +& \sum_{i \in I} \left( st_{it}\, y_{it} + vt_{it}\, x_{it} \right) \leq cap_t + && \forall\, t \in T \tag{3} \\[4pt] +& x_{it} \leq \min\!\left( \frac{cap_t - st_{it}}{vt_{it}},\; sd_{it|T|} \right) y_{it} + && \forall\, i \in I,\ \forall\, t \in T \tag{4} \\[4pt] +& y_{it} \in \{0,1\},\ x_{it} \geq 0,\ s_{it} \geq 0,\ s_{i0} \geq 0,\ s_{i|T|} = 0 + && \forall\, i \in I,\ \forall\, t \in T \tag{5} +\end{align}$$ diff --git a/araujo2015/problem_description.txt b/araujo2015/problem_description.txt new file mode 100644 index 0000000000000000000000000000000000000000..209d60d6f75acfb43ce17527918768100544f74c --- /dev/null +++ b/araujo2015/problem_description.txt @@ -0,0 +1,9 @@ +# Problem Description + +A manufacturing facility produces multiple items on a single machine over a finite planning horizon divided into discrete time periods. The input data specifies the number of items and the number of periods, the demand for each item in each period, a setup cost for each item in each period (incurred when the item is produced in that period), a per-unit holding cost for each item in each period (charged on inventory carried at the end of the period), a per-unit variable production cost for each item in each period, a setup time for each item in each period (consumed from the period's capacity whenever that item is produced), a per-unit variable production time for each item in each period, a time capacity for each period, and a per-unit cost for initial inventory for each item. + +The planner must determine, for each item in each period, the quantity to produce, whether a setup is performed, and the inventory held at the end of the period. The planner must also determine the amount of initial inventory for each item, which is stock available at the start of the first period without requiring a setup. + +In every period, the inventory of each item carried from the previous period plus the production of that item in the current period must equal the demand for that item in the current period plus the inventory carried forward to the next period. All inventory at the end of the final period must be zero; that is, no stock may remain after the last period. Production of an item in a given period can only occur if a setup for that item is performed in that period. Furthermore, the quantity produced of any item in a period cannot exceed the smaller of two values: the period's total time capacity minus that item's setup time in that period divided by that item's per-unit production time in that period, and the total remaining demand for that item from that period through the end of the horizon. In each period, the total time consumed by all setups and all production across every item must not exceed the period's time capacity. + +The goal is to minimize total cost, which is the sum of four components: the cost of initial inventory (the per-unit initial inventory cost times the initial stock for each item, summed over all items), the setup costs (the setup cost times the setup indicator for each item in each period, summed over all items and periods), the variable production costs (the per-unit production cost times the production quantity for each item in each period, summed over all items and periods), and the holding costs (the per-unit holding cost times the end-of-period inventory for each item in each period, summed over all items and periods). diff --git a/araujo2015/solution_logger.py b/araujo2015/solution_logger.py new file mode 100644 index 0000000000000000000000000000000000000000..5393fa935ce71df9da95cee04fe5199077cb8461 --- /dev/null +++ b/araujo2015/solution_logger.py @@ -0,0 +1,61 @@ +""" +Convergence logger for optimization algorithms. + +Records incumbent solutions with timestamps to a JSONL file. +This module is provided to LLM-generated programs — they only need to call +`log(objective_value)` whenever a better feasible solution is found. + +Usage in generated code: + from solution_logger import SolutionLogger + logger = SolutionLogger(log_path, sense="minimize") # or "maximize" + # ... inside algorithm loop: + logger.log(objective_value) +""" + +import json +import time + + +class SolutionLogger: + def __init__(self, log_path, sense="minimize"): + """ + Args: + log_path: Path to the JSONL output file. + sense: "minimize" or "maximize". + """ + self.log_path = log_path + self.sense = sense + self.start_time = time.time() + self.best_obj = None + self.min_interval = 0.1 # seconds, avoid excessive writes + + self._last_log_time = 0.0 + # Clear the file + with open(self.log_path, "w") as f: + pass + + def log(self, objective_value): + """Record a new incumbent if it improves on the best known.""" + if objective_value is None: + return + + # Check if this is an improvement + if self.best_obj is not None: + if self.sense == "minimize" and objective_value >= self.best_obj: + return + if self.sense == "maximize" and objective_value <= self.best_obj: + return + + elapsed = time.time() - self.start_time + + # Throttle writes + if self.best_obj is not None and elapsed - self._last_log_time < self.min_interval: + self.best_obj = objective_value + return + + self.best_obj = objective_value + self._last_log_time = elapsed + + with open(self.log_path, "a") as f: + f.write(json.dumps({"time": round(elapsed, 3), + "objective_value": objective_value}) + "\n") diff --git a/araujo2015/solution_schema.json b/araujo2015/solution_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..5d6ef147176c4bd55bcf91b5de03353b839ad621 --- /dev/null +++ b/araujo2015/solution_schema.json @@ -0,0 +1,6 @@ +{ + "objective_value": " Total cost including initial inventory costs, setup costs, variable production costs, and holding costs.", + "production": " Quantity of each item produced on the machine in each period.", + "setup": " Whether the machine is set up to produce each item in each period (1 = yes, 0 = no).", + "inventory": " Stock of each item on hand at each point in time, where the first entry is the initial inventory before the first period and subsequent entries are the inventory at the end of each period." +} diff --git a/araujo2020/feasibility_check.py b/araujo2020/feasibility_check.py new file mode 100644 index 0000000000000000000000000000000000000000..6394c452be33ad126a9a1d7ddff049f798912156 --- /dev/null +++ b/araujo2020/feasibility_check.py @@ -0,0 +1,619 @@ +#!/usr/bin/env python3 +""" +Feasibility checker for MMRCPSP / MMRCMPSP solutions. + +Checks all hard constraints from the PDT formulation (Eqs. 8-16) of: + Araujo et al. (2019): "Strong Bounds for Resource Constrained Project + Scheduling: Preprocessing and Cutting Planes" + +Constraint numbering (top to bottom in formulation): + 1: Eq (8) - Assignment: each job to exactly one mode and start time + 2: Eq (9) - Non-renewable resource capacity + 3: Eq (10) - Renewable resource capacity per time period + 4: Eq (11) - Precedence constraints + 5: Eq (12) - Linking z and x variables + 6: Eq (13) - Makespan >= project completion times + 7: Eq (14) - Binary domain of x + 8: Eq (15) - Binary domain of z + 9: Eq (16) - Non-negativity of h + 10: Eq (7) - Objective consistency: reported objective_value must equal + the recomputed Total Project Delay (TPD) from the schedule +""" + +import argparse +import json +from collections import defaultdict, deque + +# FRONTIEROR_RELEASE_CONTRACT_V2 +_FRONTIEROR_REQUIRED_SOLUTION_FIELDS = ('makespan', 'objective_value', 'schedule') +_FRONTIEROR_NONEMPTY_SOLUTION_FIELDS = ('makespan',) +_FRONTIEROR_DERIVED_FIELD_RULES = () +_FRONTIEROR_CONDITIONAL_FIELD_RULES = () + + +def _frontieror_validate_solution_contract(solution): + """Return a structured rejection when the public solution contract is absent.""" + violations = [] + if not isinstance(solution, dict): + violations.append("solution must be a JSON object") + else: + missing = [ + field + for field in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS + if field not in solution or solution[field] is None + ] + if missing: + violations.append( + "missing required solution field(s): " + ", ".join(missing) + ) + + for discriminator, expected_value, fields in _FRONTIEROR_CONDITIONAL_FIELD_RULES: + if solution.get(discriminator) != expected_value: + continue + conditional_missing = [ + field + for field in fields + if field not in solution or solution[field] is None + ] + if conditional_missing: + violations.append( + "missing conditionally required solution field(s): " + + ", ".join(conditional_missing) + ) + + empty = [ + field + for field in _FRONTIEROR_NONEMPTY_SOLUTION_FIELDS + if field in solution and solution[field] in (None, [], {}) + ] + if empty: + violations.append( + "empty required decision witness field(s): " + ", ".join(empty) + ) + + for target, operation, source, source_key in _FRONTIEROR_DERIVED_FIELD_RULES: + try: + if operation == "assignment_key_set": + reported_set = set(solution[target]) + expected_set = {entry[source_key] for entry in solution[source]} + if reported_set != expected_set: + violations.append( + f"{target}={sorted(reported_set)} does not match the " + f"decision-derived value {sorted(expected_set)}" + ) + continue + + reported = float(solution[target]) + if operation == "length": + expected = float(len(solution[source])) + elif operation == "mapping_value": + expected = float(solution[source][source_key]) + else: + raise ValueError("unknown derived-field operation") + except (KeyError, TypeError, ValueError, OverflowError): + violations.append( + f"cannot derive {target} from decision witness field {source}" + ) + continue + if ( + reported != reported + or expected != expected + or reported in (float("inf"), float("-inf")) + or expected in (float("inf"), float("-inf")) + or abs(reported - expected) > 1e-6 + ): + violations.append( + f"{target}={reported} does not match the decision-derived " + f"value {expected}" + ) + + if "objective_value" in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS: + objective = solution.get("objective_value") + if ( + isinstance(objective, bool) + or not isinstance(objective, (int, float)) + or objective != objective + or objective in (float("inf"), float("-inf")) + ): + violations.append("objective_value must be a finite JSON number") + + if not violations: + return None + return { + "feasible": False, + "violated_constraints": ["solution_schema"], + "violations": violations, + "violation_magnitudes": [], + } + + +def _frontieror_write_unexpected_rejection(error): + """Turn malformed candidate/checker exceptions into a normal rejection.""" + import json as _frontieror_json + import sys as _frontieror_sys + + result_path = None + for index, argument in enumerate(_frontieror_sys.argv): + if argument == "--result_path" and index + 1 < len(_frontieror_sys.argv): + result_path = _frontieror_sys.argv[index + 1] + break + if argument.startswith("--result_path="): + result_path = argument.split("=", 1)[1] + break + if not result_path: + return False + try: + with open(result_path, "w") as result_handle: + _frontieror_json.dump( + { + "feasible": False, + "violated_constraints": ["malformed_solution"], + "violations": [ + "checker rejected malformed candidate: " + + type(error).__name__ + + ": " + + str(error)[:500] + ], + "violation_magnitudes": [], + }, + result_handle, + indent=2, + ) + except Exception: + return False + return True + + +TOL = 1e-5 +EPS = 1e-5 + + +def load_json(path): + with open(path) as f: + return json.load(f) + + +def _topological_sort(job_ids, successors_map): + pred = defaultdict(list) + for jid in job_ids: + for s in successors_map.get(jid, []): + pred[s].append(jid) + indeg = {jid: len(pred[jid]) for jid in job_ids} + q = deque([jid for jid in job_ids if indeg[jid] == 0]) + order = [] + while q: + jid = q.popleft() + order.append(jid) + for s in successors_map.get(jid, []): + indeg[s] -= 1 + if indeg[s] == 0: + q.append(s) + return order + + +def _compute_cpd(jobs_data, project): + """CPM forward pass with min-mode durations: lambda_p = est[sink] - sigma_p.""" + pid = project["project_id"] + sigma = project["release_date"] + sink = project["artificial_sink_job_id"] + job_ids = [j["job_id"] for j in jobs_data if j["project_id"] == pid] + successors_map = { + j["job_id"]: list(j["successors"]) + for j in jobs_data if j["project_id"] == pid + } + min_dur = { + j["job_id"]: min(m["duration"] for m in j["modes"]) + for j in jobs_data if j["project_id"] == pid + } + order = _topological_sort(job_ids, successors_map) + est = {jid: sigma for jid in job_ids} + for jid in order: + for s in successors_map.get(jid, []): + est[s] = max(est[s], est[jid] + min_dur[jid]) + if sink not in est: + return None + return est[sink] - sigma + + +def check_feasibility(instance, solution): + violations = [] + violation_magnitudes = [] + + jobs_data = instance["jobs"] + projects = instance["projects"] + resources = instance["resources"] + renewable_res = resources.get("renewable", []) + nonrenewable_res = resources.get("nonrenewable", []) + + schedule = solution["schedule"] + h_val = solution.get("makespan", 0) + + # Build lookup structures + job_by_id = {j["job_id"]: j for j in jobs_data} + mode_by_jm = {} + for j in jobs_data: + for m in j["modes"]: + mode_by_jm[(j["job_id"], m["mode_id"])] = m + + # Build assignment from solution: job_id -> (mode_id, start_time) + assignment = {} + for entry in schedule: + jid = entry["job_id"] + mid = entry["mode_id"] + st = entry["start_time"] + if jid in assignment: + assignment[jid] = None # Mark as duplicate + else: + assignment[jid] = (mid, st) + + all_job_ids = set(j["job_id"] for j in jobs_data) + assigned_job_ids = set(entry["job_id"] for entry in schedule) + + # Helper to record a violation + def record(constraint_idx, msg, lhs, rhs, operator): + if operator == "eq": + violation_amount = abs(lhs - rhs) + elif operator == "leq": + violation_amount = max(0.0, lhs - rhs) + elif operator == "geq": + violation_amount = max(0.0, rhs - lhs) + else: + violation_amount = 0.0 + + if violation_amount > TOL: + normalizer = max(abs(rhs), EPS) + ratio = violation_amount / normalizer + violations.append((constraint_idx, msg)) + violation_magnitudes.append({ + "constraint": constraint_idx, + "lhs": float(lhs), + "rhs": float(rhs), + "raw_excess": float(violation_amount), + "normalizer": float(normalizer), + "ratio": float(ratio), + }) + + # ========================================================================= + # Constraint 1: Eq (8) - Each job assigned to exactly one mode/start time + # sum_{m,t} x_{jmt} = 1 for all j + # ========================================================================= + for j in jobs_data: + jid = j["job_id"] + count = sum(1 for entry in schedule if entry["job_id"] == jid) + if count != 1: + lhs = float(count) + rhs = 1.0 + record(1, f"Job {jid}: assigned {count} times (expected exactly 1)", lhs, rhs, "eq") + + # Check that assigned mode is valid for the job + for entry in schedule: + jid = entry["job_id"] + mid = entry["mode_id"] + if jid not in job_by_id: + record(1, f"Job {jid} in solution does not exist in instance", 0.0, 1.0, "eq") + continue + valid_modes = [m["mode_id"] for m in job_by_id[jid]["modes"]] + if mid not in valid_modes: + record(1, f"Job {jid}: mode {mid} not in valid modes {valid_modes}", 0.0, 1.0, "eq") + + # ========================================================================= + # Constraint 2: Eq (9) - Non-renewable resource capacity + # sum_j sum_m sum_t q_{kjm} * x_{jmt} <= check_q_k for all k + # ========================================================================= + for k_data in nonrenewable_res: + kid = k_data["resource_id"] + cap = k_data["capacity"] + total_usage = 0.0 + for entry in schedule: + jid = entry["job_id"] + mid = entry["mode_id"] + key = (jid, mid) + if key in mode_by_jm: + total_usage += mode_by_jm[key]["nonrenewable_consumption"][kid] + lhs = total_usage + rhs = float(cap) + record(2, f"Non-renewable resource {kid}: usage {lhs} exceeds capacity {rhs}", lhs, rhs, "leq") + + # ========================================================================= + # Constraint 3: Eq (10) - Renewable resource capacity per time period + # sum_j sum_m q_{rjm} * z_{jmt} <= check_q_r for all r, t + # ========================================================================= + # Derive z from x: z_{j,m,t}=1 iff job j in mode m is processing at time t + # i.e., start_time <= t < start_time + duration + max_time = 0 + processing_intervals = {} # job_id -> (mode_id, start, end_exclusive) + for entry in schedule: + jid = entry["job_id"] + mid = entry["mode_id"] + st = entry["start_time"] + key = (jid, mid) + if key in mode_by_jm: + dur = mode_by_jm[key]["duration"] + processing_intervals[jid] = (mid, st, st + dur) + if st + dur > max_time: + max_time = st + dur + + for r_data in renewable_res: + rid = r_data["resource_id"] + cap = r_data["capacity"] + for t in range(max_time): + usage = 0.0 + active_jobs = [] + for jid, (mid, start, end) in processing_intervals.items(): + if start <= t < end: + usage += mode_by_jm[(jid, mid)]["renewable_consumption"][rid] + active_jobs.append(jid) + lhs = usage + rhs = float(cap) + if lhs - rhs > TOL: + record( + 3, + f"Renewable resource {rid} at time {t}: usage {lhs} exceeds capacity {rhs} (active jobs: {active_jobs})", + lhs, rhs, "leq", + ) + + # ========================================================================= + # Constraint 4: Eq (11) - Precedence constraints + # sum_m sum_t (t + d_{jm}) * x_{jmt} - sum_z sum_i i * x_{szi} <= 0 + # i.e., finish_time(j) <= start_time(s) for each (j,s) in precedence + # ========================================================================= + for j in jobs_data: + jid = j["job_id"] + if jid not in assigned_job_ids: + continue + assigned_j = next((e for e in schedule if e["job_id"] == jid), None) + if assigned_j is None: + continue + mid_j = assigned_j["mode_id"] + st_j = assigned_j["start_time"] + dur_j = mode_by_jm.get((jid, mid_j), {}).get("duration", 0) if (jid, mid_j) in mode_by_jm else 0 + finish_j = st_j + dur_j + + for s_id in j.get("successors", []): + assigned_s = next((e for e in schedule if e["job_id"] == s_id), None) + if assigned_s is None: + continue + st_s = assigned_s["start_time"] + # LHS = finish_j - start_s, must be <= 0 + lhs = float(finish_j - st_s) + rhs = 0.0 + if lhs > TOL: + record( + 4, + f"Precedence: job {jid} finishes at {finish_j} but successor {s_id} starts at {st_s}", + lhs, rhs, "leq", + ) + + # ========================================================================= + # Constraint 5: Eq (12) - Linking z and x + # z_{jmt} - sum_{t'=(t-d+1)}^{t} x_{jmt'} = 0 + # This is definitional: z is derived from x. We verify consistency. + # Since z is derived from the schedule, this is satisfied by construction. + # We still check: for each job, the derived z values are consistent with + # being binary and matching exactly the processing interval. + # ========================================================================= + for entry in schedule: + jid = entry["job_id"] + mid = entry["mode_id"] + st = entry["start_time"] + key = (jid, mid) + if key not in mode_by_jm: + continue + dur = mode_by_jm[key]["duration"] + if dur == 0: + continue + # z_{j,m,t} should be 1 for t in [st, st+dur-1] and 0 elsewhere + # For each t in processing window, check: + # z_{jmt} = sum_{t'=max(e_j, t-d+1)}^{min(l_jm, t)} x_{jmt'} + # Since x_{j,mid,st}=1 and all other x_{j,*,*}=0, z_{j,mid,t}=1 + # iff st <= t and t-dur+1 <= st, i.e., st <= t <= st+dur-1. + # This is exactly the processing interval, so by construction OK. + # We verify start_time is non-negative. + if st < 0: + record(5, f"Job {jid}: start time {st} is negative", float(st), 0.0, "geq") + + # ========================================================================= + # Constraint 6: Eq (13) - Makespan computation + # h - sum_m sum_t t * x_{a_p,m,t} >= 0 for all p + # i.e., h >= completion_time(sink_job) for each project + # ========================================================================= + for p in projects: + pid = p["project_id"] + a_p = p["artificial_sink_job_id"] + assigned_sink = next((e for e in schedule if e["job_id"] == a_p), None) + if assigned_sink is None: + continue + sink_start = assigned_sink["start_time"] + # sink has duration 0, so completion = start + # h >= t * x_{a_p,m,t} which equals sink_start + lhs = float(h_val) + rhs = float(sink_start) + # Constraint: h - sink_start >= 0, i.e., lhs = h, rhs = sink_start, geq + # Rewriting: lhs_of_constraint = h - sink_start >= 0 + # So we check h >= sink_start + if rhs - lhs > TOL: + record( + 6, + f"Project {pid}: makespan h={h_val} < sink job {a_p} start time {sink_start}", + lhs, rhs, "geq", + ) + + # ``h`` is the model's canonical makespan variable. The Gurobi objective + # contains a positive tie-break coefficient on h, so it must equal (not + # merely dominate) the latest artificial-sink start represented by the + # schedule. This also binds the published summary field to its witness. + sink_entries = [ + next((e for e in schedule + if e["job_id"] == p["artificial_sink_job_id"]), None) + for p in projects + ] + if all(entry is not None for entry in sink_entries): + derived_makespan = max( + (entry["start_time"] for entry in sink_entries), + default=0, + ) + record( + 6, + f"Makespan summary h={h_val} does not equal the latest project " + f"sink start {derived_makespan}", + float(h_val), float(derived_makespan), "eq", + ) + + # ========================================================================= + # Constraint 7: Eq (14) - Binary domain of x + # x_{jmt} in {0,1} + # By construction from schedule, x values are 0 or 1. Verify start times + # are integers. + # ========================================================================= + for entry in schedule: + st = entry["start_time"] + if not isinstance(st, int) and (isinstance(st, float) and st != int(st)): + record(7, f"Job {entry['job_id']}: start_time {st} is not integer (binary x violated)", + float(abs(st - round(st))), 0.0, "eq") + + # ========================================================================= + # Constraint 8: Eq (15) - Binary domain of z + # z_{jmt} in {0,1} + # Derived from binary x, so satisfied by construction. Check durations + # are non-negative integers. + # ========================================================================= + for entry in schedule: + jid = entry["job_id"] + mid = entry["mode_id"] + key = (jid, mid) + if key in mode_by_jm: + dur = mode_by_jm[key]["duration"] + if not isinstance(dur, int) or dur < 0: + record(8, f"Job {jid} mode {mid}: duration {dur} invalid (z binary violated)", + float(abs(dur - round(dur))), 0.0, "eq") + + # ========================================================================= + # Constraint 9: Eq (16) - Non-negativity AND integrality of h + # h ∈ Z_{>=0} + # ========================================================================= + if h_val < -TOL: + record(9, f"Makespan h={h_val} is negative", float(h_val), 0.0, "geq") + if isinstance(h_val, float) and abs(h_val - round(h_val)) > 1e-6: + record(9, f"Makespan h={h_val} is not integer (Eq 16)", + float(abs(h_val - round(h_val))), 0.0, "eq") + elif not isinstance(h_val, (int, float)): + record(9, f"Makespan h={h_val!r} is not numeric (Eq 16)", + 0.0, 0.0, "eq") + + # ========================================================================= + # Constraint 10: Eq (7) - Objective consistency (full recompute) + # TPD = sum_{p} (sink_start_p - sigma_p - lambda_p) + # sink_start_p : start_time of project p's artificial_sink_job in schedule + # sigma_p : project release_date + # lambda_p : Critical Path Duration via forward CPM on min-mode durations + # The Gurobi reference reports TPD only (epsilon * h tiebreaker excluded + # from objective_value), so the comparison is exact. + # ========================================================================= + reported_obj = solution.get("objective_value") + if reported_obj is not None: + try: + reported = float(reported_obj) + except (TypeError, ValueError): + reported = None + + # Skip the obj check only if any sink job is missing from the schedule + # (already flagged by constraint 1). Otherwise the recompute is well-defined. + sink_starts = {} + sinks_present = True + for p in projects: + sink_id = p["artificial_sink_job_id"] + entry = next((e for e in schedule if e["job_id"] == sink_id), None) + if entry is None: + sinks_present = False + break + sink_starts[p["project_id"]] = entry["start_time"] + + if reported is not None and sinks_present: + true_tpd = 0.0 + cpd_ok = True + for p in projects: + lam = _compute_cpd(jobs_data, p) + if lam is None: + cpd_ok = False + break + sigma_p = p["release_date"] + true_tpd += sink_starts[p["project_id"]] - sigma_p - lam + + if cpd_ok: + abs_diff = abs(reported - true_tpd) + # TPD is integer-valued; floor at 0.5 catches any integer mismatch. + tol = max(0.5, 1e-3 * abs(true_tpd)) + if abs_diff > tol: + record( + 10, + ( + f"Objective consistency violated: reported objective_value=" + f"{reported} differs from recomputed Total Project Delay=" + f"{true_tpd} (|diff|={abs_diff:.3g}, tol={tol:.3g})" + ), + float(reported), + float(true_tpd), + "eq", + ) + + # Build output + violated_indices = sorted(set(c for c, _ in violations)) + violation_messages = [] + for idx in violated_indices: + msgs = [msg for c, msg in violations if c == idx] + violation_messages.extend(msgs) + + feasible = len(violated_indices) == 0 + + return { + "feasible": feasible, + "violated_constraints": violated_indices, + "violations": violation_messages, + "violation_magnitudes": violation_magnitudes, + } + + +def main(): + parser = argparse.ArgumentParser( + description="Feasibility checker for MMRCPSP solutions (Araujo et al. 2019)" + ) + parser.add_argument("--instance_path", required=True, + help="Path to the JSON instance file") + parser.add_argument("--solution_path", required=True, + help="Path to the JSON solution file") + parser.add_argument("--result_path", required=True, + help="Path to write the JSON feasibility result") + args = parser.parse_args() + + instance = load_json(args.instance_path) + solution = load_json(args.solution_path) + _frontieror_contract_result = _frontieror_validate_solution_contract(solution) + if _frontieror_contract_result is not None: + with open(args.result_path, "w") as _frontieror_result_handle: + json.dump( + _frontieror_contract_result, + _frontieror_result_handle, + indent=2, + ) + return + result = check_feasibility(instance, solution) + + with open(args.result_path, "w") as f: + json.dump(result, f, indent=2) + + print(f"Feasibility result written to {args.result_path}") + print(f"Feasible: {result['feasible']}") + if not result["feasible"]: + print(f"Violated constraints: {result['violated_constraints']}") + for v in result["violations"]: + print(f" - {v}") + + +if __name__ == "__main__": + try: + main() + except Exception as _frontieror_unexpected_error: + if not _frontieror_write_unexpected_rejection( + _frontieror_unexpected_error + ): + raise diff --git a/araujo2020/gurobi_code.py b/araujo2020/gurobi_code.py new file mode 100644 index 0000000000000000000000000000000000000000..d6e2c25235d1a147216503ddc551b37c633f72a5 --- /dev/null +++ b/araujo2020/gurobi_code.py @@ -0,0 +1,460 @@ +#!/usr/bin/env python3 +""" +Gurobi implementation of the MMRCPSP / MMRCMPSP formulation from: + Araujo et al. (2019): "Strong Bounds for Resource Constrained Project + Scheduling: Preprocessing and Cutting Planes" + +Implements the PDT (Pulse Discrete Time) MILP formulation (Eqs. 7-16) +with preprocessing for time window reduction (Eqs. 4-6). + +NOT SPECIFIED IN PAPER: + - epsilon value in Eq. (7): inferred as 1e-6 (small tiebreaker coefficient) + - beta_p upper bound source: inferred as sum of max-mode durations (serial schedule) +""" + +import argparse +import json +from collections import defaultdict, deque + +import gurobipy as gp +from gurobipy import GRB +import os as _os, sys as _sys +# Walk up from this file's directory to find repo root (containing scripts/). +_repo = _os.path.dirname(_os.path.abspath(__file__)) +while _repo != _os.path.dirname(_repo) and not _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _repo = _os.path.dirname(_repo) +if _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _sys.path.insert(0, _repo) +try: + from scripts.utils.gurobi_log_helper import install_gurobi_logger +except ImportError: + def install_gurobi_logger(log_path): # no-op fallback when scripts/ unavailable + pass + + +def load_instance(path): + with open(path) as f: + return json.load(f) + + +def topological_sort(job_ids, successors_map): + """Return topological order of job_ids given successors_map.""" + predecessors = defaultdict(list) + for jid in job_ids: + for s in successors_map.get(jid, []): + predecessors[s].append(jid) + + in_degree = {jid: len(predecessors[jid]) for jid in job_ids} + queue = deque([jid for jid in job_ids if in_degree[jid] == 0]) + order = [] + while queue: + jid = queue.popleft() + order.append(jid) + for s in successors_map.get(jid, []): + in_degree[s] -= 1 + if in_degree[s] == 0: + queue.append(s) + return order + + +def compute_cpm_forward(jobs_data, project): + """ + Forward CPM pass: compute earliest start times using minimum (fastest) mode durations. + Returns dict {job_id: earliest_start_time}. + """ + sigma = project['release_date'] + pid = project['project_id'] + job_ids = [j['job_id'] for j in jobs_data if j['project_id'] == pid] + successors_map = {j['job_id']: list(j['successors']) + for j in jobs_data if j['project_id'] == pid} + min_dur = {j['job_id']: min(m['duration'] for m in j['modes']) + for j in jobs_data if j['project_id'] == pid} + + topo = topological_sort(job_ids, successors_map) + est = {jid: sigma for jid in job_ids} + for jid in topo: + for s in successors_map.get(jid, []): + est[s] = max(est[s], est[jid] + min_dur[jid]) + return est + + +def compute_back_cpm(jobs_data, project): + """ + Backward pass: compute longest path from each job to sink using minimum durations. + back[j] = total min-duration path length from j to project sink. + Used to compute L_{jm} and latest start times. + """ + pid = project['project_id'] + job_ids = [j['job_id'] for j in jobs_data if j['project_id'] == pid] + successors_map = {j['job_id']: list(j['successors']) + for j in jobs_data if j['project_id'] == pid} + min_dur = {j['job_id']: min(m['duration'] for m in j['modes']) + for j in jobs_data if j['project_id'] == pid} + + topo = topological_sort(job_ids, successors_map) + back = {jid: 0 for jid in job_ids} + for jid in reversed(topo): + succs = successors_map.get(jid, []) + if succs: + back[jid] = min_dur[jid] + max(back[s] for s in succs) + else: + back[jid] = min_dur[jid] + return back + + +def compute_beta_ub(jobs_data, project): + """ + Compute upper bound beta_p for project completion time. + INFERRED ASSUMPTION: beta_p = sigma_p + sum of max-mode durations (worst-case + serial schedule). This is always a valid upper bound since jobs can always + be scheduled sequentially. + """ + sigma = project['release_date'] + total = sum( + max(m['duration'] for m in j['modes']) + for j in jobs_data if j['project_id'] == project['project_id'] + ) + return sigma + total + + +def build_time_windows(jobs_data, project, est, back, sigma_p, lambda_p, alpha): + """ + Compute T_{jm} = (earliest_start, latest_start) for each (job, mode). + l_{jm} = sigma_p + lambda_p - L_{jm} + alpha [Eq. 6] + L_{jm} = d_{jm} + max_{s in S_j} back[s] (d_{jm} + longest path from successors) + """ + pid = project['project_id'] + successors_map = {j['job_id']: list(j['successors']) + for j in jobs_data if j['project_id'] == pid} + + T_jm = {} + for j in jobs_data: + if j['project_id'] != pid: + continue + jid = j['job_id'] + e_j = est[jid] + succs = successors_map.get(jid, []) + + for m_data in j['modes']: + mid = m_data['mode_id'] + d_jm = m_data['duration'] + # L_{jm}: d_{jm} + longest min-duration path from successors to sink + if succs: + L_jm = d_jm + max(back[s] for s in succs) + else: + L_jm = d_jm + l_jm = sigma_p + lambda_p - L_jm + alpha + # Ensure valid window; if l_jm < e_j, window is empty (mode infeasible) + l_jm_int = int(max(e_j, l_jm)) + T_jm[(jid, mid)] = (int(e_j), l_jm_int) + + return T_jm + + +def build_and_solve(instance, time_limit): + jobs_data = instance['jobs'] + projects = instance['projects'] + resources = instance['resources'] + renewable_res = resources.get('renewable', []) + nonrenewable_res = resources.get('nonrenewable', []) + + # Project-level data + sigma_map = {p['project_id']: p['release_date'] for p in projects} + sink_map = {p['project_id']: p['artificial_sink_job_id'] for p in projects} + + est_all = {} + back_all = {} + lambda_map = {} + beta_map = {} + + for p in projects: + pid = p['project_id'] + est = compute_cpm_forward(jobs_data, p) + back = compute_back_cpm(jobs_data, p) + est_all.update(est) + back_all.update(back) + a_p = sink_map[pid] + lambda_map[pid] = est[a_p] - sigma_map[pid] # CPD [Eq. 3] + beta_map[pid] = compute_beta_ub(jobs_data, p) + + # Compute alpha [Eq. 4] + alpha = sum( + beta_map[p['project_id']] - sigma_map[p['project_id']] - lambda_map[p['project_id']] + for p in projects + ) + alpha = max(0, alpha) + + # Compute t_check and T [Eq. 5] + t_check = int(max(sigma_map[p['project_id']] + lambda_map[p['project_id']] + alpha + for p in projects)) + T_set = range(t_check + 1) + + # Build time windows T_{jm} [Eq. 6] + T_jm = {} + for p in projects: + pid = p['project_id'] + tw = build_time_windows( + jobs_data, p, + {j['job_id']: est_all[j['job_id']] for j in jobs_data if j['project_id'] == pid}, + {j['job_id']: back_all[j['job_id']] for j in jobs_data if j['project_id'] == pid}, + sigma_map[pid], lambda_map[pid], alpha + ) + T_jm.update(tw) + + # Build index structures + job_mode_data = {j['job_id']: {m['mode_id']: m for m in j['modes']} for j in jobs_data} + successors_map = {j['job_id']: list(j['successors']) for j in jobs_data} + + # ------------------------------------------------------------------- + # Build Gurobi model + # ------------------------------------------------------------------- + model = gp.Model("MMRCPSP") + model.setParam("Threads", 1) + model.setParam("TimeLimit", time_limit) + + # EPSILON: NOT EXPLICITLY SPECIFIED IN PAPER + # Inferred assumption: epsilon = 1e-6 (small enough to be pure tiebreaker) + epsilon = 1e-6 + + # Decision variables x_{jmt} [Eq. 14]: binary, start-time indexed + x = {} + for j in jobs_data: + jid = j['job_id'] + for m_data in j['modes']: + mid = m_data['mode_id'] + e_j, l_jm = T_jm[(jid, mid)] + for t in range(e_j, l_jm + 1): + x[(jid, mid, t)] = model.addVar(vtype=GRB.BINARY, name=f"x_{jid}_{mid}_{t}") + + # Decision variables z_{jmt} [Eq. 15]: binary, processing-time indexed + # z_{jmt}=1 if job j is being processed at time t in mode m + # Domain: t in [e_j, l_jm + d_jm - 1] (processing window) + z = {} + for j in jobs_data: + jid = j['job_id'] + for m_data in j['modes']: + mid = m_data['mode_id'] + d_jm = m_data['duration'] + if d_jm == 0: + continue # Zero-duration jobs are never "being processed" + e_j, l_jm = T_jm[(jid, mid)] + for t in range(e_j, min(l_jm + d_jm, t_check + 1)): + z[(jid, mid, t)] = model.addVar(vtype=GRB.BINARY, name=f"z_{jid}_{mid}_{t}") + + # h: integer makespan tiebreaker variable [Eq. 16] + h = model.addVar(vtype=GRB.INTEGER, lb=0, name="h") + model.update() + + # ------------------------------------------------------------------- + # Objective [Eq. 7]: minimize TPD + epsilon * h + # ------------------------------------------------------------------- + obj = gp.LinExpr() + for p in projects: + pid = p['project_id'] + a_p = sink_map[pid] + sigma_p = sigma_map[pid] + lambda_p = lambda_map[pid] + for m_data in job_mode_data[a_p].values(): + mid = m_data['mode_id'] + e_j, l_jm = T_jm[(a_p, mid)] + for t in range(e_j, l_jm + 1): + if (a_p, mid, t) in x: + obj += (t - (sigma_p + lambda_p)) * x[(a_p, mid, t)] + obj += epsilon * h + model.setObjective(obj, GRB.MINIMIZE) + + # ------------------------------------------------------------------- + # Constraint (8): Each job assigned to exactly one mode and start time + # ------------------------------------------------------------------- + for j in jobs_data: + jid = j['job_id'] + lhs = gp.LinExpr() + for m_data in j['modes']: + mid = m_data['mode_id'] + e_j, l_jm = T_jm[(jid, mid)] + for t in range(e_j, l_jm + 1): + if (jid, mid, t) in x: + lhs += x[(jid, mid, t)] + model.addConstr(lhs == 1, name=f"assign_{jid}") + + # ------------------------------------------------------------------- + # Constraint (9): Non-renewable resource capacity + # ------------------------------------------------------------------- + for k_data in nonrenewable_res: + kid = k_data['resource_id'] + cap = k_data['capacity'] + lhs = gp.LinExpr() + for j in jobs_data: + jid = j['job_id'] + for m_data in j['modes']: + mid = m_data['mode_id'] + q = m_data['nonrenewable_consumption'][kid] + if q == 0: + continue + e_j, l_jm = T_jm[(jid, mid)] + for t in range(e_j, l_jm + 1): + if (jid, mid, t) in x: + lhs += q * x[(jid, mid, t)] + model.addConstr(lhs <= cap, name=f"nonrenew_{kid}") + + # ------------------------------------------------------------------- + # Constraint (10): Renewable resource capacity for each time period + # ------------------------------------------------------------------- + # Build index: for each t, which (j,m) z-variables are defined there? + z_by_t = defaultdict(list) + for (jid, mid, t) in z: + z_by_t[t].append((jid, mid)) + + for r_data in renewable_res: + rid = r_data['resource_id'] + cap = r_data['capacity'] + for t in T_set: + entries = z_by_t.get(t, []) + if not entries: + continue + lhs = gp.LinExpr() + for (jid, mid) in entries: + q = job_mode_data[jid][mid]['renewable_consumption'][rid] + lhs += q * z[(jid, mid, t)] + if lhs.size() > 0: + model.addConstr(lhs <= cap, name=f"renew_{rid}_{t}") + + # ------------------------------------------------------------------- + # Constraint (11): Aggregated precedence constraints + # ------------------------------------------------------------------- + for j in jobs_data: + jid = j['job_id'] + for s_id in successors_map.get(jid, []): + s_modes = job_mode_data[s_id] + lhs = gp.LinExpr() + # (t + d_{jm}) * x_{jmt} for job j + for m_data in j['modes']: + mid = m_data['mode_id'] + d_jm = m_data['duration'] + e_j, l_jm = T_jm[(jid, mid)] + for t in range(e_j, l_jm + 1): + if (jid, mid, t) in x: + lhs += (t + d_jm) * x[(jid, mid, t)] + # - i * x_{szi} for successor s + for m_data in s_modes.values(): + smid = m_data['mode_id'] + e_s, l_sm = T_jm[(s_id, smid)] + for i in range(e_s, l_sm + 1): + if (s_id, smid, i) in x: + lhs -= i * x[(s_id, smid, i)] + model.addConstr(lhs <= 0, name=f"prec_{jid}_{s_id}") + + # ------------------------------------------------------------------- + # Constraint (12): Linking z and x variables + # z_{jmt} = sum_{t'=max(e_j, t-d_jm+1)}^{min(l_jm, t)} x_{jmt'} + # ------------------------------------------------------------------- + for j in jobs_data: + jid = j['job_id'] + for m_data in j['modes']: + mid = m_data['mode_id'] + d_jm = m_data['duration'] + if d_jm == 0: + continue # No linking needed for zero-duration jobs + e_j, l_jm = T_jm[(jid, mid)] + for t in range(e_j, min(l_jm + d_jm, t_check + 1)): + if (jid, mid, t) not in z: + continue + rhs = gp.LinExpr() + t_lo = max(e_j, t - d_jm + 1) + t_hi = min(l_jm, t) + for tp in range(t_lo, t_hi + 1): + if (jid, mid, tp) in x: + rhs += x[(jid, mid, tp)] + model.addConstr(z[(jid, mid, t)] - rhs == 0, name=f"link_{jid}_{mid}_{t}") + + # ------------------------------------------------------------------- + # Constraint (13): Makespan computation (tiebreaker) + # h >= t * x_{a_p, m, t} for all projects p, modes m, times t + # ------------------------------------------------------------------- + for p in projects: + pid = p['project_id'] + a_p = sink_map[pid] + lhs = gp.LinExpr() + lhs += h + for m_data in job_mode_data[a_p].values(): + mid = m_data['mode_id'] + e_j, l_jm = T_jm[(a_p, mid)] + for t in range(e_j, l_jm + 1): + if (a_p, mid, t) in x: + lhs -= t * x[(a_p, mid, t)] + model.addConstr(lhs >= 0, name=f"makespan_{pid}") + + # ------------------------------------------------------------------- + # Solve + # ------------------------------------------------------------------- + model.optimize() + + # ------------------------------------------------------------------- + # Extract solution + # ------------------------------------------------------------------- + solution = {} + status = model.Status + + if status in [GRB.OPTIMAL, GRB.TIME_LIMIT, GRB.SUBOPTIMAL] and model.SolCount > 0: + # Compute TPD (objective without epsilon*h term) + tpd_val = 0.0 + schedule = [] + for p in projects: + pid = p['project_id'] + a_p = sink_map[pid] + sigma_p = sigma_map[pid] + lambda_p = lambda_map[pid] + for m_data in job_mode_data[a_p].values(): + mid = m_data['mode_id'] + e_j, l_jm = T_jm[(a_p, mid)] + for t in range(e_j, l_jm + 1): + if (a_p, mid, t) in x and x[(a_p, mid, t)].X > 0.5: + tpd_val += t - (sigma_p + lambda_p) + + for j in jobs_data: + jid = j['job_id'] + for m_data in j['modes']: + mid = m_data['mode_id'] + e_j, l_jm = T_jm[(jid, mid)] + for t in range(e_j, l_jm + 1): + if (jid, mid, t) in x and x[(jid, mid, t)].X > 0.5: + schedule.append({'job_id': jid, 'mode_id': mid, 'start_time': t}) + + solution['objective_value'] = tpd_val + solution['status'] = 'optimal' if status == GRB.OPTIMAL else 'feasible' + solution['schedule'] = schedule + solution['makespan'] = int(round(h.X)) + solution['solver_runtime_seconds'] = model.Runtime + else: + solution['objective_value'] = None + solution['status'] = 'infeasible_or_no_solution' + solution['solver_runtime_seconds'] = model.Runtime + + return solution + + +def main(): + parser = argparse.ArgumentParser(description="Gurobi MILP solver for MMRCPSP (Araujo et al. 2019)") + parser.add_argument("--instance_path", required=True, + help="Path to the JSON instance file") + parser.add_argument("--solution_path", required=True, + help="Path to write the solution JSON file") + parser.add_argument("--time_limit", type=int, required=True, + help="Maximum solver runtime in seconds") + parser.add_argument("--log_path", type=str, default=None, help="Path to log incumbent solutions") + args = parser.parse_args() + install_gurobi_logger(args.log_path) + + instance = load_instance(args.instance_path) + solution = build_and_solve(instance, args.time_limit) + + with open(args.solution_path, 'w') as f: + json.dump(solution, f, indent=2) + + print(f"Solution written to {args.solution_path}") + print(f"Objective value (TPD): {solution.get('objective_value')}") + print(f"Status: {solution.get('status')}") + + +if __name__ == "__main__": + main() diff --git a/araujo2020/gurobi_feasi_result/large_feasi_result_1.json 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/dev/null +++ b/araujo2020/instance/large_instance_5.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:307d6ecd6d9cd473374d1aba33e668880d354dc654137e685553a571227fd932 +size 32327 diff --git a/araujo2020/instance/tiny_instance.json b/araujo2020/instance/tiny_instance.json new file mode 100644 index 0000000000000000000000000000000000000000..bb0ad71f61b1d2b5f0340d8baab83e2dbffa821f --- /dev/null +++ b/araujo2020/instance/tiny_instance.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ca18b2d41c8f09c7f1ee9978f8f3237eb2f159dd34b9855b9a61855d32e6ae9f +size 10116 diff --git a/araujo2020/instance_schema.json b/araujo2020/instance_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..c9d667481a87d5cf5a1b1823c1e6feaa3f7f7f35 --- /dev/null +++ b/araujo2020/instance_schema.json @@ -0,0 +1,41 @@ +{ + "projects": [ + { + "project_id": " Unique identifier for the project.", + "release_date": " Earliest time period at which any job in this project may start.", + "num_jobs": " Total number of jobs in this project, including artificial source and sink jobs.", + "artificial_source_job_id": " Job identifier of the zero-duration dummy job marking the project start.", + "artificial_sink_job_id": " Job identifier of the zero-duration dummy job marking the project end." + } + ], + "resources": { + "renewable": [ + { + "resource_id": " Unique identifier for this renewable resource.", + "capacity": " Maximum total consumption allowed across all jobs being processed simultaneously in any single time period." + } + ], + "nonrenewable": [ + { + "resource_id": " Unique identifier for this nonrenewable resource.", + "capacity": " Maximum total consumption allowed across all jobs over the entire planning horizon." + } + ] + }, + "jobs": [ + { + "job_id": " Unique identifier for this job.", + "project_id": " Identifier of the project this job belongs to.", + "type": " Role of the job: 'artificial_source' for the project start marker, 'artificial_sink' for the project end marker, or 'real' for an actual task.", + "successors": " Job identifiers that must not start before this job finishes.", + "modes": [ + { + "mode_id": " Unique identifier for this execution mode within the job.", + "duration": " Number of time periods required to complete the job when executed in this mode.", + "renewable_consumption": " Amount of each renewable resource consumed per time period while the job is being processed in this mode.", + "nonrenewable_consumption": " Amount of each nonrenewable resource consumed over the entire execution of the job in this mode." + } + ] + } + ] +} diff --git a/araujo2020/mathematical_formulation.md b/araujo2020/mathematical_formulation.md new file mode 100644 index 0000000000000000000000000000000000000000..bc46acb901e43a9e5afca45c5a608cee65c4a9ea --- /dev/null +++ b/araujo2020/mathematical_formulation.md @@ -0,0 +1,74 @@ +# Original Formulation: Multi-mode Resource-Constrained Multi-Project Scheduling Problem (MMRCMPSP) + +*Source: Strong Bounds for Resource Constrained Project Scheduling: Preprocessing and Cutting Planes, Janniele A. S. Araujo, Haroldo G. Santos, Bernard Gendron, Sanjay Dominik Jena, Samuel S. Brito, Danilo S. Souza, 2019.* + +This is the time-indexed (pulse discrete-time, PDT) integer programming formulation of Section 3.3, which the paper introduces as the definition of the problem and carries into all subsequent (cutting-plane / experimental) sections. It unifies the three variants studied in the paper (SMRCPSP, MMRCPSP, MMRCMPSP). + +## Sets and Indices + +$$\begin{align*} +\mathcal{P} &: \text{set of all projects } p;\\ +\mathcal{J} &: \text{set of all jobs } j;\\ +\mathcal{M}_j &: \text{set of modes available for job } j \in \mathcal{J}, \text{ index } m;\\ +\mathcal{J}_p &: \text{set of jobs belonging to project } p, \quad \mathcal{J}_p \subseteq \mathcal{J}\ \ \forall p \in \mathcal{P};\\ +\mathcal{K} &: \text{set of non-renewable resources, index } k;\\ +\mathcal{R} &: \text{set of renewable resources, index } r;\\ +\mathcal{S} &: \text{set of direct precedence relationships } (j,s) \in \mathcal{J} \times \mathcal{J};\\ +\mathcal{S}_j &: \text{direct successors } s \text{ of job } j \ \text{(used in constraint (11))};\\ +\mathcal{T} \subset \mathbb{Z}^+ &: \text{set of time periods in the planning horizon, index } t;\\ +\mathcal{T}_{jm} \subseteq \mathcal{T} &: \text{time horizon for job } j \in \mathcal{J} \text{ on mode } m \in \mathcal{M}_j \ \text{(after preprocessing).} +\end{align*}$$ + +## Parameters + +$$\begin{align*} +d_{jm} \in \mathbb{Z}^+ &: \text{duration of job } j \text{ on mode } m \in \mathcal{M}_j;\\ +q_{kjm} \in \mathbb{Z}^+ &: \text{amount of non-renewable resource } k \in \mathcal{K} \text{ to execute job } j \text{ on mode } m;\\ +q_{rjm} \in \mathbb{Z}^+ &: \text{amount of renewable resource } r \in \mathcal{R} \text{ to execute job } j \text{ on mode } m;\\ +\breve{q}_k \in \mathbb{Z}^+ &: \text{available amount of non-renewable resource } k \in \mathcal{K};\\ +\breve{q}_r \in \mathbb{Z}^+ &: \text{available amount of renewable resource } r \in \mathcal{R};\\ +\sigma_p \in \mathcal{T} &: \text{release date of project } p;\\ +\lambda_p &: \text{critical path duration (CPD) lower bound of project } p;\\ +a_p \in \mathcal{J}_p &: \text{artificial job representing the end of project } p;\\ +\epsilon > 0 &: \text{small tie-breaking coefficient (value not specified in paper).} +\end{align*}$$ (The horizons $\mathcal{T}$ and $\mathcal{T}_{jm}$ follow from preprocessing Eqs. (4)–(6) via $\alpha=\sum_{p\in\mathcal{P}}(\beta_p-\sigma_p-\lambda_p)$, $\breve{t}=\max_{p\in P}(\sigma_p+\lambda_p+\alpha)$, $\mathcal{T}=\{0,\dots,\breve{t}\}$, and $\mathcal{T}_{jm}=\{\breve{e}^s_j,\dots,\breve{l}^s_{jm}\}$; these only restrict the index sets above.) + +## Decision Variables + +$$\begin{align*} +x_{jmt} \in \{0,1\} &: 1 \text{ if job } j \in \mathcal{J} \text{ is allocated on mode } m \in \mathcal{M}_j \text{ at starting time } t \in \mathcal{T}_{jm};\\ +z_{jmt} \in \{0,1\} &: 1 \text{ if job } j \in \mathcal{J} \text{ on mode } m \in \mathcal{M}_j \text{ is being processed during time } t \in \mathcal{T}_{jm};\\ +h \in \mathbb{Z}^+ &: \text{integer variable used to compute the total makespan (tie-breaker).} +\end{align*}$$ + +## Objective + +$$\begin{equation} +\text{Minimize} \quad +\sum_{p \in \mathcal{P}} \sum_{m \in \mathcal{M}_{a_p}} \sum_{t \in \mathcal{T}_{a_p m}} +\left[ t - (\sigma_p + \lambda_p) \right] x_{a_p m t} \;+\; \epsilon\, h +\tag{7} +\end{equation}$$ + +## Constraints + +$$\begin{align} +& \sum_{m \in \mathcal{M}_j} \sum_{t \in \mathcal{T}_{jm}} x_{jmt} = 1 + && \forall j \in \mathcal{J} \tag{8}\\[4pt] +& \sum_{j \in \mathcal{J}} \sum_{m \in \mathcal{M}_j} \sum_{t \in \mathcal{T}_{jm}} q_{kjm}\, x_{jmt} \le \breve{q}_k + && \forall k \in \mathcal{K} \tag{9}\\[4pt] +& \sum_{j \in \mathcal{J}} \sum_{m \in \mathcal{M}_j} q_{rjm}\, z_{jmt} \le \breve{q}_r + && \forall r \in \mathcal{R},\ \forall t \in \mathcal{T} \tag{10}\\[4pt] +& \sum_{m \in \mathcal{M}_j} \sum_{t \in \mathcal{T}_{jm}} (t + d_{jm})\, x_{jmt} + \;-\; \sum_{z \in \mathcal{M}_s} \sum_{i \in \mathcal{T}_{sz}} i\, x_{szi} \le 0 + && \forall j \in \mathcal{J},\ \forall s \in \mathcal{S}_j \tag{11}\\[4pt] +& z_{jmt} - \sum_{t' = (t - d_{jm} + 1)}^{t} x_{jmt'} = 0 + && \forall j \in \mathcal{J},\ \forall m \in \mathcal{M}_j,\ \forall t \in \mathcal{T}_{jm} \tag{12}\\[4pt] +& h - \sum_{m \in \mathcal{M}_{a_p}} \sum_{t \in \mathcal{T}_{a_p m}} t\, x_{a_p m t} \ge 0 + && \forall p \in \mathcal{P} \tag{13}\\[4pt] +& x_{jmt} \in \{0,1\} + && \forall j \in \mathcal{J},\ \forall m \in \mathcal{M}_j,\ \forall t \tag{14}\\[4pt] +& z_{jmt} \in \{0,1\} + && \forall j \in \mathcal{J},\ \forall m \in \mathcal{M}_j,\ \forall t \in \mathcal{T}_{jm} \tag{15}\\[4pt] +& h \ge 0 \tag{16} +\end{align}$$ diff --git a/araujo2020/problem_description.txt b/araujo2020/problem_description.txt new file mode 100644 index 0000000000000000000000000000000000000000..0e483709e5d1f3598f20db0b5f443a7961a797ef --- /dev/null +++ b/araujo2020/problem_description.txt @@ -0,0 +1,9 @@ +# Problem Description + +A collection of projects must be scheduled over a discrete planning horizon. Each project contains a set of jobs, including two artificial jobs with zero duration and zero resource consumption that mark the project's start and end. Every job belongs to exactly one project and can be executed in one of several available modes, where each mode specifies the job's processing duration and its consumption of each resource. Two types of resources exist: renewable resources, which have a per-period capacity limit that restricts the total consumption of all jobs being processed simultaneously in any single time period, and non-renewable resources, which have a global capacity limit that restricts the total consumption summed over all jobs across the entire horizon regardless of when they are scheduled. Each project has a release date before which none of its jobs may start. Direct precedence relationships between pairs of jobs require that whenever one job must precede another, the predecessor must finish (its start time plus its duration in the chosen mode) no later than the successor's start time. + +The input data specifies the set of projects, the set of jobs with their project membership, the set of available modes per job with associated durations and per-resource consumption values, the capacity of each renewable and non-renewable resource, the release date of each project, the precedence relationships between jobs, and the identity of the artificial end job for each project. A critical path duration is computed for each project using the Critical Path Method while ignoring resource constraints. The earliest start time for each job is determined by forward propagation through the precedence graph using the Critical Path Method. An upper bound on each project's completion time is obtained from any known solution. A quantity called the maximum allowable total project delay is computed as the sum over all projects of the difference between the project's upper-bound completion time and the sum of its release date and its critical path duration. The overall planning horizon extends from time zero through the maximum over all projects of the sum of each project's release date, its critical path duration, and the maximum allowable total project delay. For each job in each mode, the latest allowable start time equals the job's project release date plus the project's critical path duration, minus the total duration of the longest path from that job to the project's artificial end job computed using the shortest-duration modes for all jobs along that path, plus the maximum allowable total project delay. The set of periods at which each job-mode combination may begin spans from that job's earliest start time through its latest allowable start time in that mode. + +The scheduler must choose, for every job, exactly one mode and exactly one starting time from the job's allowable time window for that mode. For each time period and each renewable resource, the sum of the renewable resource consumption over all jobs that are being processed during that period must not exceed the resource's per-period capacity. A job is considered to be processing during a given time period if it was started at some earlier period within its duration window such that processing has not yet completed; specifically, a job started at a particular period in a mode with a particular duration is processing during each period from its start through its start plus its duration minus one. For each non-renewable resource, the total consumption across all jobs (each contributing the consumption of its chosen mode) must not exceed the resource's global capacity. + +The goal is to minimize the total project delay, defined as the sum over all projects of the difference between each project's actual completion time (the start time of its artificial end job) and the sum of its release date and its critical path duration. A secondary tiebreaking term is added: a small positive coefficient multiplied by the overall makespan, where the makespan is at least as large as the completion time of every project's artificial end job. This hierarchical approach first minimizes the aggregate delay beyond the critical-path lower bounds and then, among schedules with the same total delay, prefers those with the smallest makespan. diff --git a/araujo2020/solution_logger.py b/araujo2020/solution_logger.py new file mode 100644 index 0000000000000000000000000000000000000000..5393fa935ce71df9da95cee04fe5199077cb8461 --- /dev/null +++ b/araujo2020/solution_logger.py @@ -0,0 +1,61 @@ +""" +Convergence logger for optimization algorithms. + +Records incumbent solutions with timestamps to a JSONL file. +This module is provided to LLM-generated programs — they only need to call +`log(objective_value)` whenever a better feasible solution is found. + +Usage in generated code: + from solution_logger import SolutionLogger + logger = SolutionLogger(log_path, sense="minimize") # or "maximize" + # ... inside algorithm loop: + logger.log(objective_value) +""" + +import json +import time + + +class SolutionLogger: + def __init__(self, log_path, sense="minimize"): + """ + Args: + log_path: Path to the JSONL output file. + sense: "minimize" or "maximize". + """ + self.log_path = log_path + self.sense = sense + self.start_time = time.time() + self.best_obj = None + self.min_interval = 0.1 # seconds, avoid excessive writes + + self._last_log_time = 0.0 + # Clear the file + with open(self.log_path, "w") as f: + pass + + def log(self, objective_value): + """Record a new incumbent if it improves on the best known.""" + if objective_value is None: + return + + # Check if this is an improvement + if self.best_obj is not None: + if self.sense == "minimize" and objective_value >= self.best_obj: + return + if self.sense == "maximize" and objective_value <= self.best_obj: + return + + elapsed = time.time() - self.start_time + + # Throttle writes + if self.best_obj is not None and elapsed - self._last_log_time < self.min_interval: + self.best_obj = objective_value + return + + self.best_obj = objective_value + self._last_log_time = elapsed + + with open(self.log_path, "a") as f: + f.write(json.dumps({"time": round(elapsed, 3), + "objective_value": objective_value}) + "\n") diff --git a/araujo2020/solution_schema.json b/araujo2020/solution_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..ce7034e43d55695366a2e97b54e67d7a9c719219 --- /dev/null +++ b/araujo2020/solution_schema.json @@ -0,0 +1,11 @@ +{ + "objective_value": " Total project delay, computed as the sum over all projects of the difference between the actual completion time of the project and the sum of its release date and its critical path duration.", + "schedule": [ + { + "job_id": " Identifier of the scheduled job.", + "mode_id": " Execution mode chosen for this job.", + "start_time": " Time period at which this job begins processing." + } + ], + "makespan": " Latest completion time across all projects, used as a tiebreaker among schedules with equal total project delay." +} diff --git a/archetti2007/feasibility_check.py b/archetti2007/feasibility_check.py new file mode 100644 index 0000000000000000000000000000000000000000..06e3b267e371905602fcd9ce411154b2098703ef --- /dev/null +++ b/archetti2007/feasibility_check.py @@ -0,0 +1,716 @@ +#!/usr/bin/env python3 +""" +Feasibility checker for the VMIR-OU (Vendor-Managed Inventory Routing +with Order-Up-To level policy) problem. + +Source: Archetti, Bertazzi, Laporte, Speranza (2007) + "A Branch-and-Cut Algorithm for a Vendor-Managed Inventory-Routing Problem" + Transportation Science 41(3), pp. 382-391. + +Checks all hard constraints (2)-(16) from the mathematical formulation, +plus an obj-consistency check (constraint 16) that recomputes the +objective from the solution variables (B, I, y) and rejects when the +reported objective_value disagrees beyond tolerance. +""" + +import argparse +import json +import sys + +# FRONTIEROR_RELEASE_CONTRACT_V2 +_FRONTIEROR_REQUIRED_SOLUTION_FIELDS = ('deliveries', 'objective_value', 'retailer_inventory', 'routes', 'supplier_inventory') +_FRONTIEROR_NONEMPTY_SOLUTION_FIELDS = () +_FRONTIEROR_DERIVED_FIELD_RULES = () +_FRONTIEROR_CONDITIONAL_FIELD_RULES = () + + +def _frontieror_validate_solution_contract(solution): + """Return a structured rejection when the public solution contract is absent.""" + violations = [] + if not isinstance(solution, dict): + violations.append("solution must be a JSON object") + else: + missing = [ + field + for field in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS + if field not in solution or solution[field] is None + ] + if missing: + violations.append( + "missing required solution field(s): " + ", ".join(missing) + ) + + for discriminator, expected_value, fields in _FRONTIEROR_CONDITIONAL_FIELD_RULES: + if solution.get(discriminator) != expected_value: + continue + conditional_missing = [ + field + for field in fields + if field not in solution or solution[field] is None + ] + if conditional_missing: + violations.append( + "missing conditionally required solution field(s): " + + ", ".join(conditional_missing) + ) + + empty = [ + field + for field in _FRONTIEROR_NONEMPTY_SOLUTION_FIELDS + if field in solution and solution[field] in (None, [], {}) + ] + if empty: + violations.append( + "empty required decision witness field(s): " + ", ".join(empty) + ) + + for target, operation, source, source_key in _FRONTIEROR_DERIVED_FIELD_RULES: + try: + if operation == "assignment_key_set": + reported_set = set(solution[target]) + expected_set = {entry[source_key] for entry in solution[source]} + if reported_set != expected_set: + violations.append( + f"{target}={sorted(reported_set)} does not match the " + f"decision-derived value {sorted(expected_set)}" + ) + continue + + reported = float(solution[target]) + if operation == "length": + expected = float(len(solution[source])) + elif operation == "mapping_value": + expected = float(solution[source][source_key]) + else: + raise ValueError("unknown derived-field operation") + except (KeyError, TypeError, ValueError, OverflowError): + violations.append( + f"cannot derive {target} from decision witness field {source}" + ) + continue + if ( + reported != reported + or expected != expected + or reported in (float("inf"), float("-inf")) + or expected in (float("inf"), float("-inf")) + or abs(reported - expected) > 1e-6 + ): + violations.append( + f"{target}={reported} does not match the decision-derived " + f"value {expected}" + ) + + if "objective_value" in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS: + objective = solution.get("objective_value") + if ( + isinstance(objective, bool) + or not isinstance(objective, (int, float)) + or objective != objective + or objective in (float("inf"), float("-inf")) + ): + violations.append("objective_value must be a finite JSON number") + + if not violations: + return None + return { + "feasible": False, + "violated_constraints": ["solution_schema"], + "violations": violations, + "violation_magnitudes": [], + } + + +def _frontieror_write_unexpected_rejection(error): + """Turn malformed candidate/checker exceptions into a normal rejection.""" + import json as _frontieror_json + import sys as _frontieror_sys + + result_path = None + for index, argument in enumerate(_frontieror_sys.argv): + if argument == "--result_path" and index + 1 < len(_frontieror_sys.argv): + result_path = _frontieror_sys.argv[index + 1] + break + if argument.startswith("--result_path="): + result_path = argument.split("=", 1)[1] + break + if not result_path: + return False + try: + with open(result_path, "w") as result_handle: + _frontieror_json.dump( + { + "feasible": False, + "violated_constraints": ["malformed_solution"], + "violations": [ + "checker rejected malformed candidate: " + + type(error).__name__ + + ": " + + str(error)[:500] + ], + "violation_magnitudes": [], + }, + result_handle, + indent=2, + ) + except Exception: + return False + return True + + +TOL = 1e-5 +EPS = 1e-5 + + +def load_json(path): + with open(path, "r") as f: + return json.load(f) + + +def check_feasibility(instance, solution): + """Check all hard constraints of the VMIR-OU model. + + Returns a dict with feasibility results. + """ + violations = [] + violation_magnitudes = [] + violated_constraint_set = set() + + # ------------------------------------------------------------------- + # Extract instance data + # ------------------------------------------------------------------- + n = instance["num_retailers"] + H = instance["time_horizon"] + C = instance["vehicle_capacity"] + dist = instance["distance_matrix"] + + supplier = instance["supplier"] + B0 = supplier["starting_inventory"] + r0 = supplier["production_rate"] + h0 = supplier["inventory_cost"] + + retailers = {ret["id"]: ret for ret in instance["retailers"]} + r = {s: retailers[s]["demand"] for s in retailers} + U = {s: retailers[s]["max_inventory"] for s in retailers} + I0 = {s: retailers[s]["starting_inventory"] for s in retailers} + h = {s: retailers[s]["inventory_cost"] for s in retailers} + + M = list(range(1, n + 1)) + T = list(range(1, H + 1)) + T_prime = list(range(1, H + 2)) # {1, ..., H+1} + M_prime = list(range(0, n + 1)) # {0, 1, ..., n} + + # ------------------------------------------------------------------- + # Extract solution data + # ------------------------------------------------------------------- + # deliveries[t][s] = x_{s,t} (quantity shipped to retailer s at time t) + sol_deliveries = solution.get("deliveries", {}) + x = {} + for t in T: + t_str = str(t) + period_del = sol_deliveries.get(t_str, {}) + for s in M: + s_str = str(s) + x[s, t] = float(period_del.get(s_str, 0.0)) + + # supplier_inventory[t] = B_t + sol_supplier_inv = solution.get("supplier_inventory", {}) + B = {} + for t in T_prime: + B[t] = float(sol_supplier_inv.get(str(t), 0.0)) + + # retailer_inventory[s][t] = I_{s,t} + sol_retailer_inv = solution.get("retailer_inventory", {}) + I = {} + for s in M: + s_str = str(s) + ret_inv = sol_retailer_inv.get(s_str, {}) + for t in T_prime: + I[s, t] = float(ret_inv.get(str(t), 0.0)) + + # routes[t] = list of edges, each edge = [node_i, node_j, value] + # From these we reconstruct z and y variables. + sol_routes = solution.get("routes", {}) + + # Reconstruct z_{i,t} from solution: + # z_{s,t} = 1 if retailer s is delivered at time t (x_{s,t} > 0) + # z_{0,t} = 1 if any delivery happens at time t + z = {} + for i in M_prime: + for t in T: + z[i, t] = 0 + + for t in T: + t_str = str(t) + # Mark retailers as visited based on deliveries + period_del = sol_deliveries.get(t_str, {}) + for s_str, qty in period_del.items(): + s = int(s_str) + if float(qty) > TOL: + z[s, t] = 1 + # Also mark retailers from route edges + edges = sol_routes.get(t_str, []) + for edge in edges: + for node in edge[:2]: # first two elements are the edge endpoints + if node in range(0, n + 1): + if z.get((node, t)) is not None: + z[node, t] = 1 + # Depot is visited if any retailer is visited + if any(z[s, t] == 1 for s in M): + z[0, t] = 1 + + # Reconstruct y variables from route edges. + # Each edge in routes[t] is [lower_node, higher_node, value]. + # y^t_{i,j} where j < i. + y = {} + for t in T: + for i in M_prime: + for j in M_prime: + if j < i: + y[i, j, t] = 0 + + for t in T: + t_str = str(t) + edges = sol_routes.get(t_str, []) + for edge in edges: + node_a = edge[0] + node_b = edge[1] + val = int(round(edge[2])) if len(edge) > 2 else 1 + i_var = max(node_a, node_b) + j_var = min(node_a, node_b) + if (i_var, j_var, t) in y: + y[i_var, j_var, t] += val + + # ------------------------------------------------------------------- + # Helper to record violations + # ------------------------------------------------------------------- + def record_violation(constraint_idx, lhs_val, rhs_val, violation_amount, msg): + if violation_amount > TOL: + violated_constraint_set.add(constraint_idx) + normalizer = max(abs(rhs_val), EPS) + ratio = violation_amount / normalizer + violations.append(msg) + violation_magnitudes.append({ + "constraint": constraint_idx, + "lhs": float(lhs_val), + "rhs": float(rhs_val), + "raw_excess": float(violation_amount), + "normalizer": float(normalizer), + "ratio": float(ratio), + }) + + # =================================================================== + # Constraint 1 (tag 2): Supplier inventory definition + # B_t = B_{t-1} + r_{0,t-1} - sum_{s in M} x_{s,t-1}, t in T' + # where r_{0,0} = 0 and x_{s,0} = 0 + # =================================================================== + for t in T_prime: + if t == 1: + lhs = B[t] + rhs_val = B0 + else: + r0_prev = r0 # production at t-1 (for t-1 >= 1) + total_shipped_prev = sum(x.get((s, t - 1), 0.0) for s in M) + rhs_val = B[t - 1] + r0_prev - total_shipped_prev + lhs = B[t] + violation_amount = abs(lhs - rhs_val) + record_violation( + 1, lhs, rhs_val, violation_amount, + f"Constraint 1 (Supplier inventory definition) violated at t={t}: " + f"B_{t}={lhs:.4f} != {rhs_val:.4f}" + ) + + # =================================================================== + # Constraint 2 (tag 3): Stockout at supplier + # B_t >= sum_{s in M} x_{s,t}, t in T + # =================================================================== + for t in T: + total_shipped = sum(x.get((s, t), 0.0) for s in M) + lhs = B[t] + rhs_val = total_shipped + violation_amount = max(0.0, rhs_val - lhs) # >= constraint + record_violation( + 2, lhs, rhs_val, violation_amount, + f"Constraint 2 (Supplier stockout) violated at t={t}: " + f"B_{t}={lhs:.4f} < sum_x={total_shipped:.4f}" + ) + + # =================================================================== + # Constraint 3 (tag 4): Retailer inventory definition + # I_{s,t} = I_{s,t-1} + x_{s,t-1} - r_{s,t-1}, s in M, t in T' + # where x_{s,0} = r_{s,0} = 0 + # =================================================================== + for s in M: + for t in T_prime: + if t == 1: + lhs = I[s, t] + rhs_val = I0[s] + else: + x_prev = x.get((s, t - 1), 0.0) + r_prev = r[s] + rhs_val = I[s, t - 1] + x_prev - r_prev + lhs = I[s, t] + violation_amount = abs(lhs - rhs_val) + record_violation( + 3, lhs, rhs_val, violation_amount, + f"Constraint 3 (Retailer inventory definition) violated for " + f"retailer {s} at t={t}: I_{s},{t}={lhs:.4f} != {rhs_val:.4f}" + ) + + # =================================================================== + # Constraint 4 (tag 5): Stockout at retailers + # I_{s,t} >= 0, s in M, t in T' + # =================================================================== + for s in M: + for t in T_prime: + lhs = I[s, t] + rhs_val = 0.0 + violation_amount = max(0.0, -lhs) # >= 0 + record_violation( + 4, lhs, rhs_val, violation_amount, + f"Constraint 4 (Retailer stockout) violated for retailer {s} " + f"at t={t}: I_{s},{t}={lhs:.4f} < 0" + ) + + # =================================================================== + # Constraint 5 (tag 6): Order-up-to lower bound + # x_{s,t} >= U_s * z_{s,t} - I_{s,t}, s in M, t in T + # =================================================================== + for s in M: + for t in T: + lhs = x[s, t] + rhs_val = U[s] * z[s, t] - I[s, t] + violation_amount = max(0.0, rhs_val - lhs) # >= constraint + record_violation( + 5, lhs, rhs_val, violation_amount, + f"Constraint 5 (Order-up-to lower bound) violated for " + f"retailer {s} at t={t}: x={lhs:.4f} < U*z - I = {rhs_val:.4f}" + ) + + # =================================================================== + # Constraint 6 (tag 7): Upper bound on shipping + # x_{s,t} <= U_s - I_{s,t}, s in M, t in T + # =================================================================== + for s in M: + for t in T: + lhs = x[s, t] + rhs_val = U[s] - I[s, t] + violation_amount = max(0.0, lhs - rhs_val) # <= constraint + record_violation( + 6, lhs, rhs_val, violation_amount, + f"Constraint 6 (Shipping upper bound) violated for " + f"retailer {s} at t={t}: x={lhs:.4f} > U - I = {rhs_val:.4f}" + ) + + # =================================================================== + # Constraint 7 (tag 8): Linking x and z + # x_{s,t} <= U_s * z_{s,t}, s in M, t in T + # =================================================================== + for s in M: + for t in T: + lhs = x[s, t] + rhs_val = U[s] * z[s, t] + violation_amount = max(0.0, lhs - rhs_val) # <= constraint + record_violation( + 7, lhs, rhs_val, violation_amount, + f"Constraint 7 (Linking x and z) violated for " + f"retailer {s} at t={t}: x={lhs:.4f} > U*z = {rhs_val:.4f}" + ) + + # =================================================================== + # Constraint 8 (tag 9): Vehicle capacity + # sum_{s in M} x_{s,t} <= C, t in T + # =================================================================== + for t in T: + total_shipped = sum(x.get((s, t), 0.0) for s in M) + lhs = total_shipped + rhs_val = C + violation_amount = max(0.0, lhs - rhs_val) # <= constraint + record_violation( + 8, lhs, rhs_val, violation_amount, + f"Constraint 8 (Vehicle capacity) violated at t={t}: " + f"sum_x={lhs:.4f} > C={rhs_val}" + ) + + # =================================================================== + # Constraint 9 (tag 10): Supplier inclusion in route + # sum_{s in M} x_{s,t} <= C * z_{0,t}, t in T + # =================================================================== + for t in T: + total_shipped = sum(x.get((s, t), 0.0) for s in M) + lhs = total_shipped + rhs_val = C * z[0, t] + violation_amount = max(0.0, lhs - rhs_val) # <= constraint + record_violation( + 9, lhs, rhs_val, violation_amount, + f"Constraint 9 (Supplier inclusion) violated at t={t}: " + f"sum_x={lhs:.4f} > C*z0={rhs_val:.4f}" + ) + + # =================================================================== + # Constraint 10 (tag 11): Degree constraints + # sum_{ji} y^t_{ji} = 2*z_{i,t}, i in M', t in T + # =================================================================== + for i in M_prime: + for t in T: + degree = 0 + for j in M_prime: + if j < i: + degree += y.get((i, j, t), 0) + elif j > i: + degree += y.get((j, i, t), 0) + lhs = degree + rhs_val = 2 * z[i, t] + violation_amount = abs(lhs - rhs_val) + record_violation( + 10, lhs, rhs_val, violation_amount, + f"Constraint 10 (Degree) violated for node {i} at t={t}: " + f"degree={lhs} != 2*z={rhs_val}" + ) + + # =================================================================== + # Constraint 11 (tag 12): Subtour elimination + # sum_{i in S, j in S, j= 1: + adj[i].add(j) + adj[j].add(i) + + # Find retailers connected to depot + depot_connected = set() + for s in visited: + if y.get((s, 0, t), 0) >= 1: + depot_connected.add(s) + + # BFS from depot-connected retailers to find full depot component + full_depot_set = set() + queue = list(depot_connected) + while queue: + node = queue.pop() + if node in full_depot_set: + continue + full_depot_set.add(node) + for neighbor in adj.get(node, set()): + if neighbor not in full_depot_set: + queue.append(neighbor) + + # Find components not connected to depot + remaining = set(visited) - full_depot_set + while remaining: + start = next(iter(remaining)) + component = set() + queue = [start] + while queue: + node = queue.pop() + if node in component: + continue + component.add(node) + remaining.discard(node) + for neighbor in adj.get(node, set()): + if neighbor not in component: + queue.append(neighbor) + + # This is a subtour: compute violation + S = component + # LHS: sum of y edges within S + lhs_val = 0 + for i_s in S: + for j_s in S: + if j_s < i_s: + lhs_val += y.get((i_s, j_s, t), 0) + # RHS: sum z_{it} - max z_{kt} (k in S) + sum_z = sum(z[i_s, t] for i_s in S) + max_z = max(z[k_s, t] for k_s in S) + rhs_val = sum_z - max_z + + violation_amount = max(0.0, lhs_val - rhs_val) + record_violation( + 11, float(lhs_val), float(rhs_val), violation_amount, + f"Constraint 11 (Subtour elimination) violated at t={t}: " + f"subtour among retailers {sorted(S)}" + ) + + # =================================================================== + # Constraint 12 (tag 13): Nonnegativity of x + # x_{s,t} >= 0, s in M, t in T + # =================================================================== + for s in M: + for t in T: + lhs = x[s, t] + rhs_val = 0.0 + violation_amount = max(0.0, -lhs) + record_violation( + 12, lhs, rhs_val, violation_amount, + f"Constraint 12 (Nonnegativity x) violated for " + f"retailer {s} at t={t}: x={lhs:.4f} < 0" + ) + + # =================================================================== + # Constraint 13 (tag 14): Binary y_{ij} for retailer-retailer edges + # y^t_{ij} in {0, 1}, i in M, j in M, j < i, t in T + # =================================================================== + for i in M: + for j in M: + if j < i: + for t in T: + val = y.get((i, j, t), 0) + if val not in (0, 1): + dist_from_binary = min(abs(val - 0), abs(val - 1)) + record_violation( + 13, float(val), 0.0, dist_from_binary, + f"Constraint 13 (Binary y_ij) violated for " + f"edge ({j},{i}) at t={t}: y={val} not in {{0,1}}" + ) + + # =================================================================== + # Constraint 14 (tag 15): y_{i0} in {0, 1, 2} + # y^t_{i0} in {0, 1, 2}, i in M, t in T + # =================================================================== + for i in M: + for t in T: + val = y.get((i, 0, t), 0) + if val not in (0, 1, 2): + dist_from_domain = min(abs(val - 0), abs(val - 1), abs(val - 2)) + record_violation( + 14, float(val), 0.0, dist_from_domain, + f"Constraint 14 (Domain y_i0) violated for " + f"edge (0,{i}) at t={t}: y={val} not in {{0,1,2}}" + ) + + # =================================================================== + # Constraint 15 (tag 16): Binary z + # z_{i,t} in {0, 1}, i in M', t in T + # =================================================================== + for i in M_prime: + for t in T: + val = z[i, t] + if val not in (0, 1): + dist_from_binary = min(abs(val - 0), abs(val - 1)) + record_violation( + 15, float(val), 0.0, dist_from_binary, + f"Constraint 15 (Binary z) violated for " + f"node {i} at t={t}: z={val} not in {{0,1}}" + ) + + # =================================================================== + # Constraint 16 (objective consistency, Tier C anti-exploit): + # The reported objective_value must equal the recomputed + # sum_{t in T'} h_0 * B_t + # + sum_{s in M} sum_{t in T'} h_s * I_{s,t} + # + sum_{i,j in M', j tol: + violated_constraint_set.add(16) + normalizer = max(abs(true_obj), EPS) + ratio = abs_diff / normalizer + msg = ( + f"Constraint 16 (Objective consistency) violated: " + f"reported objective_value={reported} differs from " + f"recomputed h0*sum_B + sum_s h_s*sum_I + sum_t sum_ij c_ij*y={true_obj} " + f"(|diff|={abs_diff:.3g}, tol={tol:.3g})" + ) + violations.append(msg) + violation_magnitudes.append({ + "constraint": 16, + "lhs": float(reported), + "rhs": float(true_obj), + "raw_excess": float(abs_diff), + "normalizer": float(normalizer), + "ratio": float(ratio), + }) + + # ------------------------------------------------------------------- + # Build result + # ------------------------------------------------------------------- + violated_constraints = sorted(violated_constraint_set) + feasible = len(violated_constraints) == 0 + + result = { + "feasible": feasible, + "violated_constraints": violated_constraints, + "violations": violations, + "violation_magnitudes": violation_magnitudes, + } + return result + + +def main(): + parser = argparse.ArgumentParser( + description="Feasibility checker for VMIR-OU (Archetti et al. 2007)") + parser.add_argument("--instance_path", type=str, required=True, + help="Path to the JSON file containing the data instance") + parser.add_argument("--solution_path", type=str, required=True, + help="Path to the JSON file containing the candidate solution") + parser.add_argument("--result_path", type=str, required=True, + help="Path to write the JSON file containing the feasibility result") + args = parser.parse_args() + + instance = load_json(args.instance_path) + solution = load_json(args.solution_path) + _frontieror_contract_result = _frontieror_validate_solution_contract(solution) + if _frontieror_contract_result is not None: + with open(args.result_path, "w") as _frontieror_result_handle: + json.dump( + _frontieror_contract_result, + _frontieror_result_handle, + indent=2, + ) + return + + result = check_feasibility(instance, solution) + + with open(args.result_path, "w") as f: + json.dump(result, f, indent=2) + + if result["feasible"]: + print("Solution is FEASIBLE.") + else: + print(f"Solution is INFEASIBLE. Violated constraints: {result['violated_constraints']}") + for v in result["violations"]: + print(f" - {v}") + + +if __name__ == "__main__": + try: + main() + except Exception as _frontieror_unexpected_error: + if not _frontieror_write_unexpected_rejection( + _frontieror_unexpected_error + ): + raise diff --git a/archetti2007/gurobi_code.py b/archetti2007/gurobi_code.py new file mode 100644 index 0000000000000000000000000000000000000000..3d73bf02673eac142dbee8a9d75eb872af5f9158 --- /dev/null +++ b/archetti2007/gurobi_code.py @@ -0,0 +1,553 @@ +#!/usr/bin/env python3 +""" +Gurobi implementation of the VMIR-OU (Vendor-Managed Inventory Routing +with Order-Up-To level) problem. + +Source: Archetti, Bertazzi, Laporte, Speranza (2007) + "A Branch-and-Cut Algorithm for a Vendor-Managed Inventory-Routing Problem" + Transportation Science 41(3), pp. 382-391. + +This program implements the full MILP model (equations 1-16) with: + - Valid inequalities (17)-(19), (22)-(24) added statically at root + - Subtour elimination constraints (12) separated dynamically via lazy callbacks + - Branching priority: z variables first, then y variables + - Best-bound-first search strategy +""" + +import argparse +import json +import math +import sys +import time +from itertools import combinations + +import gurobipy as gp +from gurobipy import GRB +import os as _os, sys as _sys +# Walk up from this file's directory to find repo root (containing scripts/). +_repo = _os.path.dirname(_os.path.abspath(__file__)) +while _repo != _os.path.dirname(_repo) and not _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _repo = _os.path.dirname(_repo) +if _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _sys.path.insert(0, _repo) +try: + from scripts.utils.gurobi_log_helper import install_gurobi_logger +except ImportError: + def install_gurobi_logger(log_path): # no-op fallback when scripts/ unavailable + pass + + +def load_instance(path): + """Load a VMIR-OU instance from JSON.""" + with open(path, 'r') as f: + data = json.load(f) + return data + + +def find_connected_components(n_retailers, z_vals, y_vals, t): + """ + Given fractional/integer z and y values for time period t, + find connected components among visited retailers using a + support graph approach. Used for subtour elimination separation. + + Returns a list of components (sets of retailer indices 1..n) + that do NOT contain the depot (node 0). + """ + # Build adjacency from y values + visited = set() + for s in range(1, n_retailers + 1): + if z_vals.get((s, t), 0) > 0.5: + visited.add(s) + + if len(visited) <= 1: + return [] + + # Build adjacency list among visited retailers + adj = {s: set() for s in visited} + for s in visited: + # Check edge to depot + # y_{s,0} uses key (s, 0) since s > 0 + pass # depot connections handled separately + + for i in visited: + for j in visited: + if j < i: + val = y_vals.get((i, j, t), 0) + if val > 0.5: + adj[i].add(j) + adj[j].add(i) + + # Check depot connectivity + depot_connected = set() + for s in visited: + # Edge between s and 0: key is (s, 0, t) since s > 0 + val = y_vals.get((s, 0, t), 0) + if val > 0.5: + depot_connected.add(s) + + # BFS/DFS to find components + remaining = set(visited) + components = [] + while remaining: + start = next(iter(remaining)) + component = set() + queue = [start] + while queue: + node = queue.pop() + if node in component: + continue + component.add(node) + remaining.discard(node) + for neighbor in adj.get(node, set()): + if neighbor not in component: + queue.append(neighbor) + components.append(component) + + # Return only components NOT connected to depot + subtour_components = [] + for comp in components: + if not comp.intersection(depot_connected): + subtour_components.append(comp) + + return subtour_components + + +def subtour_callback(model, where): + """ + Lazy constraint callback for subtour elimination. + Implements the Padberg-Rinaldi style separation: + find connected components among visited nodes that don't include depot. + + Uses constraint (12): + sum_{i in S, j in S, j= 0) + x = {} + for s in M: + for t in T: + x[s, t] = model.addVar(lb=0.0, vtype=GRB.CONTINUOUS, name=f"x_{s}_{t}") + + # B[t]: supplier inventory at time t in T' (continuous) + B = {} + for t in T_prime: + B[t] = model.addVar(lb=-GRB.INFINITY, vtype=GRB.CONTINUOUS, name=f"B_{t}") + + # I[s,t]: retailer s inventory at time t in T' (continuous) + I = {} + for s in M: + for t in T_prime: + I[s, t] = model.addVar(lb=-GRB.INFINITY, vtype=GRB.CONTINUOUS, name=f"I_{s}_{t}") + + # z[i,t]: binary, 1 if vertex i is visited at time t + z = {} + for i in M_prime: + for t in T: + z[i, t] = model.addVar(vtype=GRB.BINARY, name=f"z_{i}_{t}") + + # y[i,j,t]: for j < i + # If i,j both in M (retailers): binary {0,1} + # If j=0 (supplier): integer {0,1,2} + y = {} + for t in T: + for i in M_prime: + for j in M_prime: + if j < i: + if j == 0: + # y[i,0,t] in {0,1,2} + y[i, j, t] = model.addVar(lb=0, ub=2, vtype=GRB.INTEGER, + name=f"y_{i}_{j}_{t}") + else: + # y[i,j,t] in {0,1} + y[i, j, t] = model.addVar(vtype=GRB.BINARY, + name=f"y_{i}_{j}_{t}") + + model.update() + + # ===================== + # BRANCHING PRIORITIES + # ===================== + # Priority 1: z variables (higher priority = branched first) + for key in z: + z[key].BranchPriority = 2 + for key in y: + y[key].BranchPriority = 1 + + # ===================== + # OBJECTIVE FUNCTION (1) + # ===================== + obj = gp.LinExpr() + # Supplier inventory cost over T' + for t in T_prime: + obj += h0 * B[t] + # Retailer inventory cost over T' + for s in M: + for t in T_prime: + obj += h[s] * I[s, t] + # Transportation cost over T + for t in T: + for i in M_prime: + for j in M_prime: + if j < i: + obj += c[i, j] * y[i, j, t] + + model.setObjective(obj, GRB.MINIMIZE) + + # ===================== + # CONSTRAINTS + # ===================== + + # (2) Supplier inventory definition: B_t = B_{t-1} + r_{0,t-1} - sum_s x_{s,t-1} + # For t=1: B_1 = B_0 + r_{0,0} - sum_s x_{s,0} = B_0 (since r_{00}=0, x_{s0}=0) + # For t>1: B_t = B_{t-1} + r_0 - sum_s x_{s,t-1} + for t in T_prime: + if t == 1: + model.addConstr(B[t] == B0, name=f"inv_supplier_{t}") + else: + model.addConstr( + B[t] == B[t - 1] + r0 - gp.quicksum(x[s, t - 1] for s in M), + name=f"inv_supplier_{t}") + + # (3) Stockout at supplier: B_t >= sum_s x_{st}, t in T + for t in T: + model.addConstr( + B[t] >= gp.quicksum(x[s, t] for s in M), + name=f"stockout_supplier_{t}") + + # (4) Retailer inventory definition: I_{st} = I_{s,t-1} + x_{s,t-1} - r_{s,t-1} + # For t=1: I_{s,1} = I_{s,0} + x_{s,0} - r_{s,0} = I_{s,0} (x_{s0}=r_{s0}=0) + for s in M: + for t in T_prime: + if t == 1: + model.addConstr(I[s, t] == I0[s], name=f"inv_retailer_{s}_{t}") + else: + model.addConstr( + I[s, t] == I[s, t - 1] + x[s, t - 1] - r[s], + name=f"inv_retailer_{s}_{t}") + + # (5) Stockout at retailers: I_{st} >= 0, s in M, t in T' + for s in M: + for t in T_prime: + model.addConstr(I[s, t] >= 0, name=f"nonneg_inv_{s}_{t}") + + # (6) Order-up-to: x_{st} >= U_s * z_{st} - I_{st} + for s in M: + for t in T: + model.addConstr( + x[s, t] >= U[s] * z[s, t] - I[s, t], + name=f"order_up_to_lb_{s}_{t}") + + # (7) x_{st} <= U_s - I_{st} + for s in M: + for t in T: + model.addConstr( + x[s, t] <= U[s] - I[s, t], + name=f"order_up_to_ub_{s}_{t}") + + # (8) x_{st} <= U_s * z_{st} + for s in M: + for t in T: + model.addConstr( + x[s, t] <= U[s] * z[s, t], + name=f"order_up_to_link_{s}_{t}") + + # (9) Capacity: sum_s x_{st} <= C, t in T + for t in T: + model.addConstr( + gp.quicksum(x[s, t] for s in M) <= C, + name=f"capacity_{t}") + + # (10) Supplier inclusion: sum_s x_{st} <= C * z_{0t} + for t in T: + model.addConstr( + gp.quicksum(x[s, t] for s in M) <= C * z[0, t], + name=f"supplier_link_{t}") + + # (11) Degree constraints: + # sum_{j in M', ji} y^t_{ji} = 2*z_{it} + for i in M_prime: + for t in T: + lhs = gp.LinExpr() + for j in M_prime: + if j < i: + lhs += y[i, j, t] + elif j > i: + lhs += y[j, i, t] + model.addConstr(lhs == 2 * z[i, t], name=f"degree_{i}_{t}") + + # (13) x_{st} >= 0 -- already enforced by variable bounds + + # ===================== + # VALID INEQUALITIES + # ===================== + + # (17) I_{st} >= (1 - z_{st}) * r_{st}, s in M, t in T + for s in M: + for t in T: + model.addConstr( + I[s, t] >= (1 - z[s, t]) * r[s], + name=f"vi17_{s}_{t}") + + # (18) I_{s,t-k} >= (sum_{j=0}^{k} r_{s,t-j}) * (1 - sum_{j=0}^{k} z_{s,t-j}) + # for s in M, t in T, k = 0, 1, ..., t-1 + for s in M: + for t in T: + for k in range(0, t): # k = 0, 1, ..., t-1 + sum_r = sum(r[s] for j in range(k + 1)) # r is constant + sum_r_val = (k + 1) * r[s] + + sum_z = gp.LinExpr() + for j in range(k + 1): + tj = t - j + if tj >= 1: + sum_z += z[s, tj] + + t_minus_k = t - k + if t_minus_k >= 1: + inv_var = I[s, t_minus_k] + elif t_minus_k == 0: + # I_{s,0} is a constant = I0[s], skip adding constraint + # since I0[s] >= sum_r * (1 - sum_z) is always true when + # sum_z could be at most k+1 (all visited) + continue + else: + continue + + model.addConstr( + inv_var >= sum_r_val * (1 - sum_z), + name=f"vi18_{s}_{t}_{k}") + + # (19) I_{st} >= U_s * z_{s,t-k} - sum_{j=t-k}^{t-1} r_{sj} + # for s in M, t in T, k = 1, 2, ..., t-1 + # Valid only for VMIR-OU + for s in M: + for t in T: + for k in range(1, t): # k = 1, ..., t-1 + t_minus_k = t - k + if t_minus_k < 1: + continue + # sum_{j=t-k}^{t-1} r_{sj} = k * r[s] (constant demand) + sum_r_val = k * r[s] + model.addConstr( + I[s, t] >= U[s] * z[s, t_minus_k] - sum_r_val, + name=f"vi19_{s}_{t}_{k}") + + # (22) z_{st} <= z_{0t}, s in M, t in T + for s in M: + for t in T: + model.addConstr(z[s, t] <= z[0, t], name=f"vi22_{s}_{t}") + + # (23) y^t_{i0} <= 2 * z_{it}, i in M, t in T + for i in M: + for t in T: + model.addConstr(y[i, 0, t] <= 2 * z[i, t], name=f"vi23_{i}_{t}") + + # (24) y^t_{ij} <= z_{it}, i in M, j in M, t in T + # Note: y is defined for j < i, so we add for all i,j in M with j < i + for i in M: + for j in M: + if j < i: + for t in T: + model.addConstr(y[i, j, t] <= z[i, t], name=f"vi24_{i}_{j}_{t}") + + # ===================== + # STORE FOR CALLBACK + # ===================== + model._z = z + model._y = y + model._n_retailers = n + model._H = H + + # ===================== + # OPTIMIZE + # ===================== + model.optimize(subtour_callback) + + # ===================== + # EXTRACT SOLUTION + # ===================== + result = { + "objective_value": None, + "status": model.Status, + "solver": "Gurobi", + "problem": "VMIR-OU" + } + + if model.SolCount > 0: + result["objective_value"] = model.ObjVal + + # Extract delivery schedule + deliveries = {} + for t in T: + deliveries[t] = {} + for s in M: + xval = x[s, t].X + if xval > 1e-6: + deliveries[t][s] = round(xval, 4) + result["deliveries"] = {str(k): {str(sk): sv for sk, sv in v.items()} + for k, v in deliveries.items()} + + # Extract routes + routes = {} + for t in T: + route_edges = [] + for i in M_prime: + for j in M_prime: + if j < i: + yval = y[i, j, t].X + if yval > 0.5: + route_edges.append((j, i, round(yval))) + if route_edges: + routes[t] = route_edges + result["routes"] = {str(k): v for k, v in routes.items()} + + # Extract inventory levels + supplier_inv = {} + for t in T_prime: + supplier_inv[t] = round(B[t].X, 4) + result["supplier_inventory"] = {str(k): v for k, v in supplier_inv.items()} + + retailer_inv = {} + for s in M: + retailer_inv[s] = {} + for t in T_prime: + retailer_inv[s][t] = round(I[s, t].X, 4) + result["retailer_inventory"] = { + str(s): {str(t): v for t, v in inv.items()} + for s, inv in retailer_inv.items() + } + + if model.Status == GRB.OPTIMAL: + result["status_str"] = "OPTIMAL" + elif model.Status == GRB.TIME_LIMIT: + result["status_str"] = "TIME_LIMIT" + if hasattr(model, 'MIPGap'): + result["mip_gap"] = model.MIPGap + else: + result["status_str"] = f"STATUS_{model.Status}" + else: + result["status_str"] = "NO_SOLUTION_FOUND" + + return result + + +def main(): + parser = argparse.ArgumentParser( + description="Solve VMIR-OU problem using Gurobi (Archetti et al. 2007)") + parser.add_argument("--instance_path", type=str, required=True, + help="Path to the JSON instance file") + parser.add_argument("--solution_path", type=str, required=True, + help="Path to write the solution JSON file") + parser.add_argument("--time_limit", type=int, required=True, + help="Maximum solver runtime in seconds") + parser.add_argument("--log_path", type=str, default=None, help="Path to log incumbent solutions") + args = parser.parse_args() + install_gurobi_logger(args.log_path) + + data = load_instance(args.instance_path) + result = solve_vmir_ou(data, args.time_limit) + + with open(args.solution_path, 'w') as f: + json.dump(result, f, indent=2, default=str) + + print(f"Solution written to {args.solution_path}") + if result["objective_value"] is not None: + print(f"Objective value: {result['objective_value']}") + print(f"Status: {result['status_str']}") + else: + print("No feasible solution found.") + + +if __name__ == "__main__": + main() diff --git a/archetti2007/gurobi_feasi_result/large_feasi_result_1.json b/archetti2007/gurobi_feasi_result/large_feasi_result_1.json new file mode 100644 index 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b/archetti2007/instance_schema.json @@ -0,0 +1,25 @@ +{ + "num_retailers": " Number of retailers to be served by the supplier.", + "time_horizon": " Number of discrete periods over which deliveries can be scheduled.", + "supplier": { + "id": " Identifier for the supplier node in the distance matrix.", + "x": " Horizontal coordinate of the supplier location.", + "y": " Vertical coordinate of the supplier location.", + "starting_inventory": " Amount of product available at the supplier at the beginning of the planning horizon.", + "production_rate": " Quantity of product produced at the supplier in each period.", + "inventory_cost": " Per-unit cost of holding one unit of product at the supplier for one period." + }, + "retailers": [ + { + "id": " Identifier for this retailer node in the distance matrix.", + "x": " Horizontal coordinate of this retailer's location.", + "y": " Vertical coordinate of this retailer's location.", + "demand": " Quantity of product consumed by this retailer in each period.", + "max_inventory": " Maximum amount of product this retailer can store at any time.", + "starting_inventory": " Amount of product available at this retailer at the beginning of the planning horizon.", + "inventory_cost": " Per-unit cost of holding one unit of product at this retailer for one period." + } + ], + "vehicle_capacity": " Maximum total quantity of product the delivery vehicle can carry in a single period.", + "distance_matrix": " Symmetric transportation cost between each pair of locations, where row and column 0 is the supplier and rows and columns 1 through num_retailers are the retailers." +} diff --git a/archetti2007/mathematical_formulation.md b/archetti2007/mathematical_formulation.md new file mode 100644 index 0000000000000000000000000000000000000000..86258d7ab24af4b868ae8b99d1916134e15fee3a --- /dev/null +++ b/archetti2007/mathematical_formulation.md @@ -0,0 +1,91 @@ +# Original Formulation: Vendor-Managed Inventory-Routing with Order-Up-To Level (VMIR-OU) + +*Source: A Branch-and-Cut Algorithm for a Vendor-Managed Inventory-Routing Problem, Claudia Archetti, Luca Bertazzi, Gilbert Laporte, Maria Grazia Speranza, 2007.* + +## Sets and Parameters + +- $\mathcal{M} = \{1, 2, \ldots, n\}$: set of retailers. + +- Vertex $0$: the common supplier; $\mathcal{M}' = \mathcal{M} \cup \{0\}$ is the set of all vertices. + +- $\mathcal{T} = \{1, \ldots, H\}$: set of discrete time periods (time horizon of length $H$). + +- $\mathcal{T}' = \mathcal{T} \cup \{H+1\}$: extended time horizon (the period $H+1$ is included in the inventory-cost accounting). + +- $\mathcal{S} \subseteq \mathcal{M}$: a generic subset of retailers (used in the subtour elimination constraints). + +- $r_{0t}$: product quantity made available at the supplier at time $t \in \mathcal{T}$, with $r_{00} = 0$. + +- $r_{st}$: product quantity consumed by retailer $s \in \mathcal{M}$ at time $t \in \mathcal{T}$, with $r_{s0} = 0$, $s \in \mathcal{M}$. + +- $B_0$: starting inventory level at the supplier. + +- $U_s$: maximum inventory level at retailer $s \in \mathcal{M}$. + +- $I_{s0}$: starting inventory level at retailer $s \in \mathcal{M}$, with $I_{s0} \leq U_s$. + +- $h_0$: unit inventory cost at the supplier. + +- $h_s$: unit inventory cost at retailer $s \in \mathcal{M}$. + +- $C$: vehicle capacity. + +- $c_{ij}$: transportation cost from vertex $i$ to vertex $j$, symmetric ($c_{ij} = c_{ji}$), $i, j \in \mathcal{M}'$. + +## Decision Variables + +- $x_{st} \geq 0$: quantity shipped to retailer $s \in \mathcal{M}$ at time $t \in \mathcal{T}$ (continuous). + +- $B_t$: inventory level at the supplier at time $t \in \mathcal{T}'$ (continuous). + +- $I_{st}$: inventory level at retailer $s \in \mathcal{M}$ at time $t \in \mathcal{T}'$ (continuous). + +- $z_{it} \in \{0,1\}$: equal to $1$ if vertex $i \in \mathcal{M}'$ is visited at time $t \in \mathcal{T}$, $0$ otherwise. + +- $y^t_{ij}$: equal to $1$ if $j$ immediately follows $i$ in the route traveled at time $t$, $0$ otherwise; binary for retailer–retailer edges, and in $\{0,1,2\}$ for the retailer–supplier edge ($y^t_{i0} = 2$ means a direct round trip serving only retailer $i$). + +## Objective + +$$\begin{equation} +\min \quad \sum_{t \in \mathcal{T}'} h_0 B_t + + \sum_{s \in \mathcal{M}} \sum_{t \in \mathcal{T}'} h_s I_{st} + + \sum_{i \in \mathcal{M}'} \sum_{\substack{j \in \mathcal{M}' \\ j < i}} \sum_{t \in \mathcal{T}} c_{ij}\, y^t_{ij} +\tag{1} +\end{equation}$$ + +## Constraints + +$$\begin{align} +B_t &= B_{t-1} + r_{0,t-1} - \sum_{s \in \mathcal{M}} x_{s,t-1}, + & & t \in \mathcal{T}' \tag{2} \\ +B_t &\geq \sum_{s \in \mathcal{M}} x_{st}, + & & t \in \mathcal{T} \tag{3} \\ +I_{st} &= I_{s,t-1} + x_{s,t-1} - r_{s,t-1}, + & & s \in \mathcal{M},\ t \in \mathcal{T}' \tag{4} \\ +I_{st} &\geq 0, + & & s \in \mathcal{M},\ t \in \mathcal{T}' \tag{5} \\ +x_{st} &\geq U_s z_{st} - I_{st}, + & & s \in \mathcal{M},\ t \in \mathcal{T} \tag{6} \\ +x_{st} &\leq U_s - I_{st}, + & & s \in \mathcal{M},\ t \in \mathcal{T} \tag{7} \\ +x_{st} &\leq U_s z_{st}, + & & s \in \mathcal{M},\ t \in \mathcal{T} \tag{8} \\ +\sum_{s \in \mathcal{M}} x_{st} &\leq C, + & & t \in \mathcal{T} \tag{9} \\ +\sum_{s \in \mathcal{M}} x_{st} &\leq C z_{0t}, + & & t \in \mathcal{T} \tag{10} \\ +\sum_{\substack{j \in \mathcal{M}' \\ j < i}} y^t_{ij} + \sum_{\substack{j \in \mathcal{M}' \\ j > i}} y^t_{ji} &= 2 z_{it}, + & & i \in \mathcal{M}',\ t \in \mathcal{T} \tag{11} \\ +\sum_{i \in \mathcal{S}} \sum_{\substack{j \in \mathcal{S} \\ j < i}} y^t_{ij} &\leq \sum_{i \in \mathcal{S}} z_{it} - z_{kt}, + & & \mathcal{S} \subseteq \mathcal{M},\ t \in \mathcal{T},\ \text{for some } k \in \mathcal{S} \tag{12} \\ +x_{st} &\geq 0, + & & s \in \mathcal{M},\ t \in \mathcal{T} \tag{13} \\ +y^t_{ij} &\in \{0, 1\}, + & & i \in \mathcal{M},\ j \in \mathcal{M},\ j < i,\ t \in \mathcal{T} \tag{14} \\ +y^t_{i0} &\in \{0, 1, 2\}, + & & i \in \mathcal{M},\ t \in \mathcal{T} \tag{15} \\ +z_{it} &\in \{0, 1\}, + & & i \in \mathcal{M}',\ t \in \mathcal{T} \tag{16} +\end{align}$$ + +In constraints (2) and (4), $r_{00} = 0$, $x_{s0} = 0$, and $r_{s0} = 0$ for $s \in \mathcal{M}$. diff --git a/archetti2007/problem_description.txt b/archetti2007/problem_description.txt new file mode 100644 index 0000000000000000000000000000000000000000..314c1f317cc945c20da394795b9d39c79662f5f7 --- /dev/null +++ b/archetti2007/problem_description.txt @@ -0,0 +1,13 @@ +# Problem Description + +A supplier ships a single product to a set of n retailers over a discrete time horizon of H periods. At each period, a known quantity of product is produced or made available at the supplier, and each retailer consumes a known quantity of product. The supplier begins with a given starting inventory, and each retailer has a given starting inventory that does not exceed its maximum inventory level. Each retailer has a maximum inventory level, and the supplier holds product at a per-unit inventory cost per period, while each retailer incurs its own per-unit inventory cost per period. A single vehicle with a fixed capacity is available to deliver product from the supplier to any subset of retailers in each period, traveling a route that starts and ends at the supplier. The symmetric transportation cost between every pair of locations (supplier and retailers) is given. + +The supplier must decide, for each period, which retailers to visit and how much product to ship to each, as well as the route the vehicle follows to serve the visited retailers. Whenever a retailer is visited in a given period, the quantity shipped must exactly equal the difference between the retailer's maximum inventory level and its current inventory level at that time, thereby raising the retailer's inventory to its maximum. If a retailer is not visited in a period, it receives nothing. The total quantity loaded onto the vehicle in any period must not exceed the vehicle's capacity. + +The inventory at the supplier at each period equals the supplier's inventory in the previous period, plus the quantity produced at the supplier in the previous period, minus the total quantity shipped to all retailers in the previous period. In each period of the planning horizon, the supplier's inventory must be at least the total quantity shipped to all retailers in that period. The inventory at each retailer at each period equals the retailer's inventory in the previous period, plus the quantity received from the supplier in the previous period, minus the quantity consumed by the retailer in the previous period. No retailer's inventory may ever fall below zero at any period from one through the end of the extended horizon, which includes one period beyond the last delivery period. Inventory costs at both the supplier and all retailers are assessed over this extended horizon. + +In each period, if any retailer receives a delivery, the vehicle route must include the supplier. The route in each period forms a single tour: the total number of route-segment traversals touching each visited location (supplier and retailers) equals exactly two in that period. The route must not contain any loop among retailers that does not pass through the supplier. Specifically, for any subset of retailers, the number of route segments connecting only pairs of locations within that subset cannot exceed the number of visited retailers in that subset minus the visit status of one chosen retailer from that subset. + +The route segments in each period between two retailers are either used or not used. The route segment between any retailer and the supplier may be traversed zero, one, or two times in a period, the value of two corresponding to a direct round-trip serving a single retailer. A retailer can only appear in a route segment if that retailer is visited in that period, and the supplier-to-retailer segment can only be traversed twice if that retailer is visited. + +The goal is to minimize the total cost, which is the sum of three components: the supplier's inventory holding cost summed over all periods of the extended horizon, the retailers' inventory holding costs summed over all retailers and all periods of the extended horizon, and the transportation costs summed over all route segments used across all delivery periods. diff --git a/archetti2007/solution_logger.py b/archetti2007/solution_logger.py new file mode 100644 index 0000000000000000000000000000000000000000..5393fa935ce71df9da95cee04fe5199077cb8461 --- /dev/null +++ b/archetti2007/solution_logger.py @@ -0,0 +1,61 @@ +""" +Convergence logger for optimization algorithms. + +Records incumbent solutions with timestamps to a JSONL file. +This module is provided to LLM-generated programs — they only need to call +`log(objective_value)` whenever a better feasible solution is found. + +Usage in generated code: + from solution_logger import SolutionLogger + logger = SolutionLogger(log_path, sense="minimize") # or "maximize" + # ... inside algorithm loop: + logger.log(objective_value) +""" + +import json +import time + + +class SolutionLogger: + def __init__(self, log_path, sense="minimize"): + """ + Args: + log_path: Path to the JSONL output file. + sense: "minimize" or "maximize". + """ + self.log_path = log_path + self.sense = sense + self.start_time = time.time() + self.best_obj = None + self.min_interval = 0.1 # seconds, avoid excessive writes + + self._last_log_time = 0.0 + # Clear the file + with open(self.log_path, "w") as f: + pass + + def log(self, objective_value): + """Record a new incumbent if it improves on the best known.""" + if objective_value is None: + return + + # Check if this is an improvement + if self.best_obj is not None: + if self.sense == "minimize" and objective_value >= self.best_obj: + return + if self.sense == "maximize" and objective_value <= self.best_obj: + return + + elapsed = time.time() - self.start_time + + # Throttle writes + if self.best_obj is not None and elapsed - self._last_log_time < self.min_interval: + self.best_obj = objective_value + return + + self.best_obj = objective_value + self._last_log_time = elapsed + + with open(self.log_path, "a") as f: + f.write(json.dumps({"time": round(elapsed, 3), + "objective_value": objective_value}) + "\n") diff --git a/archetti2007/solution_schema.json b/archetti2007/solution_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..085cb730149b6fb9a8437da172fd5fbfe30ec6c7 --- /dev/null +++ b/archetti2007/solution_schema.json @@ -0,0 +1,7 @@ +{ + "objective_value": " Total cost combining the supplier's inventory holding cost over the extended horizon, all retailers' inventory holding costs over the extended horizon, and the transportation costs of all route segments used in delivery periods.", + "deliveries": " dict, key: '{retailer}'> Quantity of product shipped from the supplier to each visited retailer in each delivery period.", + "routes": " list[list[int]]> Route segments used by the vehicle in each delivery period, where each entry [i, j, k] indicates the segment between location i and location j is traversed k times, with k equal to 1 for a segment between two retailers and 1 or 2 for a segment incident to the supplier.", + "supplier_inventory": " Inventory level held at the supplier at each period of the extended horizon.", + "retailer_inventory": " dict, key: '{period}'> Inventory level held at each retailer at each period of the extended horizon." +} diff --git a/armbruster2012/feasibility_check.py b/armbruster2012/feasibility_check.py new file mode 100644 index 0000000000000000000000000000000000000000..e3c5dbe5fc9fbc71aa436d9f1c87b5c33996d177 --- /dev/null +++ b/armbruster2012/feasibility_check.py @@ -0,0 +1,509 @@ +#!/usr/bin/env python3 +""" +Feasibility checker for Minimum Graph Bisection (Armbruster et al. 2012). + +Checks the ILP formulation (Equation 1) constraints: + Constraint 1: sum_{j=2}^{n} f_j * y_{1j} <= F + Constraint 2: f_1 + sum_{j=2}^{n} f_j * (1 - y_{1j}) <= F + Constraint 3: Cycle inequalities (odd subset D of cycle C): + sum_{ij in D} y_{ij} - sum_{ij in C\\D} y_{ij} <= |D| - 1 + Constraint 4: y in {0, 1}^E (binary domain) + Constraint 5: solution["objective_value"] equals the cut cost + sum_{ij in E} w_{ij} * y_{ij} recomputed from the partition. + (Without this, an LLM can report any objective and pass C1-C4.) + +Node indexing: the paper uses 1-based with node 1 as the star center. +The data uses 0-based with node 0 as the star center. +""" + +import argparse +import json +from collections import defaultdict + +# FRONTIEROR_RELEASE_CONTRACT_V2 +_FRONTIEROR_REQUIRED_SOLUTION_FIELDS = ('objective_value', 'partition_S', 'partition_complement') +_FRONTIEROR_NONEMPTY_SOLUTION_FIELDS = () +_FRONTIEROR_DERIVED_FIELD_RULES = () +_FRONTIEROR_CONDITIONAL_FIELD_RULES = () + + +def _frontieror_validate_solution_contract(solution): + """Return a structured rejection when the public solution contract is absent.""" + violations = [] + if not isinstance(solution, dict): + violations.append("solution must be a JSON object") + else: + missing = [ + field + for field in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS + if field not in solution or solution[field] is None + ] + if missing: + violations.append( + "missing required solution field(s): " + ", ".join(missing) + ) + + for discriminator, expected_value, fields in _FRONTIEROR_CONDITIONAL_FIELD_RULES: + if solution.get(discriminator) != expected_value: + continue + conditional_missing = [ + field + for field in fields + if field not in solution or solution[field] is None + ] + if conditional_missing: + violations.append( + "missing conditionally required solution field(s): " + + ", ".join(conditional_missing) + ) + + empty = [ + field + for field in _FRONTIEROR_NONEMPTY_SOLUTION_FIELDS + if field in solution and solution[field] in (None, [], {}) + ] + if empty: + violations.append( + "empty required decision witness field(s): " + ", ".join(empty) + ) + + for target, operation, source, source_key in _FRONTIEROR_DERIVED_FIELD_RULES: + try: + if operation == "assignment_key_set": + reported_set = set(solution[target]) + expected_set = {entry[source_key] for entry in solution[source]} + if reported_set != expected_set: + violations.append( + f"{target}={sorted(reported_set)} does not match the " + f"decision-derived value {sorted(expected_set)}" + ) + continue + + reported = float(solution[target]) + if operation == "length": + expected = float(len(solution[source])) + elif operation == "mapping_value": + expected = float(solution[source][source_key]) + else: + raise ValueError("unknown derived-field operation") + except (KeyError, TypeError, ValueError, OverflowError): + violations.append( + f"cannot derive {target} from decision witness field {source}" + ) + continue + if ( + reported != reported + or expected != expected + or reported in (float("inf"), float("-inf")) + or expected in (float("inf"), float("-inf")) + or abs(reported - expected) > 1e-6 + ): + violations.append( + f"{target}={reported} does not match the decision-derived " + f"value {expected}" + ) + + if "objective_value" in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS: + objective = solution.get("objective_value") + if ( + isinstance(objective, bool) + or not isinstance(objective, (int, float)) + or objective != objective + or objective in (float("inf"), float("-inf")) + ): + violations.append("objective_value must be a finite JSON number") + + if not violations: + return None + return { + "feasible": False, + "violated_constraints": ["solution_schema"], + "violations": violations, + "violation_magnitudes": [], + } + + +def _frontieror_write_unexpected_rejection(error): + """Turn malformed candidate/checker exceptions into a normal rejection.""" + import json as _frontieror_json + import sys as _frontieror_sys + + result_path = None + for index, argument in enumerate(_frontieror_sys.argv): + if argument == "--result_path" and index + 1 < len(_frontieror_sys.argv): + result_path = _frontieror_sys.argv[index + 1] + break + if argument.startswith("--result_path="): + result_path = argument.split("=", 1)[1] + break + if not result_path: + return False + try: + with open(result_path, "w") as result_handle: + _frontieror_json.dump( + { + "feasible": False, + "violated_constraints": ["malformed_solution"], + "violations": [ + "checker rejected malformed candidate: " + + type(error).__name__ + + ": " + + str(error)[:500] + ], + "violation_magnitudes": [], + }, + result_handle, + indent=2, + ) + except Exception: + return False + return True + + + +def load_json(path): + with open(path, "r") as f: + return json.load(f) + + +def extract_partition(instance, solution): + """ + Extract a 0/1 partition array from either efficient or gurobi solution format. + Returns partition array or None if no valid solution exists. + """ + n = instance["num_nodes"] + + # Efficient solution format: has "partition" list + if "partition" in solution and isinstance(solution["partition"], list) and len(solution["partition"]) > 0: + return solution["partition"] + + # Gurobi solution format: has "partition_S" and "partition_complement" + if "partition_S" in solution and "partition_complement" in solution: + if len(solution["partition_S"]) == 0 and len(solution["partition_complement"]) == 0: + return None # No solution (e.g., INFEASIBLE) + partition = [None] * n + for node in solution["partition_S"]: + partition[node] = 0 # Side containing node 0 + for node in solution["partition_complement"]: + partition[node] = 1 # Side separated from node 0 + # Check all nodes assigned + if any(p is None for p in partition): + return None + return partition + + return None + + +def check_feasibility(instance, solution): + """ + Check all hard constraints of the Minimum Graph Bisection ILP. + Returns (feasible, violated_constraints, violations, violation_magnitudes). + """ + tol = 1e-5 + eps = 1e-5 + + n = instance["num_nodes"] + edges = [tuple(e) for e in instance["edges"]] + node_weights = instance["node_weights"] + F = instance["bisection_capacity_F"] + + violated_constraints = set() + violations = [] + violation_magnitudes = [] + + # Extract partition + partition = extract_partition(instance, solution) + if partition is None: + # No solution to check — report all structural constraints violated + violations.append("No valid partition found in solution (e.g., solver returned INFEASIBLE)") + violated_constraints.add(4) + violation_magnitudes.append({ + "constraint": 4, + "lhs": float("nan"), + "rhs": float("nan"), + "raw_excess": float("inf"), + "normalizer": eps, + "ratio": float("inf"), + }) + return (False, + sorted(violated_constraints), + violations, + violation_magnitudes) + + # Build augmented edge set (star from node 0 to all others) + edge_set = set() + for i, j in edges: + edge_set.add((min(i, j), max(i, j))) + for j in range(1, n): + edge_set.add((0, j)) + + # Derive y from partition: y_{ij} = 1 if partition[i] != partition[j] + def y_val(i, j): + return 1 if partition[i] != partition[j] else 0 + + # ------------------------------------------------------------------ + # Constraint 4: Binary domain — y in {0,1}^E + # Check that partition values are all 0 or 1. + # ------------------------------------------------------------------ + non_binary = [] + for i in range(n): + val = partition[i] + if val not in (0, 1) and not (isinstance(val, float) and (abs(val) < tol or abs(val - 1.0) < tol)): + non_binary.append(i) + + if non_binary: + violated_constraints.add(4) + for node in non_binary: + val = partition[node] + violation_amount = min(abs(val - 0), abs(val - 1)) + rhs = 1.0 # closest binary value bound + normalizer = max(abs(rhs), eps) + violations.append( + f"Constraint 4: Node {node} has non-binary partition value {val}" + ) + violation_magnitudes.append({ + "constraint": 4, + "lhs": float(val), + "rhs": float(rhs), + "raw_excess": float(violation_amount), + "normalizer": float(normalizer), + "ratio": float(violation_amount / normalizer), + }) + + # ------------------------------------------------------------------ + # Constraint 1: sum_{j=1}^{n-1} f_j * y_{0,j} <= F + # Weight of nodes separated from node 0 must not exceed F. + # ------------------------------------------------------------------ + lhs_1 = sum(node_weights[j] * y_val(0, j) for j in range(1, n)) + rhs_1 = float(F) + violation_amount_1 = lhs_1 - rhs_1 # positive means violated (LHS > RHS for <=) + + if violation_amount_1 > tol: + violated_constraints.add(1) + normalizer_1 = max(abs(rhs_1), eps) + violations.append( + f"Constraint 1: Weight of nodes separated from node 0 = {lhs_1} exceeds capacity F = {rhs_1}" + ) + violation_magnitudes.append({ + "constraint": 1, + "lhs": float(lhs_1), + "rhs": float(rhs_1), + "raw_excess": float(violation_amount_1), + "normalizer": float(normalizer_1), + "ratio": float(violation_amount_1 / normalizer_1), + }) + + # ------------------------------------------------------------------ + # Constraint 2: f_0 + sum_{j=1}^{n-1} f_j * (1 - y_{0,j}) <= F + # Weight of nodes in the same cluster as node 0 must not exceed F. + # ------------------------------------------------------------------ + lhs_2 = node_weights[0] + sum(node_weights[j] * (1 - y_val(0, j)) for j in range(1, n)) + rhs_2 = float(F) + violation_amount_2 = lhs_2 - rhs_2 + + if violation_amount_2 > tol: + violated_constraints.add(2) + normalizer_2 = max(abs(rhs_2), eps) + violations.append( + f"Constraint 2: Weight of nodes with node 0 = {lhs_2} exceeds capacity F = {rhs_2}" + ) + violation_magnitudes.append({ + "constraint": 2, + "lhs": float(lhs_2), + "rhs": float(rhs_2), + "raw_excess": float(violation_amount_2), + "normalizer": float(normalizer_2), + "ratio": float(violation_amount_2 / normalizer_2), + }) + + # ------------------------------------------------------------------ + # Constraint 3: Cycle inequalities (odd-subset of cycle) + # For binary partitions derived from a valid 0/1 assignment, cycle + # inequalities are always satisfied. We verify via triangle + # inequalities on the augmented graph (triangles are the shortest + # cycles and capture all violations for binary solutions). + # + # For a triangle (a, b, c) with all three edges in the augmented graph: + # |D|=1 forms: y_{ab} - y_{ac} - y_{bc} <= 0 (and permutations) + # |D|=3 form: y_{ab} + y_{ac} + y_{bc} <= 2 + # ------------------------------------------------------------------ + # Build adjacency for augmented graph + adj = defaultdict(set) + for (i, j) in edge_set: + adj[i].add(j) + adj[j].add(i) + + constraint_3_violated = False + # Check triangle inequalities on all triangles in augmented graph + # To avoid O(n^3), iterate over edges and check common neighbors + checked_triangles = set() + for (i, j) in edge_set: + common = adj[i] & adj[j] + for k in common: + tri = tuple(sorted([i, j, k])) + if tri in checked_triangles: + continue + checked_triangles.add(tri) + + a, b, c = tri + y_ab = y_val(a, b) + y_ac = y_val(a, c) + y_bc = y_val(b, c) + + # |D|=1 inequalities (3 forms): + # y_ab - y_ac - y_bc <= 0 + # y_ac - y_ab - y_bc <= 0 + # y_bc - y_ab - y_ac <= 0 + for (d_val, cd_vals, label) in [ + (y_ab, y_ac + y_bc, f"y_{{{a},{b}}} - y_{{{a},{c}}} - y_{{{b},{c}}}"), + (y_ac, y_ab + y_bc, f"y_{{{a},{c}}} - y_{{{a},{b}}} - y_{{{b},{c}}}"), + (y_bc, y_ab + y_ac, f"y_{{{b},{c}}} - y_{{{a},{b}}} - y_{{{a},{c}}}"), + ]: + lhs_val = d_val - cd_vals + rhs_val = 0.0 + excess = lhs_val - rhs_val + if excess > tol: + constraint_3_violated = True + normalizer = max(abs(rhs_val), eps) + if 3 not in violated_constraints: + violated_constraints.add(3) + violations.append( + f"Constraint 3: Triangle ({a},{b},{c}) |D|=1 inequality violated: {label} = {lhs_val} > 0" + ) + violation_magnitudes.append({ + "constraint": 3, + "lhs": float(lhs_val), + "rhs": float(rhs_val), + "raw_excess": float(excess), + "normalizer": float(normalizer), + "ratio": float(excess / normalizer), + }) + + # |D|=3 inequality: y_ab + y_ac + y_bc <= 2 + lhs_d3 = y_ab + y_ac + y_bc + rhs_d3 = 2.0 + excess_d3 = lhs_d3 - rhs_d3 + if excess_d3 > tol: + constraint_3_violated = True + normalizer_d3 = max(abs(rhs_d3), eps) + if 3 not in violated_constraints: + violated_constraints.add(3) + violations.append( + f"Constraint 3: Triangle ({a},{b},{c}) |D|=3 inequality violated: " + f"y_{{{a},{b}}} + y_{{{a},{c}}} + y_{{{b},{c}}} = {lhs_d3} > 2" + ) + violation_magnitudes.append({ + "constraint": 3, + "lhs": float(lhs_d3), + "rhs": float(rhs_d3), + "raw_excess": float(excess_d3), + "normalizer": float(normalizer_d3), + "ratio": float(excess_d3 / normalizer_d3), + }) + + # ------------------------------------------------------------------ + # Constraint 5: objective_value must match the cut cost recomputed + # from the partition. Edge weights are non-negative in this benchmark, + # so the reported objective should equal sum of w_{ij} over cut edges. + # ------------------------------------------------------------------ + if "objective_value" in solution and solution["objective_value"] is not None: + edge_weights = instance.get("edge_weights", []) + if len(edge_weights) == len(edges): + recomputed_obj = sum( + float(edge_weights[k]) + for k, (i, j) in enumerate(edges) + if partition[i] != partition[j] + ) + try: + claimed_obj = float(solution["objective_value"]) + except (TypeError, ValueError): + claimed_obj = None + if claimed_obj is not None: + diff = abs(claimed_obj - recomputed_obj) + # Allow 0.1% relative slack, with a 0.5 absolute floor for + # integer-weight rounding noise. + threshold = max(0.5, 1e-3 * abs(recomputed_obj)) + if diff > threshold: + violated_constraints.add(5) + normalizer = max(abs(recomputed_obj), eps) + violations.append( + f"Constraint 5: objective_value mismatch — reported " + f"{claimed_obj} but recomputed cut cost is " + f"{recomputed_obj} (diff {diff:.4g})" + ) + violation_magnitudes.append({ + "constraint": 5, + "lhs": float(claimed_obj), + "rhs": float(recomputed_obj), + "raw_excess": float(diff), + "normalizer": float(normalizer), + "ratio": float(diff / normalizer), + }) + + feasible = len(violated_constraints) == 0 + return (feasible, + sorted(violated_constraints), + violations, + violation_magnitudes) + + +def main(): + parser = argparse.ArgumentParser( + description="Feasibility checker for Minimum Graph Bisection (Armbruster et al. 2012)" + ) + parser.add_argument( + "--instance_path", type=str, required=True, + help="Path to the JSON file containing the data instance" + ) + parser.add_argument( + "--solution_path", type=str, required=True, + help="Path to the JSON file containing the candidate solution" + ) + parser.add_argument( + "--result_path", type=str, required=True, + help="Path to write the JSON file containing the feasibility result" + ) + args = parser.parse_args() + + instance = load_json(args.instance_path) + solution = load_json(args.solution_path) + _frontieror_contract_result = _frontieror_validate_solution_contract(solution) + if _frontieror_contract_result is not None: + with open(args.result_path, "w") as _frontieror_result_handle: + json.dump( + _frontieror_contract_result, + _frontieror_result_handle, + indent=2, + ) + return + + feasible, violated_constraints, violations, violation_magnitudes = check_feasibility( + instance, solution + ) + + result = { + "feasible": feasible, + "violated_constraints": violated_constraints, + "violations": violations, + "violation_magnitudes": violation_magnitudes, + } + + with open(args.result_path, "w") as f: + json.dump(result, f, indent=2) + + print(f"Feasible: {feasible}") + if not feasible: + print(f"Violated constraints: {violated_constraints}") + for v in violations: + print(f" - {v}") + print(f"Result written to {args.result_path}") + + +if __name__ == "__main__": + try: + main() + except Exception as _frontieror_unexpected_error: + if not _frontieror_write_unexpected_rejection( + _frontieror_unexpected_error + ): + raise diff --git a/armbruster2012/gurobi_code.py b/armbruster2012/gurobi_code.py new file mode 100644 index 0000000000000000000000000000000000000000..7125071cf191dfab873d46722427ee0fa5769a2e --- /dev/null +++ b/armbruster2012/gurobi_code.py @@ -0,0 +1,234 @@ +""" +Minimum Graph Bisection ILP solver using Gurobi. + +Implements the edge-based ILP formulation (Equation 1) from: + Armbruster et al. (2012) - "LP and SDP branch-and-cut algorithms + for the minimum graph bisection problem" + +Triangle (cycle) inequalities on the augmented star graph are added upfront. +Since the augmented graph contains a star K_{1,n-1} from node 0, every pair +of nodes (i, j) with i,j != 0 that share an edge forms a triangle (0, i, j). +Triangle inequalities on this augmented graph, combined with binary variables, +are sufficient to ensure valid cut solutions. +""" + +import argparse +import json +from collections import defaultdict + +import gurobipy as gp +from gurobipy import GRB +import os as _os, sys as _sys +# Walk up from this file's directory to find repo root (containing scripts/). +_repo = _os.path.dirname(_os.path.abspath(__file__)) +while _repo != _os.path.dirname(_repo) and not _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _repo = _os.path.dirname(_repo) +if _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _sys.path.insert(0, _repo) +try: + from scripts.utils.gurobi_log_helper import install_gurobi_logger +except ImportError: + def install_gurobi_logger(log_path): # no-op fallback when scripts/ unavailable + pass +def load_instance(path): + with open(path, "r") as f: + return json.load(f) + + +def solve_bisection(instance, time_limit): + n = instance["num_nodes"] + edges_list = [tuple(e) for e in instance["edges"]] + edge_weights = instance["edge_weights"] + node_weights = instance["node_weights"] + F = instance["bisection_capacity_F"] + + # Build edge set and adjacency structure from original graph + edge_set = set() + edge_cost = {} + for idx, (i, j) in enumerate(edges_list): + key = (min(i, j), max(i, j)) + edge_set.add(key) + edge_cost[key] = edge_weights[idx] + + # Add star edges from node 0 to all other nodes (zero cost if not present) + for j in range(1, n): + key = (0, j) + if key not in edge_set: + edge_set.add(key) + edge_cost[key] = 0 + + # Build adjacency list for the augmented graph + adj = defaultdict(set) + for i, j in edge_set: + adj[i].add(j) + adj[j].add(i) + + # Build neighbor-pair index for triangle enumeration + # For each node, store its neighbors as a set for O(1) lookup + neighbor_set = {v: set(adj[v]) for v in range(n)} + + # Pre-compute all triangles (triples of mutually adjacent nodes) + triangles = [] + for i, j in edge_set: + common = neighbor_set[i] & neighbor_set[j] + for k in common: + tri = tuple(sorted([i, j, k])) + triangles.append(tri) + # Deduplicate + triangles = list(set(triangles)) + + # Create model + model = gp.Model("MinGraphBisection") + model.Params.TimeLimit = time_limit + model.Params.Threads = 1 + + # Create binary edge variables + all_edges = sorted(edge_set) + y = {} + for i, j in all_edges: + y[i, j] = model.addVar(vtype=GRB.BINARY, name=f"y_{i}_{j}", + obj=edge_cost[(i, j)]) + + model.setAttr("ModelSense", GRB.MINIMIZE) + model.update() + + # Helper to get variable for edge (a, b) in canonical order + def yvar(a, b): + if a > b: + a, b = b, a + return y[a, b] + + # Capacity constraint 1: sum_{i=2}^{n} f_i * y_{0,i} <= F + # (weight of cluster separated from node 0) + model.addConstr( + gp.quicksum(node_weights[j] * yvar(0, j) for j in range(1, n)) <= F, + name="cap_separated" + ) + + # Capacity constraint 2: f_0 + sum_{i=2}^{n} f_i * (1 - y_{0,i}) <= F + # (weight of cluster containing node 0) + model.addConstr( + node_weights[0] + gp.quicksum( + node_weights[j] * (1 - yvar(0, j)) for j in range(1, n) + ) <= F, + name="cap_containing" + ) + + # Add all triangle inequalities upfront as regular constraints + for idx_t, (a, b, c) in enumerate(triangles): + vab = yvar(a, b) + vbc = yvar(b, c) + vac = yvar(a, c) + # |D|=1: y_ab - y_ac - y_bc <= 0 (and permutations) + model.addConstr(vab - vac - vbc <= 0, name=f"tri_{idx_t}_D1a") + model.addConstr(vac - vab - vbc <= 0, name=f"tri_{idx_t}_D1b") + model.addConstr(vbc - vab - vac <= 0, name=f"tri_{idx_t}_D1c") + # |D|=3: y_ab + y_ac + y_bc <= 2 + model.addConstr(vab + vac + vbc <= 2, name=f"tri_{idx_t}_D3") + model.update() + + # Optimize (triangle inequalities are already added upfront as regular + # constraints; no lazy constraint callback is needed since the augmented + # graph's triangle inequalities plus binary variables fully characterize + # valid cuts) + model.optimize() + + # Extract solution + result = { + "objective_value": None, + "partition_S": [], + "partition_complement": [], + "status": model.Status, + "num_nodes": n, + } + + if model.SolCount > 0: + result["objective_value"] = model.ObjVal + + # Determine partition from star edge variables y_{0,i} + # y_{0,i} = 1 means node i is in opposite cluster from node 0 + set_with_0 = [0] # Node 0 is always in its own cluster + set_without_0 = [] + + for j in range(1, n): + val = yvar(0, j).X + if val > 0.5: + set_without_0.append(j) + else: + set_with_0.append(j) + + result["partition_S"] = set_with_0 + result["partition_complement"] = set_without_0 + + # Also compute the actual cut value from original edges only + cut_value = 0.0 + for idx, (i, j) in enumerate(edges_list): + key = (min(i, j), max(i, j)) + if key in y: + val = y[key].X + if val > 0.5: + cut_value += edge_weights[idx] + result["cut_value_original_edges"] = cut_value + + if model.Status == GRB.OPTIMAL: + result["status_str"] = "OPTIMAL" + elif model.Status == GRB.TIME_LIMIT: + result["status_str"] = "TIME_LIMIT" + elif model.Status == GRB.INFEASIBLE: + result["status_str"] = "INFEASIBLE" + else: + result["status_str"] = f"OTHER_{model.Status}" + + if hasattr(model, "MIPGap") and model.SolCount > 0: + try: + result["mip_gap"] = model.MIPGap + except Exception: + pass + + if hasattr(model, "ObjBound"): + try: + result["best_bound"] = model.ObjBound + except Exception: + pass + + result["runtime"] = model.Runtime + + return result + + +def main(): + parser = argparse.ArgumentParser( + description="Solve Minimum Graph Bisection via ILP (Armbruster et al. 2012)" + ) + parser.add_argument( + "--instance_path", type=str, required=True, + help="Path to instance JSON file" + ) + parser.add_argument( + "--solution_path", type=str, required=True, + help="Path to output solution JSON file" + ) + parser.add_argument( + "--time_limit", type=int, default=3600, + help="Gurobi time limit in seconds (default: 3600)" + ) + parser.add_argument("--log_path", type=str, default=None, help="Path to log incumbent solutions") + args = parser.parse_args() + install_gurobi_logger(args.log_path) + + instance = load_instance(args.instance_path) + result = solve_bisection(instance, args.time_limit) + + with open(args.solution_path, "w") as f: + json.dump(result, f, indent=2) + + print(f"Status: {result['status_str']}") + print(f"Objective: {result['objective_value']}") + if result["objective_value"] is not None: + print(f"Partition S size: {len(result['partition_S'])}") + print(f"Partition complement size: {len(result['partition_complement'])}") + print(f"Runtime: {result['runtime']:.2f}s") + + +if __name__ == "__main__": + 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b/armbruster2012/instance/tiny_instance_2.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:375c41011ced1e250a8bae476fcc6898c52538119ad33873ca19ef69ba00b821 +size 10996 diff --git a/armbruster2012/instance_schema.json b/armbruster2012/instance_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..0adc2a5014d1fa87522ea9f5290d616e11ad7054 --- /dev/null +++ b/armbruster2012/instance_schema.json @@ -0,0 +1,9 @@ +{ + "num_nodes": " Total number of nodes in the undirected graph.", + "num_edges": " Total number of edges in the undirected graph.", + "bisection_capacity_F": " Maximum allowable sum of node weights in each partition group.", + "total_node_weight": " Sum of all node weights across the graph.", + "node_weights": " Weight of each node, indexed by node identifier.", + "edges": " Pair of endpoint node identifiers for each edge.", + "edge_weights": " Cost associated with cutting each edge, in the same order as edges." +} \ No newline at end of file diff --git a/armbruster2012/mathematical_formulation.md b/armbruster2012/mathematical_formulation.md new file mode 100644 index 0000000000000000000000000000000000000000..ee184e1e432006cb9d6b82e75195edf0df6d4234 --- /dev/null +++ b/armbruster2012/mathematical_formulation.md @@ -0,0 +1,50 @@ +# Original Formulation: Minimum Graph Bisection (MB) + +*Source: LP and SDP branch-and-cut algorithms for the minimum graph bisection problem: a computational comparison, Michael Armbruster, Marzena Fügenschuh, Christoph Helmberg, Alexander Martin, 2012.* + +## Sets and Indices + +$$\begin{align*} +G = (V,E) \quad & \text{undirected graph with node set } V=\{1,\ldots,n\}\\ + & \text{and edge set } E \subseteq \{\{i,j\}:i,j\in V,\; i= self.best_obj: + return + if self.sense == "maximize" and objective_value <= self.best_obj: + return + + elapsed = time.time() - self.start_time + + # Throttle writes + if self.best_obj is not None and elapsed - self._last_log_time < self.min_interval: + self.best_obj = objective_value + return + + self.best_obj = objective_value + self._last_log_time = elapsed + + with open(self.log_path, "a") as f: + f.write(json.dumps({"time": round(elapsed, 3), + "objective_value": objective_value}) + "\n") diff --git a/armbruster2012/solution_schema.json b/armbruster2012/solution_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..7e692b5e9794ba59897313bb2840c77ac950a57f --- /dev/null +++ b/armbruster2012/solution_schema.json @@ -0,0 +1,5 @@ +{ + "objective_value": " Total cost of all edges whose endpoints are assigned to different groups.", + "partition_S": " Node identifiers assigned to the group containing the reference node (node 0).", + "partition_complement": " Node identifiers assigned to the group not containing the reference node." +} \ No newline at end of file diff --git a/arslan2021/feasibility_check.py b/arslan2021/feasibility_check.py new file mode 100644 index 0000000000000000000000000000000000000000..b1f2273df6c4d24452320d2c8b2b455fb8da88f1 --- /dev/null +++ b/arslan2021/feasibility_check.py @@ -0,0 +1,392 @@ +#!/usr/bin/env python3 +""" +Feasibility checker for the benchmark's LP-relaxed Two-Stage Robust Knapsack. + +Based on: Arslan & Detienne (2022), "Decomposition-based approaches for a class of +two-stage robust binary optimization problems", INFORMS Journal on Computing 34(2). + +The benchmark evaluates the LP-relaxed recourse variant of the min-max-min problem: + min_{x in {0,1}^I} sum_i (f_i - p_bar_i) x_i + + max_{xi in Xi} min_{(y,r) in Y(x)} sum_i [(p_hat_i xi_i - f_i) y_i - p_hat_i xi_i r_i] + +The ONLY first-stage decision variable is x in {0,1}^I. The second-stage +(y, r) and the dual variables (u0, v) from the deterministic-equivalent +single-level reformulation (Eq. 10-14) are NOT part of the original +decision space. Any binary x is feasible (relatively complete recourse: +Y(x) always contains (y=0, r=0)). + +Checked constraints: + Constraint 1: x_i in {0,1} for all i [original domain] + Constraint 2: reported objective_value equals the exact fixed-x value of + the deterministic-equivalent LP relaxation. +""" + +import json +import math +import argparse + + +def compute_relaxed_fixed_x_objective(instance, x): + """Solve the benchmark's LP-relaxed recourse problem for fixed x.""" + import numpy as np + from scipy.optimize import linprog + from scipy.sparse import coo_matrix + + items = instance["items"] + item_count = len(items) + params = instance["problem_parameters"] + capacity = float(params["knapsack_capacity"]) + gamma = float(params["uncertainty_budget"]) + r_offset = item_count + u_index = 2 * item_count + v_offset = u_index + 1 + variable_count = 3 * item_count + 1 + + objective = np.zeros(variable_count, dtype=float) + first_stage = 0.0 + for i, item in enumerate(items): + outsource = float(item["outsource_penalty"]) + nominal = float(item["nominal_profit"]) + first_stage += (outsource - nominal) * x[i] + objective[i] = -outsource + objective[v_offset + i] = 1.0 + objective[u_index] = gamma + + rows = [] + columns = [] + values = [] + rhs = [] + + def add_constraint(coefficients, upper): + row = len(rhs) + for column, value in coefficients.items(): + rows.append(row) + columns.append(column) + values.append(value) + rhs.append(float(upper)) + + for i, item in enumerate(items): + degradation = float(item["max_degradation"]) + add_constraint({ + i: degradation, + r_offset + i: -degradation, + u_index: -1.0, + v_offset + i: -1.0, + }, 0.0) + add_constraint({r_offset + i: 1.0, i: -1.0}, 0.0) + capacity_coefficients = {} + for i, item in enumerate(items): + capacity_coefficients[i] = float(item["weight"]) + capacity_coefficients[r_offset + i] = float(item["repair_capacity"]) + add_constraint(capacity_coefficients, capacity) + + matrix = coo_matrix( + (values, (rows, columns)), + shape=(len(rhs), variable_count), + ).tocsr() + bounds = ( + [(0.0, float(x[i])) for i in range(item_count)] + + [(0.0, 1.0) for _ in range(item_count)] + + [(0.0, None)] + + [(0.0, None) for _ in range(item_count)] + ) + result = linprog( + objective, + A_ub=matrix, + b_ub=np.asarray(rhs), + bounds=bounds, + method="highs", + options={"presolve": True}, + ) + if not result.success: + raise RuntimeError( + f"fixed-x relaxed recourse LP failed: {result.message}" + ) + return first_stage + float(result.fun) + +# FRONTIEROR_RELEASE_CONTRACT_V2 +_FRONTIEROR_REQUIRED_SOLUTION_FIELDS = ('objective_value', 'x') +_FRONTIEROR_NONEMPTY_SOLUTION_FIELDS = () +_FRONTIEROR_DERIVED_FIELD_RULES = () +_FRONTIEROR_CONDITIONAL_FIELD_RULES = () + + +def _frontieror_validate_solution_contract(solution): + """Return a structured rejection when the public solution contract is absent.""" + violations = [] + if not isinstance(solution, dict): + violations.append("solution must be a JSON object") + else: + missing = [ + field + for field in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS + if field not in solution or solution[field] is None + ] + if missing: + violations.append( + "missing required solution field(s): " + ", ".join(missing) + ) + + for discriminator, expected_value, fields in _FRONTIEROR_CONDITIONAL_FIELD_RULES: + if solution.get(discriminator) != expected_value: + continue + conditional_missing = [ + field + for field in fields + if field not in solution or solution[field] is None + ] + if conditional_missing: + violations.append( + "missing conditionally required solution field(s): " + + ", ".join(conditional_missing) + ) + + empty = [ + field + for field in _FRONTIEROR_NONEMPTY_SOLUTION_FIELDS + if field in solution and solution[field] in (None, [], {}) + ] + if empty: + violations.append( + "empty required decision witness field(s): " + ", ".join(empty) + ) + + for target, operation, source, source_key in _FRONTIEROR_DERIVED_FIELD_RULES: + try: + if operation == "assignment_key_set": + reported_set = set(solution[target]) + expected_set = {entry[source_key] for entry in solution[source]} + if reported_set != expected_set: + violations.append( + f"{target}={sorted(reported_set)} does not match the " + f"decision-derived value {sorted(expected_set)}" + ) + continue + + reported = float(solution[target]) + if operation == "length": + expected = float(len(solution[source])) + elif operation == "mapping_value": + expected = float(solution[source][source_key]) + else: + raise ValueError("unknown derived-field operation") + except (KeyError, TypeError, ValueError, OverflowError): + violations.append( + f"cannot derive {target} from decision witness field {source}" + ) + continue + if ( + reported != reported + or expected != expected + or reported in (float("inf"), float("-inf")) + or expected in (float("inf"), float("-inf")) + or abs(reported - expected) > 1e-6 + ): + violations.append( + f"{target}={reported} does not match the decision-derived " + f"value {expected}" + ) + + if "objective_value" in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS: + objective = solution.get("objective_value") + if ( + isinstance(objective, bool) + or not isinstance(objective, (int, float)) + or objective != objective + or objective in (float("inf"), float("-inf")) + ): + violations.append("objective_value must be a finite JSON number") + + if not violations: + return None + return { + "feasible": False, + "violated_constraints": ["solution_schema"], + "violations": violations, + "violation_magnitudes": [], + } + + +def _frontieror_write_unexpected_rejection(error): + """Turn malformed candidate/checker exceptions into a normal rejection.""" + import json as _frontieror_json + import sys as _frontieror_sys + + result_path = None + for index, argument in enumerate(_frontieror_sys.argv): + if argument == "--result_path" and index + 1 < len(_frontieror_sys.argv): + result_path = _frontieror_sys.argv[index + 1] + break + if argument.startswith("--result_path="): + result_path = argument.split("=", 1)[1] + break + if not result_path: + return False + try: + with open(result_path, "w") as result_handle: + _frontieror_json.dump( + { + "feasible": False, + "violated_constraints": ["malformed_solution"], + "violations": [ + "checker rejected malformed candidate: " + + type(error).__name__ + + ": " + + str(error)[:500] + ], + "violation_magnitudes": [], + }, + result_handle, + indent=2, + ) + except Exception: + return False + return True + + + +def check_feasibility(instance, solution): + """Check x and the exact objective of the LP-relaxed benchmark. + + The only decision variable in the original problem is the first-stage + x in {0,1}^I. Relatively complete recourse holds (Y(x) contains + (y=0, r=0) for any x), so any binary x is feasible. + + Additionally, the reported objective_value is checked against an + analytic envelope [L_Z, U_Z] computed from x and the instance data + to defend against fabricated/lying objective reports (Tier C + obj-consistency check). + """ + tol = 1e-5 + eps = 1e-5 + + items = instance["items"] + num_items = len(items) + + violations = [] + violation_magnitudes = [] + violated_constraints = set() + + # ---- Parse x (list or dict format) ---- + raw_x = solution.get("x") + if isinstance(raw_x, list): + try: + x = [float(v) for v in raw_x] + except (TypeError, ValueError): + x = None + elif isinstance(raw_x, dict): + try: + x = [float(raw_x[str(i)]) for i in range(num_items)] + except (KeyError, TypeError, ValueError): + x = None + else: + x = None + + # ================================================================== + # Constraint 1: First-stage binary domain x_i in {0,1} for all i + # ================================================================== + if x is None: + violated_constraints.add(1) + violations.append("Solution missing first-stage decision variable 'x'.") + else: + for i in range(num_items): + val = x[i] + nearest = round(val) + if nearest not in (0, 1) or abs(val - nearest) > tol: + violated_constraints.add(1) + nearest_binary = 0.0 if val < 0.5 else 1.0 + va = abs(val - nearest_binary) + violations.append( + f"Binary constraint violated for x_{i}: x_{i} = {val}" + ) + normalizer = max(abs(nearest_binary), eps) + violation_magnitudes.append({ + "constraint": 1, + "lhs": val, + "rhs": nearest_binary, + "raw_excess": va, + "normalizer": normalizer, + "ratio": va / normalizer, + }) + + # ================================================================== + # Constraint 2: exact objective for the benchmark's LP-relaxed recourse. + # ================================================================== + if x is not None and 1 not in violated_constraints: + expected = compute_relaxed_fixed_x_objective(instance, x) + reported = float(solution["objective_value"]) + difference = abs(reported - expected) + tol_obj = max(1e-4, 1e-8 * abs(expected)) + if difference > tol_obj: + violated_constraints.add(2) + violations.append( + f"Objective consistency violated: objective_value={reported} " + f"does not match the exact fixed-x LP-relaxation value " + f"{expected} (|diff|={difference:.4g}, tol={tol_obj:.4g})" + ) + normalizer = max(abs(expected), eps) + violation_magnitudes.append({ + "constraint": 2, + "lhs": reported, + "rhs": expected, + "raw_excess": difference, + "normalizer": normalizer, + "ratio": difference / normalizer, + }) + + return { + "feasible": len(violated_constraints) == 0, + "violated_constraints": sorted(violated_constraints), + "violations": violations, + "violation_magnitudes": violation_magnitudes, + } + + +def main(): + parser = argparse.ArgumentParser( + description="Feasibility checker for Two-Stage Robust Knapsack" + ) + parser.add_argument("--instance_path", type=str, required=True, + help="Path to JSON instance file") + parser.add_argument("--solution_path", type=str, required=True, + help="Path to JSON solution file") + parser.add_argument("--result_path", type=str, required=True, + help="Path to write JSON feasibility result") + args = parser.parse_args() + + with open(args.instance_path) as f: + instance = json.load(f) + with open(args.solution_path) as f: + solution = json.load(f) + _frontieror_contract_result = _frontieror_validate_solution_contract(solution) + if _frontieror_contract_result is not None: + with open(args.result_path, "w") as _frontieror_result_handle: + json.dump( + _frontieror_contract_result, + _frontieror_result_handle, + indent=2, + ) + return + + result = check_feasibility(instance, solution) + + with open(args.result_path, "w") as f: + json.dump(result, f, indent=2) + + if result["feasible"]: + print("Solution is feasible.") + else: + print(f"Solution is INFEASIBLE. " + f"Violated constraints: {result['violated_constraints']}") + for msg in result["violations"]: + print(f" - {msg}") + + +if __name__ == "__main__": + try: + main() + except Exception as _frontieror_unexpected_error: + if not _frontieror_write_unexpected_rejection( + _frontieror_unexpected_error + ): + raise diff --git a/arslan2021/gurobi_code.py b/arslan2021/gurobi_code.py new file mode 100644 index 0000000000000000000000000000000000000000..ae4009cb9fc3161ba2aee3e142f553c4edb83e4b --- /dev/null +++ b/arslan2021/gurobi_code.py @@ -0,0 +1,199 @@ +#!/usr/bin/env python3 +""" +Gurobi MILP implementation of the benchmark's LP-relaxed recourse variant of +the Two-Stage Robust Knapsack Problem. + +Based on: Arslan & Detienne (2022), "Decomposition-based approaches for a class of +two-stage robust binary optimization problems", INFORMS Journal on Computing 34(2). + +Implements formulation (42) / deterministic equivalent (10)-(14) applied to the +knapsack application (Section 4.1, Equation 43). + +Problem (43): + min_{x in {0,1}^I} sum_i (f_i - p_bar_i) x_i + + max_{xi in Xi} min_{(y,r) in Y(x)} sum_i [(p_hat_i xi_i - f_i) y_i - p_hat_i xi_i r_i] + +where: + Y_LP(x) = {(y,r) in [0,1]^{2I} | sum c_i y_i + t_i r_i <= C, y_i <= x_i, r_i <= y_i} + Xi = {xi in R^I_+ | sum xi_i <= Gamma, 0 <= xi_i <= 1} + +Deterministic equivalent approach: + By Proposition 2.1, the inner min over Y(x) = min over conv(Y(x)). + By Proposition 2.4, since linking is y_i <= x_i (H=I, T=-I, d=0), + conv(Y(x)) = Y_bar(x) = conv(Y) intersect {y <= x}. + By minimax theorem: max_xi min_y = min_y max_xi (both sets compact convex, bilinear). + Dualizing max_xi gives the MILP below. + +Benchmark convention: the second-stage binary set is replaced by its elementary +LP relaxation. This is an approximation of the paper's Eq. (43), not a claim +that the elementary relaxation equals the binary knapsack polytope's convex hull. +""" + +import json +import argparse +import gurobipy as gp +from gurobipy import GRB +import os as _os, sys as _sys +# Walk up from this file's directory to find repo root (containing scripts/). +_repo = _os.path.dirname(_os.path.abspath(__file__)) +while _repo != _os.path.dirname(_repo) and not _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _repo = _os.path.dirname(_repo) +if _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _sys.path.insert(0, _repo) +try: + from scripts.utils.gurobi_log_helper import install_gurobi_logger +except ImportError: + def install_gurobi_logger(log_path): # no-op fallback when scripts/ unavailable + pass + + +def load_instance(path): + """Load the knapsack instance from JSON.""" + with open(path) as f: + return json.load(f) + + +def build_and_solve(data, time_limit): + """ + Build and solve the deterministic equivalent MILP. + + The formulation dualizes the inner max over the uncertainty set Xi: + max_{xi >= 0, sum xi <= Gamma, xi <= 1} sum_i p_hat_i (y_i - r_i) xi_i + Dual: min u0 * Gamma + sum v_i s.t. u0 + v_i >= p_hat_i (y_i - r_i), u0, v_i >= 0 + + Full MILP: + min sum_i (f_i - p_bar_i) x_i - sum_i f_i y_i + Gamma u0 + sum_i v_i + s.t. u0 + v_i >= p_hat_i (y_i - r_i) for all i [dual feasibility] + sum_i c_i y_i + t_i r_i <= C [knapsack capacity] + r_i <= y_i for all i [repair requires production] + y_i <= x_i for all i [linking first/second stage] + x_i in {0,1} for all i + 0 <= y_i <= 1, 0 <= r_i <= 1 for all i [LP relaxation of conv(Y)] + u0 >= 0, v_i >= 0 for all i + """ + items = data['items'] + I = len(items) + C = data['problem_parameters']['knapsack_capacity'] + Gamma = data['problem_parameters']['uncertainty_budget'] + + # Extract parameters + weight = [item['weight'] for item in items] + p_bar = [item['nominal_profit'] for item in items] + p_hat = [item['max_degradation'] for item in items] + f_out = [item['outsource_penalty'] for item in items] + t_rep = [item['repair_capacity'] for item in items] + + model = gp.Model("TwoStageRobustKnapsack") + model.setParam("Threads", 1) + model.setParam("TimeLimit", time_limit) + + # --- Decision variables --- + # First-stage: x_i = 1 if item i is selected for production + x = model.addVars(I, vtype=GRB.BINARY, name="x") + + # Second-stage (LP relaxation of conv(Y)): + # y_i: fraction of item i produced as-is + # r_i: fraction of item i repaired + y = model.addVars(I, lb=0.0, ub=1.0, vtype=GRB.CONTINUOUS, name="y") + r = model.addVars(I, lb=0.0, ub=1.0, vtype=GRB.CONTINUOUS, name="r") + + # Dual variables for uncertainty set + # u0: dual for sum_i xi_i <= Gamma + # v_i: dual for xi_i <= 1 + u0 = model.addVar(lb=0.0, vtype=GRB.CONTINUOUS, name="u0") + v = model.addVars(I, lb=0.0, vtype=GRB.CONTINUOUS, name="v") + + # --- Objective --- + # min sum_i (f_i - p_bar_i) x_i [first-stage cost] + # + sum_i (-f_i) y_i [nominal second-stage cost] + # + Gamma * u0 + sum_i v_i [worst-case uncertainty premium via LP duality] + model.setObjective( + gp.quicksum((f_out[i] - p_bar[i]) * x[i] for i in range(I)) + + gp.quicksum(-f_out[i] * y[i] for i in range(I)) + + Gamma * u0 + + gp.quicksum(v[i] for i in range(I)), + GRB.MINIMIZE + ) + + # --- Constraints --- + + # Dual feasibility: u0 + v_i >= p_hat_i (y_i - r_i) for all i + # From LP duality of: max_{xi in Xi} sum_i p_hat_i xi_i (y_i - r_i) + for i in range(I): + model.addConstr(u0 + v[i] >= p_hat[i] * (y[i] - r[i]), + name=f"dual_feas_{i}") + + # Knapsack capacity constraint (from Y): + # sum_i c_i y_i + t_i r_i <= C + model.addConstr( + gp.quicksum(weight[i] * y[i] + t_rep[i] * r[i] for i in range(I)) <= C, + name="knapsack_cap" + ) + + # Repair requires production: r_i <= y_i for all i (from Y) + for i in range(I): + model.addConstr(r[i] <= y[i], name=f"repair_req_{i}") + + # Linking constraint: y_i <= x_i for all i (from Y(x)) + # This couples first-stage selection with second-stage production + for i in range(I): + model.addConstr(y[i] <= x[i], name=f"linking_{i}") + + # --- Solve --- + model.optimize() + + # --- Extract solution --- + solution = {"solver_status": model.Status} + + if model.SolCount > 0: + solution["objective_value"] = model.ObjVal + # Original formulation (Eq. 43) has first-stage binary x only; the + # second-stage (y, r) and dual variables (u0, v) are artifacts of the + # deterministic-equivalent single-level reformulation with LP duality. + # They are NOT part of the original problem's decision space, so they + # are not exported. + solution["x"] = {str(i): int(round(x[i].X)) for i in range(I)} + + if model.Status == GRB.OPTIMAL: + solution["optimality_gap"] = 0.0 + else: + solution["optimality_gap"] = model.MIPGap + + # Interpret solution + selected_items = [i for i in range(I) if round(x[i].X) == 1] + solution["selected_items"] = selected_items + else: + solution["objective_value"] = None + + return solution + + +def main(): + parser = argparse.ArgumentParser( + description="Two-Stage Robust Knapsack - Gurobi deterministic equivalent MILP" + ) + parser.add_argument("--instance_path", type=str, required=True, + help="Path to JSON instance file") + parser.add_argument("--solution_path", type=str, required=True, + help="Path for output solution JSON") + parser.add_argument("--time_limit", type=int, required=True, + help="Maximum solver runtime in seconds") + parser.add_argument("--log_path", type=str, default=None, help="Path to log incumbent solutions") + args = parser.parse_args() + install_gurobi_logger(args.log_path) + + data = load_instance(args.instance_path) + solution = build_and_solve(data, args.time_limit) + + with open(args.solution_path, 'w') as f: + json.dump(solution, f, indent=2) + + if solution["objective_value"] is not None: + print(f"Objective value: {solution['objective_value']:.6f}") + print(f"Status: {solution['solver_status']}") + else: + print("No feasible solution found.") + + +if __name__ == 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b/arslan2021/instance_schema.json @@ -0,0 +1,16 @@ +{ + "problem_parameters": { + "knapsack_capacity": " Total capacity of the knapsack available for production and repair activities.", + "uncertainty_budget": " Maximum total degradation the adversary can impose across all items." + }, + "items": [ + { + "id": " Unique identifier for the item.", + "weight": " Capacity consumed when the item is produced.", + "nominal_profit": " Profit earned from the item when there is no degradation.", + "max_degradation": " Worst-case profit reduction for the item when its degradation factor equals one.", + "outsource_penalty": " Cost penalty incurred when the item is outsourced instead of produced in-house.", + "repair_capacity": " Additional capacity consumed when the item is repaired to recover its full nominal profit." + } + ] +} diff --git a/arslan2021/mathematical_formulation.md b/arslan2021/mathematical_formulation.md new file mode 100644 index 0000000000000000000000000000000000000000..8824b307f2f524d36295db27daa16e4f5dcdff95 --- /dev/null +++ b/arslan2021/mathematical_formulation.md @@ -0,0 +1,75 @@ +# Original Formulation (General Min-Max-Min, Eq. 1) + +## Sets, Dimensions, and Parameters + +- Dimensions: $N = p + p'$ first-stage vars; $N_1$ binary first-stage; $M = q + q'$ second-stage vars; $M_1$ binary second-stage; $S$ dimension of uncertainty vector $\xi$; $S'$ rows of $A$. + +- First-stage data: $G \in \mathbb{Z}^{N' \times N}$, $g \in \mathbb{Z}^{N'}$, $c \in \mathbb{Z}^N$. + +- Second-stage data: $E \in \mathbb{Z}^{M'' \times M}$, $e \in \mathbb{Z}^{M''}$, $f \in \mathbb{Z}^M$, $Q \in \mathbb{Z}^{M \times S}$. + +- Linking-constraint data: $H \in \mathbb{Z}^{M' \times M}$, $T \in \mathbb{Z}^{M' \times N_1}$, $d \in \mathbb{Z}^{M'}$. + +- Uncertainty-set data: $A \in \mathbb{Z}^{S' \times S}$, $b \in \mathbb{Z}^{S'}$. + +## Feasible Regions + +$$\begin{align*} +X & = \{\, x \in \{0,1\}^{N_1} \times \mathbb{R}^{N - N_1}_+ \;:\; G x \leq g \,\}, \\ +Y & = \{\, y \in \{0,1\}^{M_1} \times \mathbb{R}^{M - M_1}_+ \;:\; E y \leq e \,\}, \\ +Y(x) & = \{\, y \in Y \;:\; H y \leq d - T x_1 \,\}, \\ +\Xi & = \{\, \xi \in \mathbb{R}^{S} \;:\; A \xi \leq b \,\}. +\end{align*}$$ + +## Original Problem + +$$\begin{align} +\min_{x \in X} \; c^\top x \;+\; \max_{\xi \in \Xi} \; \min_{y \in Y(x)} \; (f + Q\xi)^\top y + \tag{1} +\end{align}$$ + +At the first stage, $x$ is chosen before $\xi$ is observed. An adversary then picks $\xi \in \Xi$ to maximize the second-stage cost. Finally, the decision maker reacts by choosing $y \in Y(x)$ that minimizes $(f + Q\xi)^\top y$. Both $X$ and $Y$ are bounded mixed-binary sets. + +# Benchmark Application: LP-Relaxed Two-Stage Robust Knapsack + +The original paper's Eq. (43) has binary recourse. The released benchmark +uses the explicit LP relaxation below, replacing the binary domains of +$(y,r)$ by their unit intervals. This distinction is material and defines the +objective values in the bundled references. + +## Sets and Parameters + +- $I = \{1, \ldots, I\}$: set of items. + +- $c_i$: weight (capacity requirement) of item $i$. + +- $\bar{p}_i$: expected profit of item $i$; $\hat{p}_i$: maximum profit degradation. + +- $f_i$: outsourcing cost/penalty; $t_i$: additional repair capacity for item $i$. + +- $C$: total knapsack capacity; $\Gamma$: uncertainty budget. + +## Uncertainty Set + +$$\begin{align*} +\Xi \;=\; \Big\{\, \xi \in \mathbb{R}^{I}_+ \;:\; \sum_{i \in I} \xi_i \leq \Gamma,\; 0 \leq \xi_i \leq 1 \ \forall i \in I \,\Big\}. +\end{align*}$$ + +## Decision Variables + +First-stage: $x_i \in \{0,1\}$, $x_i = 1$ iff item $i$ is selected for production. +Second-stage (in $Y_{LP}(x)$): $0\le y_i\le1$ (fraction produced as-is), $0\le r_i\le1$ (fraction repaired). $$\begin{align*} +Y_{LP}(x) \;=\; \Big\{ (y, r) \in [0,1]^I \times [0,1]^I \;:\; + & \sum_{i \in I} (c_i y_i + t_i r_i) \leq C,\; + y_i \leq x_i\ \forall i,\; + r_i \leq y_i\ \forall i \Big\}. +\end{align*}$$ + +## LP-Relaxed Min-Max-Min Problem + +$$\begin{align} +\min_{x \in \{0,1\}^I}\; \sum_{i \in I} (f_i - \bar{p}_i)\, x_i + \;+\; \max_{\xi \in \Xi}\; \min_{(y,r) \in Y_{LP}(x)}\; + \sum_{i \in I} \Big[\, (\hat{p}_i\, \xi_i - f_i)\, y_i \;-\; \hat{p}_i\, \xi_i\, r_i \,\Big] + \tag{43-LP} +\end{align}$$ diff --git a/arslan2021/problem_description.txt b/arslan2021/problem_description.txt new file mode 100644 index 0000000000000000000000000000000000000000..dee7f22a90908b0100269d4089eab179f1b32c61 --- /dev/null +++ b/arslan2021/problem_description.txt @@ -0,0 +1,11 @@ +# Problem Description + +A company manages a collection of items, each characterized by a weight, a nominal profit, a maximum profit degradation, an outsourcing penalty cost, and a repair capacity requirement. The company has a knapsack with a fixed total capacity. In a first planning phase, before any uncertainty is revealed, the company must decide which items to select for potential in-house production. This benchmark then uses the LP-relaxed recourse approximation of the binary model: after an adversarial degradation is revealed, the fractions produced as-is and repaired may take any value between zero and one. Items not selected in the first phase cannot be produced or repaired. This relaxation is the problem evaluated by the supplied model, checker, and reference objectives; it should not be interpreted as the original binary-recourse Eq. (43). + +The uncertainty in profit degradation is governed by a vector of degradation factors, one per item, each ranging from zero to one. The total sum of all degradation factors across items is limited by an uncertainty budget. The actual profit of an item produced as-is equals its nominal profit minus the product of its maximum degradation and the corresponding degradation factor. An item that is repaired recovers its full nominal profit regardless of the degradation factor. An item that is outsourced yields its nominal profit minus its outsourcing penalty, regardless of degradation. The adversary chooses the degradation factors to inflict the worst possible outcome on the company, subject to the budget and per-item bounds. + +The input data specifies the number of items, and for each item: its weight (consumption of knapsack capacity when produced), its nominal profit, its maximum profit degradation (the worst-case profit reduction when the degradation factor equals one), its outsourcing penalty cost, and the additional capacity consumed when that item is repaired instead of produced as-is. The data also specifies the total knapsack capacity and the uncertainty budget controlling the total allowable degradation across all items. + +The company must choose which items to select in the first phase. The second-stage production and repair variables are continuous fractions in [0,1]. A repaired fraction cannot exceed the produced fraction. The total capacity consumed by production and repair together must not exceed the knapsack capacity: each produced fraction consumes its proportional item weight, and each repaired fraction consumes its proportional additional repair capacity. Only items selected in the first phase may receive a positive production or repair fraction. + +The goal is to minimize the total worst-case cost. This cost is the sum of two components. The first component, determined before uncertainty is revealed, equals the sum over all selected items of the difference between each item's outsourcing penalty and its nominal profit. The second component, determined after the adversary reveals the degradation, equals the sum over all produced items of that item's maximum degradation multiplied by its degradation factor minus that item's outsourcing penalty, minus the sum over all repaired items of that item's maximum degradation multiplied by its degradation factor. Equivalently, selecting an item incurs a baseline cost of outsourcing penalty minus nominal profit; producing that item as-is then offsets the outsourcing penalty but exposes it to degradation; repairing it further offsets the degradation exposure. The adversary maximizes this second-stage cost over the uncertainty set, and the company minimizes against the adversary's worst case. diff --git a/arslan2021/solution_logger.py b/arslan2021/solution_logger.py new file mode 100644 index 0000000000000000000000000000000000000000..5393fa935ce71df9da95cee04fe5199077cb8461 --- /dev/null +++ b/arslan2021/solution_logger.py @@ -0,0 +1,61 @@ +""" +Convergence logger for optimization algorithms. + +Records incumbent solutions with timestamps to a JSONL file. +This module is provided to LLM-generated programs — they only need to call +`log(objective_value)` whenever a better feasible solution is found. + +Usage in generated code: + from solution_logger import SolutionLogger + logger = SolutionLogger(log_path, sense="minimize") # or "maximize" + # ... inside algorithm loop: + logger.log(objective_value) +""" + +import json +import time + + +class SolutionLogger: + def __init__(self, log_path, sense="minimize"): + """ + Args: + log_path: Path to the JSONL output file. + sense: "minimize" or "maximize". + """ + self.log_path = log_path + self.sense = sense + self.start_time = time.time() + self.best_obj = None + self.min_interval = 0.1 # seconds, avoid excessive writes + + self._last_log_time = 0.0 + # Clear the file + with open(self.log_path, "w") as f: + pass + + def log(self, objective_value): + """Record a new incumbent if it improves on the best known.""" + if objective_value is None: + return + + # Check if this is an improvement + if self.best_obj is not None: + if self.sense == "minimize" and objective_value >= self.best_obj: + return + if self.sense == "maximize" and objective_value <= self.best_obj: + return + + elapsed = time.time() - self.start_time + + # Throttle writes + if self.best_obj is not None and elapsed - self._last_log_time < self.min_interval: + self.best_obj = objective_value + return + + self.best_obj = objective_value + self._last_log_time = elapsed + + with open(self.log_path, "a") as f: + f.write(json.dumps({"time": round(elapsed, 3), + "objective_value": objective_value}) + "\n") diff --git a/arslan2021/solution_schema.json b/arslan2021/solution_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..639877a8409c0a46d09ab4d8967dfbb4caad1f14 --- /dev/null +++ b/arslan2021/solution_schema.json @@ -0,0 +1,4 @@ +{ + "objective_value": " Total worst-case cost combining first-stage selection costs and the adversarial worst-case second-stage production/repair/outsourcing costs.", + "x": " Whether each item is selected for potential in-house production in the first stage (1 = selected, 0 = not selected)." +} diff --git a/audit/CHANGE_MANIFEST_180.json b/audit/CHANGE_MANIFEST_180.json new file mode 100644 index 0000000000000000000000000000000000000000..6c0ca7a5f8351a0ed9e053f545111d9459b758f7 --- /dev/null +++ b/audit/CHANGE_MANIFEST_180.json @@ -0,0 +1,3326 @@ +{ + "source_commit": "37ccd8b6dca3bf7f4e0c58941a6ed156832a6d9e", + "changed_file_count": 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false, + "checker_changed": true, + "instance_changed": false, + "solution_changed": true, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 2, + "changed_files": [ + "feasibility_check.py", + "gurobi_solution/large_solution_1.json" + ] + }, + "wu2018": { + "prompt_changed": false, + "formulation_changed": false, + "model_changed": false, + "checker_changed": true, + "instance_changed": false, + "solution_changed": true, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 2, + "changed_files": [ + "feasibility_check.py", + "gurobi_solution/large_solution_1.json" + ] + }, + "wu2022": { + "prompt_changed": false, + "formulation_changed": false, + "model_changed": false, + "checker_changed": true, + "instance_changed": false, + "solution_changed": true, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 2, + "changed_files": [ + "feasibility_check.py", + "gurobi_solution/large_solution_1.json" + ] + }, + "wu2023": { + "prompt_changed": true, + "formulation_changed": true, + "model_changed": true, + "checker_changed": true, + "instance_changed": false, + "solution_changed": true, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 11, + "changed_files": [ + "feasibility_check.py", + "gurobi_code.py", + "gurobi_solution/large_solution_1.json", + "gurobi_solution/large_solution_2.json", + "gurobi_solution/large_solution_3.json", + "gurobi_solution/large_solution_4.json", + "gurobi_solution/large_solution_5.json", + "gurobi_solution/tiny_solution.json", + "mathematical_formulation.md", + "problem_description.txt", + "solution_schema.json" + ] + }, + "xie2019": { + "prompt_changed": true, + "formulation_changed": false, + "model_changed": false, + "checker_changed": true, + "instance_changed": false, + "solution_changed": false, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 2, + "changed_files": [ + "feasibility_check.py", + "problem_description.txt" + ] + }, + "xu2024": { + "prompt_changed": false, + "formulation_changed": false, + "model_changed": false, + "checker_changed": true, + "instance_changed": false, + "solution_changed": false, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 1, + "changed_files": [ + "feasibility_check.py" + ] + }, + "yang2013": { + "prompt_changed": false, + "formulation_changed": false, + "model_changed": false, + "checker_changed": true, + "instance_changed": false, + "solution_changed": false, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 1, + "changed_files": [ + "feasibility_check.py" + ] + }, + "zetina2019": { + "prompt_changed": false, + "formulation_changed": false, + "model_changed": false, + "checker_changed": true, + "instance_changed": false, + "solution_changed": false, + "stored_result_changed": false, + 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true, + "instance_changed": false, + "solution_changed": true, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 3, + "changed_files": [ + "feasibility_check.py", + "gurobi_code.py", + "gurobi_solution/large_solution_1.json" + ] + } + } +} diff --git a/audit/FINAL_AUDIT_180.json b/audit/FINAL_AUDIT_180.json new file mode 100644 index 0000000000000000000000000000000000000000..d3bb2131a5ba504a1bd55fb6905845808a74c55a --- /dev/null +++ b/audit/FINAL_AUDIT_180.json @@ -0,0 +1,8041 @@ +{ + "generated_at": "2026-08-29T22:03:00.757677-04:00", + "source_commit": "37ccd8b6dca3bf7f4e0c58941a6ed156832a6d9e", + "method": { + "eval_ready_gate": "all official instance/solution pairs accepted; stored results match; evaluator objective preflight passes; mutation audit is raw-clear or has an explicit manually reviewed alternative-certificate disposition", + "global_optimality_warning": "Reference feasibility and objective binding do not imply every incumbent has a global zero-gap certificate." + }, + "summary": { + "directory_count": 180, + "eval_ready_directory_count": 180, + "canonical_independent_case_count": 179, + "legacy_alias_count": 1, + "legacy_aliases": [ + "ostrowski2012" + ], + "official_pair_count": 1095, + "official_outcome_counts": { + "accepted": 1095 + }, + "official_orphan_instance_count": 0, + "stored_comparison_counts": { + "match": 1095 + }, + "evaluator_preflight_counts": { + "passed": 180 + }, + "mutation_raw_counts": { + "mutation_clear": 178, + "flagged": 2 + }, + "mutation_manual_clear_count": 2, + "mutation_effective_clear_count": 180, + "static_heuristic_counts": { + "flagged": 137, + "static_clear": 43 + }, + "reference_certification": { + "case_count": 180, + "reference_count": 1097, + "classification_counts": { + "explicit_incumbent": 398, + "solver_optimal": 474, + "solver_optimal_with_tolerance": 77, + "objective_without_certificate": 140, + "ambiguous_status": 8 + }, + "cases_with_unproven_reference": 128, + "unproven_reference_count": 546, + "cases_with_invalid_or_nonfinite_reference": 0, + "reference_without_instance_count": 2, + "cases_with_reference_without_instance": 1 + }, + "prompt_changed_case_count": 16, + "prompt_changed_cases": [ + "arslan2021", + "byeon2022", + "dayarian2019", + "earl2005", + "fischetti2017", + "he2020", + "legrain2024", + "liao2020", + "lozano2017", + "ostrowski2012", + "potthoff2010", + "rahmaniani2022", + "taninmis2022", + "wu2023", + "xie2019", + "zetina2020" + ], + "instance_changed_case_count": 8, + "instance_changed_cases": [ + "bard2002", + "byeon2022", + "kress2019", + "legrain2024", + "lozano2017", + "oliveira2020", + "potthoff2010", + "rahmaniani2022" + ], + "original_audited_92_count": 92, + "original_audited_92_prompt_changed_count": 0, + "original_audited_92_instance_changed_count": 0, + "changed_file_count": 488, + "root_changed_files": [ + "REPAIR_92.md", + "release_subset_92.json" + ], + "release_exclusion_note": "Two legacy segundo2019 solution artifacts without matching instances remain in the repair worktree for provenance and are excluded from the materialized release.", + "completion_interpretation": "All 180 directory IDs are evaluator-ready under their disclosed released contracts. They represent 179 independent benchmark cases plus one transparent legacy alias; feasible baselines are not all global-optimum proofs." + }, + "manual_mutation_dispositions": { + "cote2018": { + "effective_status": "clear_intentional_value_certificate", + "reason": "The published solution contract is intentionally objective-only; the trusted checker independently solves the finite model and accepts only the exact optimum. Both objective-tamper probes are rejected." + }, + "rahmaniani2022": { + "effective_status": "clear_valid_alternative_sparse_decision", + "reason": "Erasing the sparse storage-energy map produces a different zero-map investment decision. The checker fixes that decision, proves its exact operational recourse optimum, and accepts it only because the unchanged reported objective is still exact; objective-tamper probes are rejected." + } + }, + "cases": [ + { + "case_id": "adulyasak2015", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": true, + "phase1_semantic_disposition": "manual_no_additional_mismatch_confirmed", + "phase1_semantic_basis": "Core stochastic location/inventory variables and objective align on the tiny path; the evaluator overlay repairs the dataset checker's objective preflight. The HF checker still accepts an empty/missing-field payload and needs schema hardening.", + "phase84_previous_disposition": null, + "phase84_previous_reason": null, + "resolution_note": "Released checker/reference contract replayed and closed.", + "official_pair_count": 6, + "official_accepted_count": 6, + "official_outcome_counts": { + "accepted": 6 + }, + "stored_result_mismatches": [], + "evaluator_preflight": "passed", + "mutation_raw_status": "mutation_clear", + "mutation_effective_clear": true, + "mutation_manual_disposition": null, + "static_raw_status": "flagged", + "static_max_severity": "medium", + "static_finding_codes": [ + "instance_top_fields_not_detected_in_model", + "instance_top_fields_not_detected_in_checker" + ], + "reference_count_in_worktree": 6, + "unproven_reference_count": 5, + "prompt_changed": false, + "formulation_changed": false, + "model_changed": false, + "checker_changed": true, + "instance_changed": false, + "solution_changed": false, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 1, + "changed_files": [ + "feasibility_check.py" + ] + }, + { + "case_id": "alba2013", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": true, + "phase1_semantic_disposition": "manual_no_additional_mismatch_confirmed", + "phase1_semantic_basis": "Integer lazy separation checks complete loading/subtour feasibility; the short-path limit applies only to fractional heuristic separation and does not change integer feasibility.", + "phase84_previous_disposition": null, + "phase84_previous_reason": null, + "resolution_note": "Released checker/reference contract replayed and closed.", + "official_pair_count": 6, + "official_accepted_count": 6, + "official_outcome_counts": { + "accepted": 6 + }, + "stored_result_mismatches": [], + "evaluator_preflight": "passed", + "mutation_raw_status": "mutation_clear", + "mutation_effective_clear": true, + "mutation_manual_disposition": null, + "static_raw_status": "flagged", + "static_max_severity": "medium", + "static_finding_codes": [ + "instance_top_fields_not_detected_in_model", + "instance_top_fields_not_detected_in_checker" + ], + "reference_count_in_worktree": 6, + "unproven_reference_count": 0, + "prompt_changed": false, + "formulation_changed": false, + "model_changed": false, + "checker_changed": true, + "instance_changed": false, + "solution_changed": false, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 1, + "changed_files": [ + "feasibility_check.py" + ] + }, + { + "case_id": "alfieri2020", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": false, + "phase1_semantic_disposition": "confirmed_issue", + "phase1_semantic_basis": "**confirmed P0 witness-bypass**.", + "phase84_previous_disposition": "NO_DATA_REPAIR_CLOSED", + "phase84_previous_reason": "要求完整 coloring，并从着色见证精确重算颜色数。", + "resolution_note": "Released checker/reference contract replayed and closed.", + "official_pair_count": 6, + "official_accepted_count": 6, + "official_outcome_counts": { + "accepted": 6 + }, + "stored_result_mismatches": [], + "evaluator_preflight": "passed", + "mutation_raw_status": "mutation_clear", + "mutation_effective_clear": true, + "mutation_manual_disposition": null, + "static_raw_status": "flagged", + "static_max_severity": "medium", + "static_finding_codes": [ + "instance_top_fields_not_detected_in_model", + "instance_top_fields_not_detected_in_checker" + ], + "reference_count_in_worktree": 6, + "unproven_reference_count": 5, + "prompt_changed": false, + "formulation_changed": false, + "model_changed": false, + "checker_changed": true, + "instance_changed": false, + "solution_changed": false, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 1, + "changed_files": [ + "feasibility_check.py" + ] + }, + { + "case_id": "amaldi2013", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": true, + "phase1_semantic_disposition": "manual_no_additional_mismatch_confirmed", + "phase1_semantic_basis": "K=ceil(n/d) is a valid cover bound because any at most d points in R^d lie on a hyperplane; unit-normal and big-M geometry align. Four large references are explicit time-limit incumbents.", + "phase84_previous_disposition": null, + "phase84_previous_reason": null, + "resolution_note": "Released checker/reference contract replayed and closed.", + "official_pair_count": 6, + "official_accepted_count": 6, + "official_outcome_counts": { + "accepted": 6 + }, + "stored_result_mismatches": [], + "evaluator_preflight": "passed", + "mutation_raw_status": "mutation_clear", + "mutation_effective_clear": true, + "mutation_manual_disposition": null, + "static_raw_status": "flagged", + "static_max_severity": "medium", + "static_finding_codes": [ + "solution_schema_fields_not_detected_in_checker", + "instance_top_fields_not_detected_in_model", + "instance_top_fields_not_detected_in_checker" + ], + "reference_count_in_worktree": 6, + "unproven_reference_count": 4, + "prompt_changed": false, + "formulation_changed": false, + "model_changed": false, + "checker_changed": true, + "instance_changed": false, + "solution_changed": false, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 1, + "changed_files": [ + "feasibility_check.py" + ] + }, + { + "case_id": "araujo2015", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": true, + "phase1_semantic_disposition": "automated_no_confirmed_mismatch_on_exercised_paths", + "phase1_semantic_basis": "Static triad scan, official-tiny mutation/replay, current tiny-model reproduction, and full official replay produced no case-specific semantic finding requiring manual review.", + "phase84_previous_disposition": null, + "phase84_previous_reason": null, + "resolution_note": "Released checker/reference contract replayed and closed.", + "official_pair_count": 6, + "official_accepted_count": 6, + "official_outcome_counts": { + "accepted": 6 + }, + "stored_result_mismatches": [], + "evaluator_preflight": "passed", + "mutation_raw_status": "mutation_clear", + "mutation_effective_clear": true, + "mutation_manual_disposition": null, + "static_raw_status": "static_clear", + "static_max_severity": "none", + "static_finding_codes": [], + "reference_count_in_worktree": 6, + "unproven_reference_count": 3, + "prompt_changed": false, + "formulation_changed": false, + "model_changed": false, + "checker_changed": true, + "instance_changed": false, + "solution_changed": false, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 1, + "changed_files": [ + "feasibility_check.py" + ] + }, + { + "case_id": "araujo2020", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": false, + "phase1_semantic_disposition": "confirmed_issue", + "phase1_semantic_basis": "**confirmed P0 invalid large-1 reference/cache pair**.", + "phase84_previous_disposition": "REFERENCE_DATA_BLOCKED", + "phase84_previous_reason": "nonaccepted=large_instance_1.json:rejected", + "resolution_note": "Released checker/reference contract replayed and closed.", + "official_pair_count": 6, + "official_accepted_count": 6, + "official_outcome_counts": { + "accepted": 6 + }, + "stored_result_mismatches": [], + "evaluator_preflight": "passed", + "mutation_raw_status": "mutation_clear", + "mutation_effective_clear": true, + "mutation_manual_disposition": null, + "static_raw_status": "static_clear", + "static_max_severity": "none", + "static_finding_codes": [], + "reference_count_in_worktree": 6, + "unproven_reference_count": 0, + "prompt_changed": false, + "formulation_changed": false, + "model_changed": false, + "checker_changed": true, + "instance_changed": false, + "solution_changed": true, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 2, + "changed_files": [ + "feasibility_check.py", + "gurobi_solution/large_solution_1.json" + ] + }, + { + "case_id": "archetti2007", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": false, + "phase1_semantic_disposition": "confirmed_issue", + "phase1_semantic_basis": "**confirmed P0 stale large-1 solution/log/result bundle**.", + "phase84_previous_disposition": "REFERENCE_DATA_BLOCKED", + "phase84_previous_reason": "nonaccepted=large_instance_1.json:rejected", + "resolution_note": "Released checker/reference contract replayed and closed.", + "official_pair_count": 6, + "official_accepted_count": 6, + "official_outcome_counts": { + "accepted": 6 + }, + "stored_result_mismatches": [], + "evaluator_preflight": "passed", + "mutation_raw_status": "mutation_clear", + "mutation_effective_clear": true, + "mutation_manual_disposition": null, + "static_raw_status": "static_clear", + "static_max_severity": "none", + "static_finding_codes": [], + "reference_count_in_worktree": 6, + "unproven_reference_count": 3, + "prompt_changed": false, + "formulation_changed": false, + "model_changed": false, + "checker_changed": true, + "instance_changed": false, + "solution_changed": true, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 2, + "changed_files": [ + "feasibility_check.py", + "gurobi_solution/large_solution_1.json" + ] + }, + { + "case_id": "armbruster2012", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": false, + "phase1_semantic_disposition": "confirmed_issue", + "phase1_semantic_basis": "**confirmed release-integrity issue: `tiny_instance_2.json` has no same-name official solution**.", + "phase84_previous_disposition": "REFERENCE_DATA_BLOCKED", + "phase84_previous_reason": "orphan=tiny_instance_2.json", + "resolution_note": "Released checker/reference contract replayed and closed.", + "official_pair_count": 7, + "official_accepted_count": 7, + "official_outcome_counts": { + "accepted": 7 + }, + "stored_result_mismatches": [], + "evaluator_preflight": "passed", + "mutation_raw_status": "mutation_clear", + "mutation_effective_clear": true, + "mutation_manual_disposition": null, + "static_raw_status": "flagged", + "static_max_severity": "medium", + "static_finding_codes": [ + "instance_top_fields_not_detected_in_model", + "instance_top_fields_not_detected_in_checker" + ], + "reference_count_in_worktree": 7, + "unproven_reference_count": 5, + "prompt_changed": false, + "formulation_changed": false, + "model_changed": false, + "checker_changed": true, + "instance_changed": false, + "solution_changed": true, + "stored_result_changed": true, + "solver_log_changed": false, + "changed_file_count": 3, + "changed_files": [ + "feasibility_check.py", + "gurobi_feasi_result/tiny_feasi_result_2.json", + "gurobi_solution/tiny_solution_2.json" + ] + }, + { + "case_id": "arslan2021", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": false, + "phase1_semantic_disposition": "confirmed_issue", + "phase1_semantic_basis": "**confirmed P0 formulation/model mismatch and unverifiable objective interface**.", + "phase84_previous_disposition": "NO_DATA_REPAIR_CLOSED", + "phase84_previous_reason": "将发布契约明确为 LP-relaxed recourse，并对固定一阶段决策精确求解 recourse。", + "resolution_note": "Released checker/reference contract replayed and closed.", + "official_pair_count": 6, + "official_accepted_count": 6, + "official_outcome_counts": { + "accepted": 6 + }, + "stored_result_mismatches": [], + "evaluator_preflight": "passed", + "mutation_raw_status": "mutation_clear", + "mutation_effective_clear": true, + "mutation_manual_disposition": null, + "static_raw_status": "static_clear", + 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"eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": true, + "phase1_semantic_disposition": "manual_no_additional_mismatch_confirmed", + "phase1_semantic_basis": "Continuous z/u relaxation and penalty terms match the stated formulation; empty JSON is a schema failure even where an all-zero decision is mathematically feasible.", + "phase84_previous_disposition": null, + "phase84_previous_reason": null, + "resolution_note": "Released checker/reference contract replayed and closed.", + "official_pair_count": 6, + "official_accepted_count": 6, + "official_outcome_counts": { + "accepted": 6 + }, + "stored_result_mismatches": [], + "evaluator_preflight": "passed", + "mutation_raw_status": "mutation_clear", + "mutation_effective_clear": true, + "mutation_manual_disposition": null, + "static_raw_status": "flagged", + "static_max_severity": "medium", + "static_finding_codes": [ + "instance_top_fields_not_detected_in_model", + "instance_top_fields_not_detected_in_checker" + ], + "reference_count_in_worktree": 6, + "unproven_reference_count": 4, + "prompt_changed": false, + "formulation_changed": false, + "model_changed": false, + "checker_changed": true, + "instance_changed": false, + "solution_changed": false, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 1, + "changed_files": [ + "feasibility_check.py" + ] + }, + { + "case_id": "contreras2011", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": true, + "phase1_semantic_disposition": "automated_no_confirmed_mismatch_on_exercised_paths", + "phase1_semantic_basis": "Static triad scan, official-tiny mutation/replay, current tiny-model reproduction, and full official replay produced no case-specific semantic finding 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"changed_file_count": 1, + "changed_files": [ + "feasibility_check.py" + ] + }, + { + "case_id": "cordeau2006", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": false, + "phase1_semantic_disposition": "confirmed_issue", + "phase1_semantic_basis": "**confirmed P0 checker feasible-set mismatch: clearing service times disables all route timing constraints**.", + "phase84_previous_disposition": "NO_DATA_REPAIR_CLOSED", + "phase84_previous_reason": "要求路线、service time 和 ride time 的完整且精确 ID 覆盖。", + "resolution_note": "Released checker/reference contract replayed and closed.", + "official_pair_count": 6, + "official_accepted_count": 6, + "official_outcome_counts": { + "accepted": 6 + }, + "stored_result_mismatches": [], + "evaluator_preflight": "passed", + "mutation_raw_status": "mutation_clear", + "mutation_effective_clear": true, + "mutation_manual_disposition": null, + "static_raw_status": "static_clear", + "static_max_severity": "none", + "static_finding_codes": [], + "reference_count_in_worktree": 6, + "unproven_reference_count": 3, + "prompt_changed": false, + "formulation_changed": false, + "model_changed": false, + "checker_changed": true, + "instance_changed": false, + "solution_changed": false, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 1, + "changed_files": [ + "feasibility_check.py" + ] + }, + { + "case_id": "cordeau2014", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": false, + "phase1_semantic_disposition": "confirmed_issue", + "phase1_semantic_basis": "**confirmed P0 Gurobi-model mismatch: the path-activation cut has the wrong sign and can exclude every competing tour**.", + "phase84_previous_disposition": "DEEP_ALIGNMENT_REMAINS", + "phase84_previous_reason": "callback 符号和 ATSP bound 使用已修，但旧大规模答案未由修正模型重新认证。", + "resolution_note": "Repaired callbacks/cuts and replayed complete routing witnesses.", + "official_pair_count": 6, + "official_accepted_count": 6, + "official_outcome_counts": { + "accepted": 6 + }, + "stored_result_mismatches": [], + "evaluator_preflight": "passed", + "mutation_raw_status": "mutation_clear", + "mutation_effective_clear": true, + "mutation_manual_disposition": null, + "static_raw_status": "flagged", + "static_max_severity": "high", + "static_finding_codes": [ + "solution_schema_fields_not_detected_in_model_output", + "instance_top_fields_not_detected_in_model", + "instance_top_fields_not_detected_in_checker" + ], + "reference_count_in_worktree": 6, + "unproven_reference_count": 1, + "prompt_changed": false, + "formulation_changed": true, + "model_changed": true, + "checker_changed": true, + "instance_changed": false, + "solution_changed": true, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 5, + "changed_files": [ + "feasibility_check.py", + "gurobi_code.py", + "gurobi_solution/large_solution_2.json", + "gurobi_solution/large_solution_4.json", + "mathematical_formulation.md" + ] + }, + { + "case_id": "cordeau2019", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": false, + "phase1_semantic_disposition": "confirmed_issue", + "phase1_semantic_basis": "**confirmed P0 evaluator exploit: an empty `results` object disables both feasibility and objective validation**.", + "phase84_previous_disposition": "NO_DATA_REPAIR_CLOSED", + "phase84_previous_reason": "增加主问题类型判别，并要求所有请求变体及嵌套结果完整覆盖。", + "resolution_note": "Released checker/reference contract replayed and closed.", + "official_pair_count": 6, + "official_accepted_count": 6, + "official_outcome_counts": { + "accepted": 6 + }, + "stored_result_mismatches": [], + "evaluator_preflight": "passed", + "mutation_raw_status": "mutation_clear", + "mutation_effective_clear": true, + "mutation_manual_disposition": null, + "static_raw_status": "flagged", + "static_max_severity": "medium", + "static_finding_codes": [ + "multiple_model_objective_senses", + "instance_top_fields_not_detected_in_model", + "instance_top_fields_not_detected_in_checker" + ], + "reference_count_in_worktree": 6, + "unproven_reference_count": 6, + "prompt_changed": false, + "formulation_changed": false, + "model_changed": false, + "checker_changed": true, + "instance_changed": false, + "solution_changed": true, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 2, + "changed_files": [ + "feasibility_check.py", + "solution_schema.json" + ] + }, + { + "case_id": "cote2018", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": false, + "phase1_semantic_disposition": "confirmed_issue", + "phase1_semantic_basis": "**confirmed evaluator-contract mismatch caused by an objective-only schema**.", + "phase84_previous_disposition": "NO_DATA_REPAIR_CLOSED", + "phase84_previous_reason": "把 value-only 接口定义为数值证书，由 checker 独立精确求解模型验证。", + "resolution_note": "Released checker/reference contract replayed and closed.", + "official_pair_count": 6, + "official_accepted_count": 6, + "official_outcome_counts": { + "accepted": 6 + }, + "stored_result_mismatches": [], + "evaluator_preflight": "passed", + "mutation_raw_status": "flagged", + "mutation_effective_clear": true, + "mutation_manual_disposition": { + "effective_status": "clear_intentional_value_certificate", + "reason": "The published solution contract is intentionally objective-only; the trusted checker independently solves the finite model and accepts only the exact optimum. Both objective-tamper probes are rejected." + }, + "static_raw_status": "flagged", + "static_max_severity": "medium", + "static_finding_codes": [ + "instance_top_fields_not_detected_in_model", + "instance_top_fields_not_detected_in_checker" + ], + "reference_count_in_worktree": 6, + "unproven_reference_count": 0, + "prompt_changed": false, + "formulation_changed": false, + "model_changed": false, + "checker_changed": true, + "instance_changed": false, + "solution_changed": false, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 1, + "changed_files": [ + "feasibility_check.py" + ] + }, + { + "case_id": "cote2019", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": false, + "phase1_semantic_disposition": "confirmed_issue", + "phase1_semantic_basis": "**confirmed evaluator-blocking weak objective binding; no tiny model drift after sufficient solve time**.", + "phase84_previous_disposition": "NO_DATA_REPAIR_CLOSED", + "phase84_previous_reason": "对固定一阶段方案精确验证 routing/packing recourse 与目标。", + "resolution_note": "Released checker/reference contract replayed and closed.", + "official_pair_count": 6, + "official_accepted_count": 6, + "official_outcome_counts": { + "accepted": 6 + }, + "stored_result_mismatches": [], + "evaluator_preflight": "passed", + "mutation_raw_status": "mutation_clear", + "mutation_effective_clear": true, + "mutation_manual_disposition": null, + "static_raw_status": "flagged", + "static_max_severity": "low", + "static_finding_codes": [ + "instance_top_fields_not_detected_in_checker" + ], + "reference_count_in_worktree": 6, + "unproven_reference_count": 0, + "prompt_changed": false, + "formulation_changed": false, + "model_changed": false, + "checker_changed": true, + "instance_changed": false, + "solution_changed": false, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 1, + "changed_files": [ + "feasibility_check.py" + ] + }, + { + "case_id": "cunha2021", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": false, + "phase1_semantic_disposition": "confirmed_issue", + "phase1_semantic_basis": "**confirmed P0 objective-only large-1 reference/cache mismatch**.", + "phase84_previous_disposition": "REFERENCE_DATA_BLOCKED", + "phase84_previous_reason": "nonaccepted=large_instance_1.json:rejected", + "resolution_note": "Released checker/reference contract replayed and closed.", + "official_pair_count": 6, + "official_accepted_count": 6, + "official_outcome_counts": { + "accepted": 6 + }, + "stored_result_mismatches": [], + "evaluator_preflight": "passed", + "mutation_raw_status": "mutation_clear", + "mutation_effective_clear": true, + "mutation_manual_disposition": null, + "static_raw_status": "static_clear", + "static_max_severity": "none", + "static_finding_codes": [], + "reference_count_in_worktree": 6, + "unproven_reference_count": 6, + "prompt_changed": false, + "formulation_changed": false, + "model_changed": false, + "checker_changed": true, + "instance_changed": false, + "solution_changed": true, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 2, + "changed_files": [ + "feasibility_check.py", + "gurobi_solution/large_solution_1.json" + ] + }, + { + "case_id": "damcikurt2016", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": false, + "phase1_semantic_disposition": "confirmed_issue", + "phase1_semantic_basis": "**confirmed P0 stale/truncated official reference solutions**.", + "phase84_previous_disposition": "REFERENCE_DATA_BLOCKED", + "phase84_previous_reason": "nonaccepted=large_instance_1.json:rejected, large_instance_4.json:rejected", + "resolution_note": "Replaced truncated policies with sparse complete-policy certificates and explicit defaults.", + "official_pair_count": 6, + "official_accepted_count": 6, + "official_outcome_counts": { + "accepted": 6 + }, + "stored_result_mismatches": [], + "evaluator_preflight": "passed", + "mutation_raw_status": "mutation_clear", + "mutation_effective_clear": true, + "mutation_manual_disposition": null, + "static_raw_status": "flagged", + "static_max_severity": "high", + "static_finding_codes": [ + "solution_schema_fields_not_detected_in_model_output" + ], + "reference_count_in_worktree": 6, + "unproven_reference_count": 2, + "prompt_changed": false, + "formulation_changed": true, + "model_changed": true, + "checker_changed": true, + "instance_changed": false, + "solution_changed": true, + "stored_result_changed": true, + "solver_log_changed": false, + "changed_file_count": 8, + "changed_files": [ + "feasibility_check.py", + "gurobi_code.py", + "gurobi_feasi_result/large_feasi_result_1.json", + "gurobi_feasi_result/large_feasi_result_4.json", + "gurobi_solution/large_solution_1.json", + "gurobi_solution/large_solution_4.json", + "mathematical_formulation.md", + "solution_schema.json" + ] + }, + { + "case_id": "dayarian2019", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": false, + "phase1_semantic_disposition": "confirmed_issue", + "phase1_semantic_basis": "**confirmed P0 model/checker omission of multi-trip non-overlap, forced trip-slot activation, and incomplete objective witness**.", + "phase84_previous_disposition": "REFERENCE_DATA_BLOCKED", + "phase84_previous_reason": "nonaccepted=large_instance_1.json:rejected, large_instance_3.json:rejected", + "resolution_note": "Added ordered routes and complete f/beta/w/alpha staffing certificates; objective 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+ "official_accepted_count": 6, + "official_outcome_counts": { + "accepted": 6 + }, + "stored_result_mismatches": [], + "evaluator_preflight": "passed", + "mutation_raw_status": "mutation_clear", + "mutation_effective_clear": true, + "mutation_manual_disposition": null, + "static_raw_status": "flagged", + "static_max_severity": "medium", + "static_finding_codes": [ + "instance_top_fields_not_detected_in_model", + "instance_top_fields_not_detected_in_checker" + ], + "reference_count_in_worktree": 6, + "unproven_reference_count": 2, + "prompt_changed": false, + "formulation_changed": false, + "model_changed": false, + "checker_changed": true, + "instance_changed": false, + "solution_changed": false, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 1, + "changed_files": [ + "feasibility_check.py" + ] + }, + { + "case_id": "delage2022", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": true, + "phase1_semantic_disposition": "manual_no_additional_mismatch_confirmed", + "phase1_semantic_basis": "Continuous extra-piece variables and binary expansion implement the supplied formulation; large reference files identify themselves as time-limit feasible.", + "phase84_previous_disposition": null, + "phase84_previous_reason": null, + "resolution_note": "Released checker/reference contract replayed and closed.", + "official_pair_count": 6, + "official_accepted_count": 6, + "official_outcome_counts": { + "accepted": 6 + }, + "stored_result_mismatches": [], + "evaluator_preflight": "passed", + "mutation_raw_status": "mutation_clear", + "mutation_effective_clear": true, + "mutation_manual_disposition": null, + "static_raw_status": "flagged", + "static_max_severity": "medium", + "static_finding_codes": [ + "instance_top_fields_not_detected_in_model", + "instance_top_fields_not_detected_in_checker" + ], + "reference_count_in_worktree": 6, + "unproven_reference_count": 5, + "prompt_changed": false, + "formulation_changed": false, + "model_changed": false, + "checker_changed": true, + "instance_changed": false, + "solution_changed": false, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 1, + "changed_files": [ + "feasibility_check.py" + ] + }, + { + "case_id": "delorme2020", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": true, + "phase1_semantic_disposition": "manual_no_additional_mismatch_confirmed", + "phase1_semantic_basis": "num_bins is redundant with objective_value and the number of bin_assignments; clearing only that scalar leaves the complete packing witness and recomputed count.", + "phase84_previous_disposition": null, + "phase84_previous_reason": null, + "resolution_note": "Released checker/reference contract replayed and closed.", + "official_pair_count": 6, + "official_accepted_count": 6, + "official_outcome_counts": { + "accepted": 6 + }, + "stored_result_mismatches": [], + "evaluator_preflight": "passed", + "mutation_raw_status": "mutation_clear", + "mutation_effective_clear": true, + "mutation_manual_disposition": null, + "static_raw_status": "flagged", + "static_max_severity": "medium", + "static_finding_codes": [ + "instance_top_fields_not_detected_in_model", + "instance_top_fields_not_detected_in_checker" + ], + "reference_count_in_worktree": 6, + "unproven_reference_count": 0, + "prompt_changed": false, + "formulation_changed": false, + "model_changed": false, + "checker_changed": true, + "instance_changed": false, + "solution_changed": false, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 1, + "changed_files": [ + "feasibility_check.py" + ] + }, + { + "case_id": "desaulniers2010", + 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"solver_log_changed": false, + "changed_file_count": 1, + "changed_files": [ + "feasibility_check.py" + ] + }, + { + "case_id": "kobayashi2021", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": true, + "phase1_semantic_disposition": "automated_no_confirmed_mismatch_on_exercised_paths", + "phase1_semantic_basis": "Static triad scan, official-tiny mutation/replay, current tiny-model reproduction, and full official replay produced no case-specific semantic finding requiring manual review.", + "phase84_previous_disposition": null, + "phase84_previous_reason": null, + "resolution_note": "Released checker/reference contract replayed and closed.", + "official_pair_count": 6, + "official_accepted_count": 6, + "official_outcome_counts": { + "accepted": 6 + }, + "stored_result_mismatches": [], + "evaluator_preflight": "passed", + "mutation_raw_status": 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true, + "instance_changed": false, + "solution_changed": false, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 1, + "changed_files": [ + "feasibility_check.py" + ] + }, + { + "case_id": "martin-iradi2024", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": false, + "phase1_semantic_disposition": "confirmed_issue", + "phase1_semantic_basis": "**confirmed one-sided objective checker; apparent tiny mismatch is fully explained by Gurobi's MIP-gap stopping rule**.", + "phase84_previous_disposition": "NO_DATA_REPAIR_CLOSED", + "phase84_previous_reason": "从完整路线见证精确重算目标。", + "resolution_note": "Released checker/reference contract replayed and closed.", + "official_pair_count": 6, + "official_accepted_count": 6, + "official_outcome_counts": { + "accepted": 6 + }, + "stored_result_mismatches": [], + "evaluator_preflight": "passed", + "mutation_raw_status": "mutation_clear", + "mutation_effective_clear": true, + "mutation_manual_disposition": null, + "static_raw_status": "flagged", + "static_max_severity": "low", + "static_finding_codes": [ + "instance_top_fields_not_detected_in_checker" + ], + "reference_count_in_worktree": 6, + "unproven_reference_count": 5, + "prompt_changed": false, + "formulation_changed": false, + "model_changed": false, + "checker_changed": true, + "instance_changed": false, + "solution_changed": false, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 1, + "changed_files": [ + "feasibility_check.py" + ] + }, + { + "case_id": "mehrotra1996", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": false, + "phase1_semantic_disposition": "confirmed_issue", + "phase1_semantic_basis": "**confirmed P0 reference-certification 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manual review.", + "phase84_previous_disposition": null, + "phase84_previous_reason": null, + "resolution_note": "Released checker/reference contract replayed and closed.", + "official_pair_count": 6, + "official_accepted_count": 6, + "official_outcome_counts": { + "accepted": 6 + }, + "stored_result_mismatches": [], + "evaluator_preflight": "passed", + "mutation_raw_status": "mutation_clear", + "mutation_effective_clear": true, + "mutation_manual_disposition": null, + "static_raw_status": "flagged", + "static_max_severity": "medium", + "static_finding_codes": [ + "instance_top_fields_not_detected_in_model", + "instance_top_fields_not_detected_in_checker" + ], + "reference_count_in_worktree": 6, + "unproven_reference_count": 0, + "prompt_changed": false, + "formulation_changed": false, + "model_changed": false, + "checker_changed": true, + "instance_changed": false, + "solution_changed": false, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 1, + "changed_files": [ + "feasibility_check.py" + ] + }, + { + "case_id": "morrison2016", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": true, + "phase1_semantic_disposition": "manual_no_additional_mismatch_confirmed", + "phase1_semantic_basis": "Bron-Kerbosch enumerates all maximal independent sets, which suffices for exact coloring set cover. 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also missing the standard formulation filename**.", + "phase84_previous_disposition": "DEEP_ALIGNMENT_REMAINS", + "phase84_previous_reason": "目录是 earl2005 的错误重复身份，不是独立的 Ostrowski 2012 问题。", + "resolution_note": "Converted the mislabeled duplicate into a disclosed compatibility alias of earl2005.", + "official_pair_count": 6, + "official_accepted_count": 6, + "official_outcome_counts": { + "accepted": 6 + }, + "stored_result_mismatches": [], + "evaluator_preflight": "passed", + "mutation_raw_status": "mutation_clear", + "mutation_effective_clear": true, + "mutation_manual_disposition": null, + "static_raw_status": "flagged", + "static_max_severity": "medium", + "static_finding_codes": [ + "instance_top_fields_not_detected_in_model", + "instance_top_fields_not_detected_in_checker" + ], + "reference_count_in_worktree": 6, + "unproven_reference_count": 0, + "prompt_changed": true, + "formulation_changed": true, + "model_changed": true, + "checker_changed": true, + "instance_changed": false, + "solution_changed": false, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 6, + "changed_files": [ + "CASE_IDENTITY.json", + "feasibility_check.py", + "gurobi_code.py", + "math_model.txt", + "mathematical_formulation.md", + "problem_description.txt" + ] + }, + { + "case_id": "ozturk2020", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": false, + "phase1_semantic_disposition": "manual_no_additional_mismatch_confirmed", + "phase1_semantic_basis": "Batch decisions determine feasibility and omitted job_assignments are redundant. Missing completion fields weaken objective checking but do not create a favorable minimization path found in current probes.", + "phase84_previous_disposition": null, + "phase84_previous_reason": null, + "resolution_note": "Released checker/reference contract replayed and closed.", + "official_pair_count": 7, + "official_accepted_count": 7, + "official_outcome_counts": { + "accepted": 7 + }, + "stored_result_mismatches": [], + "evaluator_preflight": "passed", + "mutation_raw_status": "mutation_clear", + "mutation_effective_clear": true, + "mutation_manual_disposition": null, + "static_raw_status": "static_clear", + "static_max_severity": "none", + "static_finding_codes": [], + "reference_count_in_worktree": 7, + "unproven_reference_count": 6, + "prompt_changed": false, + "formulation_changed": false, + "model_changed": false, + "checker_changed": true, + "instance_changed": false, + "solution_changed": true, + "stored_result_changed": true, + "solver_log_changed": false, + "changed_file_count": 3, + "changed_files": [ + "feasibility_check.py", + "gurobi_feasi_result/tiny_feasi_result_2.json", + "gurobi_solution/tiny_solution_2.json" + ] + }, + { + "case_id": "pecin2017", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": false, + "phase1_semantic_disposition": "manual_no_additional_mismatch_confirmed", + "phase1_semantic_basis": "Route formulation and checker are exact on exercised paths; static missing-output finding is a source-analysis false positive. Large references are time-limit incumbents.", + "phase84_previous_disposition": null, + "phase84_previous_reason": null, + "resolution_note": "Released checker/reference contract replayed and closed.", + "official_pair_count": 7, + "official_accepted_count": 7, + "official_outcome_counts": { + "accepted": 7 + }, + "stored_result_mismatches": [], + "evaluator_preflight": "passed", + "mutation_raw_status": "mutation_clear", + "mutation_effective_clear": true, + "mutation_manual_disposition": null, + "static_raw_status": "flagged", + "static_max_severity": "high", + "static_finding_codes": [ + "solution_schema_fields_not_detected_in_model_output" + ], + "reference_count_in_worktree": 7, + "unproven_reference_count": 6, + "prompt_changed": false, + "formulation_changed": false, + "model_changed": false, + "checker_changed": true, + "instance_changed": false, + "solution_changed": true, + "stored_result_changed": true, + "solver_log_changed": false, + "changed_file_count": 3, + "changed_files": [ + "feasibility_check.py", + "gurobi_feasi_result/tiny_feasi_result_2.json", + "gurobi_solution/tiny_solution_2.json" + ] + }, + { + "case_id": "pedersen2024", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": true, + "phase1_semantic_disposition": "automated_no_confirmed_mismatch_on_exercised_paths", + "phase1_semantic_basis": "Static triad scan, official-tiny mutation/replay, current tiny-model reproduction, and full official replay produced no case-specific semantic finding requiring manual review.", + "phase84_previous_disposition": null, + "phase84_previous_reason": null, + "resolution_note": "Released checker/reference contract replayed and closed.", + "official_pair_count": 6, + "official_accepted_count": 6, + "official_outcome_counts": { + "accepted": 6 + }, + "stored_result_mismatches": [], + "evaluator_preflight": "passed", + 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"solver_log_changed": false, + "changed_file_count": 4, + "changed_files": [ + "feasibility_check.py", + "gurobi_solution/large_solution_2.json", + "gurobi_solution/large_solution_4.json", + "gurobi_solution/large_solution_5.json" + ] + }, + { + "case_id": "potthoff2010", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": false, + "phase1_semantic_disposition": "confirmed_issue", + "phase1_semantic_basis": "**confirmed release-integrity issue: `tiny_instance_2.json` has no same-name official solution**.", + "phase84_previous_disposition": "REFERENCE_DATA_BLOCKED", + "phase84_previous_reason": "orphan=tiny_instance_2.json", + "resolution_note": "Released a finite relief-point completion contract and regenerated all references.", + "official_pair_count": 7, + "official_accepted_count": 7, + "official_outcome_counts": { + "accepted": 7 + }, + 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false, + "changed_file_count": 1, + "changed_files": [ + "feasibility_check.py" + ] + }, + { + "case_id": "rahmaniani2017", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": false, + "phase1_semantic_disposition": "confirmed_issue", + "phase1_semantic_basis": "**confirmed P0 transportation-cost double scaling, objective mismatch, and empty-hub-edge feasibility bypass**.", + "phase84_previous_disposition": "REFERENCE_DATA_BLOCKED", + "phase84_previous_reason": "nonaccepted=large_instance_1.json:rejected, large_instance_2.json:rejected, large_instance_3.json:rejected, large_instance_4.json:rejected, large_instance_5.json:rejected, tiny_instance.json:rejected", + "resolution_note": "Released checker/reference contract replayed and closed.", + "official_pair_count": 6, + "official_accepted_count": 6, + "official_outcome_counts": { + "accepted": 6 + }, + "stored_result_mismatches": [], + "evaluator_preflight": "passed", + "mutation_raw_status": "mutation_clear", + "mutation_effective_clear": true, + "mutation_manual_disposition": null, + "static_raw_status": "flagged", + "static_max_severity": "medium", + "static_finding_codes": [ + "instance_top_fields_not_detected_in_model", + "instance_top_fields_not_detected_in_checker" + ], + "reference_count_in_worktree": 6, + "unproven_reference_count": 0, + "prompt_changed": false, + "formulation_changed": false, + "model_changed": true, + "checker_changed": true, + "instance_changed": false, + "solution_changed": true, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 8, + "changed_files": [ + "feasibility_check.py", + "gurobi_code.py", + "gurobi_solution/large_solution_1.json", + "gurobi_solution/large_solution_2.json", + "gurobi_solution/large_solution_3.json", + "gurobi_solution/large_solution_4.json", + "gurobi_solution/large_solution_5.json", + "gurobi_solution/tiny_solution.json" + ] + }, + { + "case_id": "rahmaniani2018", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": false, + "phase1_semantic_disposition": "confirmed_issue", + "phase1_semantic_basis": "**confirmed P0 partial-witness and objective-certificate gap; current tiny model output is reproducible**.", + "phase84_previous_disposition": "NO_DATA_REPAIR_CLOSED", + "phase84_previous_reason": "要求完整投资 ID，并对固定投资精确重解运营子问题与目标。", + "resolution_note": "Released checker/reference contract replayed and closed.", + "official_pair_count": 6, + "official_accepted_count": 6, + "official_outcome_counts": { + "accepted": 6 + }, + "stored_result_mismatches": [], + "evaluator_preflight": "passed", + "mutation_raw_status": "mutation_clear", + "mutation_effective_clear": true, + "mutation_manual_disposition": null, + "static_raw_status": "static_clear", + "static_max_severity": "none", + "static_finding_codes": [], + "reference_count_in_worktree": 6, + "unproven_reference_count": 0, + "prompt_changed": false, + "formulation_changed": false, + "model_changed": true, + "checker_changed": true, + "instance_changed": false, + "solution_changed": false, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 2, + "changed_files": [ + "feasibility_check.py", + "gurobi_code.py" + ] + }, + { + "case_id": "rahmaniani2020", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": true, + "phase1_semantic_disposition": "automated_no_confirmed_mismatch_on_exercised_paths", + "phase1_semantic_basis": "Static triad scan, official-tiny mutation/replay, current tiny-model reproduction, and full official replay produced no case-specific semantic finding requiring manual review.", + 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[ + "feasibility_check.py" + ] + }, + { + "case_id": "rahmaniani2022", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": false, + "phase1_semantic_disposition": "confirmed_issue", + "phase1_semantic_basis": "**confirmed P0 fail-open checker plus formulation/model investment-domain mismatch**.", + "phase84_previous_disposition": "DEEP_ALIGNMENT_REMAINS", + "phase84_previous_reason": "目标与 recourse 已精确绑定，但投资 block/domain 和 transmission 语义仍不一致。", + "resolution_note": "Aligned investment domains and bound every sparse investment to exact recourse.", + "official_pair_count": 6, + "official_accepted_count": 6, + "official_outcome_counts": { + "accepted": 6 + }, + "stored_result_mismatches": [], + "evaluator_preflight": "passed", + "mutation_raw_status": "flagged", + "mutation_effective_clear": true, + "mutation_manual_disposition": { + "effective_status": 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The checker fixes that decision, proves its exact operational recourse optimum, and accepts it only because the unchanged reported objective is still exact; objective-tamper probes are rejected." + }, + "static_raw_status": "flagged", + "static_max_severity": "high", + "static_finding_codes": [ + "solution_schema_fields_not_detected_in_model_output", + "instance_top_fields_not_detected_in_model", + "instance_top_fields_not_detected_in_checker" + ], + "reference_count_in_worktree": 6, + "unproven_reference_count": 0, + "prompt_changed": true, + "formulation_changed": true, + "model_changed": true, + "checker_changed": true, + "instance_changed": true, + "solution_changed": true, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 12, + "changed_files": [ + "feasibility_check.py", + "gurobi_code.py", + "instance/large_instance_1.json", + "instance/large_instance_2.json", + "instance/large_instance_3.json", + "instance/large_instance_4.json", + "instance/large_instance_5.json", + "instance/tiny_instance.json", + "instance_schema.json", + "mathematical_formulation.md", + "problem_description.txt", + "solution_schema.json" + ] + }, + { + "case_id": "reihaneh2019", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": true, + "phase1_semantic_disposition": "automated_no_confirmed_mismatch_on_exercised_paths", + "phase1_semantic_basis": "Static triad scan, official-tiny mutation/replay, current tiny-model reproduction, and full official replay produced no case-specific semantic finding requiring manual review.", + "phase84_previous_disposition": null, + "phase84_previous_reason": null, + "resolution_note": "Released checker/reference contract replayed and closed.", + "official_pair_count": 6, + "official_accepted_count": 6, + "official_outcome_counts": { + "accepted": 6 + }, + "stored_result_mismatches": [], + "evaluator_preflight": "passed", + "mutation_raw_status": "mutation_clear", + "mutation_effective_clear": true, + "mutation_manual_disposition": null, + "static_raw_status": "flagged", + "static_max_severity": "medium", + "static_finding_codes": [ + "instance_top_fields_not_detected_in_model", + "instance_top_fields_not_detected_in_checker" + ], + "reference_count_in_worktree": 6, + "unproven_reference_count": 5, + "prompt_changed": false, + "formulation_changed": false, + "model_changed": false, + "checker_changed": true, + "instance_changed": false, + "solution_changed": false, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 1, + "changed_files": [ + "feasibility_check.py" + ] + }, + { + "case_id": "reinhardt2016", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": true, + "phase1_semantic_disposition": 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"official_outcome_counts": { + "accepted": 6 + }, + "stored_result_mismatches": [], + "evaluator_preflight": "passed", + "mutation_raw_status": "mutation_clear", + "mutation_effective_clear": true, + "mutation_manual_disposition": null, + "static_raw_status": "flagged", + "static_max_severity": "medium", + "static_finding_codes": [ + "model_objective_sense_not_detected", + "instance_top_fields_not_detected_in_model", + "instance_top_fields_not_detected_in_checker" + ], + "reference_count_in_worktree": 6, + "unproven_reference_count": 0, + "prompt_changed": false, + "formulation_changed": false, + "model_changed": false, + "checker_changed": true, + "instance_changed": false, + "solution_changed": true, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 2, + "changed_files": [ + "feasibility_check.py", + "gurobi_solution/large_solution_1.json" + ] + }, + { + "case_id": "riley2025", + "eval_ready": true, + "publication_status": 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"static_max_severity": "medium", + "static_finding_codes": [ + "solution_schema_fields_not_detected_in_checker", + "instance_top_fields_not_detected_in_checker" + ], + "reference_count_in_worktree": 6, + "unproven_reference_count": 5, + "prompt_changed": false, + "formulation_changed": false, + "model_changed": false, + "checker_changed": true, + "instance_changed": false, + "solution_changed": true, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 2, + "changed_files": [ + "feasibility_check.py", + "gurobi_solution/large_solution_1.json" + ] + }, + { + "case_id": "roberti2021", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": false, + "phase1_semantic_disposition": "confirmed_issue", + "phase1_semantic_basis": "**confirmed P0 interrupted/no-witness large-1 artifact**.", + "phase84_previous_disposition": 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"phase1_semantic_basis": "Static triad scan, official-tiny mutation/replay, current tiny-model reproduction, and full official replay produced no case-specific semantic finding requiring manual review.", + "phase84_previous_disposition": null, + "phase84_previous_reason": null, + "resolution_note": "Released checker/reference contract replayed and closed.", + "official_pair_count": 6, + "official_accepted_count": 6, + "official_outcome_counts": { + "accepted": 6 + }, + "stored_result_mismatches": [], + "evaluator_preflight": "passed", + "mutation_raw_status": "mutation_clear", + "mutation_effective_clear": true, + "mutation_manual_disposition": null, + "static_raw_status": "static_clear", + "static_max_severity": "none", + "static_finding_codes": [], + "reference_count_in_worktree": 6, + "unproven_reference_count": 5, + "prompt_changed": false, + "formulation_changed": false, + "model_changed": false, + "checker_changed": true, + "instance_changed": false, + "solution_changed": false, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 1, + "changed_files": [ + "feasibility_check.py" + ] + }, + { + "case_id": "wei2020b", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": false, + "phase1_semantic_disposition": "confirmed_issue", + "phase1_semantic_basis": "**confirmed P0 empty large-1 solution file**.", + "phase84_previous_disposition": "REFERENCE_DATA_BLOCKED", + "phase84_previous_reason": "nonaccepted=large_instance_1.json:invalid_input_file", + "resolution_note": "Released checker/reference contract replayed and closed.", + "official_pair_count": 6, + "official_accepted_count": 6, + "official_outcome_counts": { + "accepted": 6 + }, + "stored_result_mismatches": [], + "evaluator_preflight": "passed", + "mutation_raw_status": "mutation_clear", + "mutation_effective_clear": true, + "mutation_manual_disposition": null, + "static_raw_status": "static_clear", + "static_max_severity": "none", + "static_finding_codes": [], + "reference_count_in_worktree": 6, + "unproven_reference_count": 2, + "prompt_changed": false, + "formulation_changed": false, + "model_changed": false, + "checker_changed": true, + "instance_changed": false, + "solution_changed": true, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 2, + "changed_files": [ + "feasibility_check.py", + "gurobi_solution/large_solution_1.json" + ] + }, + { + "case_id": "wu2018", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": false, + "phase1_semantic_disposition": "confirmed_issue", + "phase1_semantic_basis": "**confirmed P0 interrupted/no-witness large-1 artifact with stale cache**.", + "phase84_previous_disposition": "REFERENCE_DATA_BLOCKED", + "phase84_previous_reason": "nonaccepted=large_instance_1.json:rejected", + "resolution_note": "Released checker/reference contract replayed and closed.", + "official_pair_count": 6, + "official_accepted_count": 6, + "official_outcome_counts": { + "accepted": 6 + }, + "stored_result_mismatches": [], + "evaluator_preflight": "passed", + "mutation_raw_status": "mutation_clear", + "mutation_effective_clear": true, + "mutation_manual_disposition": null, + "static_raw_status": "static_clear", + "static_max_severity": "none", + "static_finding_codes": [], + "reference_count_in_worktree": 6, + "unproven_reference_count": 5, + "prompt_changed": false, + "formulation_changed": false, + "model_changed": false, + "checker_changed": true, + "instance_changed": false, + "solution_changed": true, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 2, + "changed_files": [ + "feasibility_check.py", + "gurobi_solution/large_solution_1.json" + ] + }, + { + "case_id": "wu2022", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": false, + "phase1_semantic_disposition": "confirmed_issue", + "phase1_semantic_basis": "**confirmed P0 checker scalability blocker plus objective-binding failure**.", + "phase84_previous_disposition": "REFERENCE_DATA_BLOCKED", + "phase84_previous_reason": "nonaccepted=large_instance_1.json:rejected", + "resolution_note": "Regenerated the missing large incumbent and retained a bounded-memory sparse checker.", + "official_pair_count": 6, + "official_accepted_count": 6, + "official_outcome_counts": { + "accepted": 6 + }, + "stored_result_mismatches": [], + "evaluator_preflight": "passed", + "mutation_raw_status": "mutation_clear", + "mutation_effective_clear": true, + "mutation_manual_disposition": null, + "static_raw_status": "flagged", + "static_max_severity": "medium", + "static_finding_codes": [ + "instance_top_fields_not_detected_in_model", + "instance_top_fields_not_detected_in_checker" + ], + "reference_count_in_worktree": 6, + "unproven_reference_count": 1, + "prompt_changed": false, + "formulation_changed": false, + "model_changed": false, + "checker_changed": true, + "instance_changed": false, + "solution_changed": true, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 2, + "changed_files": [ + "feasibility_check.py", + "gurobi_solution/large_solution_1.json" + ] + }, + { + "case_id": "wu2023", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": false, + "phase1_semantic_disposition": "confirmed_issue", + "phase1_semantic_basis": "**confirmed P0 scenario-selection witness and objective bypass**.", + "phase84_previous_disposition": "DEEP_ALIGNMENT_REMAINS", + "phase84_previous_reason": "two-stage selection→t 已精确绑定；one-stage 仍只保存 t，缺 lambda/mu 证书。", + "resolution_note": "Added the omitted one-stage/two-stage decision certificate and exact binding.", + "official_pair_count": 6, + "official_accepted_count": 6, + "official_outcome_counts": { + "accepted": 6 + }, + "stored_result_mismatches": [], + "evaluator_preflight": "passed", + "mutation_raw_status": "mutation_clear", + "mutation_effective_clear": true, + "mutation_manual_disposition": null, + "static_raw_status": "flagged", + "static_max_severity": "high", + "static_finding_codes": [ + "solution_schema_fields_not_detected_in_model_output", + "solution_schema_fields_not_detected_in_checker", + "instance_top_fields_not_detected_in_model", + "instance_top_fields_not_detected_in_checker" + ], + "reference_count_in_worktree": 6, + "unproven_reference_count": 6, + "prompt_changed": true, + "formulation_changed": true, + "model_changed": true, + "checker_changed": true, + "instance_changed": false, + "solution_changed": true, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 11, + "changed_files": [ + "feasibility_check.py", + "gurobi_code.py", + "gurobi_solution/large_solution_1.json", + "gurobi_solution/large_solution_2.json", + "gurobi_solution/large_solution_3.json", + "gurobi_solution/large_solution_4.json", + "gurobi_solution/large_solution_5.json", + "gurobi_solution/tiny_solution.json", + "mathematical_formulation.md", + "problem_description.txt", + "solution_schema.json" + ] + }, + { + "case_id": "xie2019", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": false, + "phase1_semantic_disposition": "confirmed_issue", + "phase1_semantic_basis": "**confirmed P0 checker bypass for invalid QC profiles/durations; description also misstates generated profiles as supplied data**.", + "phase84_previous_disposition": "NO_DATA_REPAIR_CLOSED", + "phase84_previous_reason": "修复负 profile、反向服务区间和不完整 vessel ID 绕过，并说明确定性 profile 生成规则。", + "resolution_note": "Released checker/reference contract replayed and closed.", + "official_pair_count": 6, + "official_accepted_count": 6, + "official_outcome_counts": { + "accepted": 6 + }, + "stored_result_mismatches": [], + "evaluator_preflight": "passed", + "mutation_raw_status": "mutation_clear", + "mutation_effective_clear": true, + "mutation_manual_disposition": null, + "static_raw_status": "flagged", + "static_max_severity": "medium", + "static_finding_codes": [ + "instance_top_fields_not_detected_in_model", + "instance_top_fields_not_detected_in_checker" + ], + "reference_count_in_worktree": 6, + "unproven_reference_count": 0, + "prompt_changed": true, + "formulation_changed": false, + "model_changed": false, + "checker_changed": true, + "instance_changed": false, + "solution_changed": false, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 2, + "changed_files": [ + "feasibility_check.py", + "problem_description.txt" + ] + }, + { + "case_id": "xu2024", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": true, + "phase1_semantic_disposition": "automated_no_confirmed_mismatch_on_exercised_paths", + "phase1_semantic_basis": "Static triad scan, official-tiny mutation/replay, current tiny-model reproduction, and full official replay produced no case-specific semantic finding requiring manual review.", + "phase84_previous_disposition": null, + "phase84_previous_reason": null, + "resolution_note": "Released checker/reference contract replayed and closed.", + "official_pair_count": 6, + "official_accepted_count": 6, + "official_outcome_counts": { + "accepted": 6 + }, + "stored_result_mismatches": [], + "evaluator_preflight": "passed", + "mutation_raw_status": "mutation_clear", + "mutation_effective_clear": true, + "mutation_manual_disposition": null, + "static_raw_status": "static_clear", + "static_max_severity": "none", + "static_finding_codes": [], + "reference_count_in_worktree": 6, + "unproven_reference_count": 6, + "prompt_changed": false, + "formulation_changed": false, + "model_changed": false, + "checker_changed": true, + "instance_changed": false, + "solution_changed": false, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 1, + "changed_files": [ + "feasibility_check.py" + ] + }, + { + "case_id": "yang2013", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": true, + "phase1_semantic_disposition": "automated_no_confirmed_mismatch_on_exercised_paths", + "phase1_semantic_basis": "Static triad scan, official-tiny mutation/replay, current tiny-model reproduction, and full official replay produced no case-specific semantic finding requiring manual review.", + "phase84_previous_disposition": null, + "phase84_previous_reason": null, + "resolution_note": "Released checker/reference contract replayed and closed.", + "official_pair_count": 6, + "official_accepted_count": 6, + "official_outcome_counts": { + "accepted": 6 + }, + "stored_result_mismatches": [], + "evaluator_preflight": "passed", + "mutation_raw_status": "mutation_clear", + "mutation_effective_clear": true, + "mutation_manual_disposition": null, + "static_raw_status": "static_clear", + "static_max_severity": "none", + "static_finding_codes": [], + "reference_count_in_worktree": 6, + "unproven_reference_count": 5, + "prompt_changed": false, + "formulation_changed": false, + "model_changed": false, + "checker_changed": true, + "instance_changed": false, + "solution_changed": false, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 1, + "changed_files": [ + "feasibility_check.py" + ] + }, + { + "case_id": "zetina2019", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": true, + "phase1_semantic_disposition": "automated_no_confirmed_mismatch_on_exercised_paths", + "phase1_semantic_basis": "Static triad scan, official-tiny mutation/replay, current tiny-model reproduction, and full official replay produced no case-specific semantic finding requiring manual review.", + "phase84_previous_disposition": null, + "phase84_previous_reason": null, + "resolution_note": "Released checker/reference contract replayed and closed.", + "official_pair_count": 6, + "official_accepted_count": 6, + "official_outcome_counts": { + "accepted": 6 + }, + "stored_result_mismatches": [], + "evaluator_preflight": "passed", + "mutation_raw_status": "mutation_clear", + "mutation_effective_clear": true, + "mutation_manual_disposition": null, + "static_raw_status": "flagged", + "static_max_severity": "medium", + "static_finding_codes": [ + "instance_top_fields_not_detected_in_model", + "instance_top_fields_not_detected_in_checker" + ], + "reference_count_in_worktree": 6, + "unproven_reference_count": 5, + "prompt_changed": false, + "formulation_changed": false, + "model_changed": false, + "checker_changed": true, + "instance_changed": false, + "solution_changed": false, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 1, + "changed_files": [ + "feasibility_check.py" + ] + }, + { + "case_id": "zetina2020", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": false, + "phase1_semantic_disposition": "confirmed_issue", + "phase1_semantic_basis": "**confirmed P0 stale large-1 solution/result and stale problem-description scale**.", + "phase84_previous_disposition": "REFERENCE_DATA_BLOCKED", + "phase84_previous_reason": "nonaccepted=large_instance_1.json:rejected", + "resolution_note": "Released checker/reference contract replayed and closed.", + "official_pair_count": 6, + "official_accepted_count": 6, + "official_outcome_counts": { + "accepted": 6 + }, + "stored_result_mismatches": [], + "evaluator_preflight": "passed", + "mutation_raw_status": "mutation_clear", + "mutation_effective_clear": true, + "mutation_manual_disposition": null, + "static_raw_status": "flagged", + "static_max_severity": "medium", + "static_finding_codes": [ + "multiple_model_objective_senses" + ], + "reference_count_in_worktree": 6, + "unproven_reference_count": 5, + "prompt_changed": true, + "formulation_changed": false, + "model_changed": false, + "checker_changed": true, + "instance_changed": false, + "solution_changed": true, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 4, + "changed_files": [ + "feasibility_check.py", + "gurobi_solution/large_solution_1.json", + "problem_description.txt", + "solution_schema.json" + ] + }, + { + "case_id": "zhang2025", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": true, + "phase1_semantic_disposition": "manual_no_additional_mismatch_confirmed", + "phase1_semantic_basis": "Static missing-output finding is a dataflow false positive: selected_duties and objective are emitted and fully checked; some large references are only feasible incumbents.", + "phase84_previous_disposition": null, + "phase84_previous_reason": null, + "resolution_note": "Released checker/reference contract replayed and closed.", + "official_pair_count": 6, + "official_accepted_count": 6, + "official_outcome_counts": { + "accepted": 6 + }, + "stored_result_mismatches": [], + "evaluator_preflight": "passed", + "mutation_raw_status": "mutation_clear", + "mutation_effective_clear": true, + "mutation_manual_disposition": null, + "static_raw_status": "flagged", + "static_max_severity": "high", + "static_finding_codes": [ + "solution_schema_fields_not_detected_in_model_output", + "instance_top_fields_not_detected_in_model", + "instance_top_fields_not_detected_in_checker" + ], + "reference_count_in_worktree": 6, + "unproven_reference_count": 3, + "prompt_changed": false, + "formulation_changed": false, + "model_changed": false, + "checker_changed": true, + "instance_changed": false, + "solution_changed": false, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 1, + "changed_files": [ + "feasibility_check.py" + ] + }, + { + "case_id": "zohali2022", + "eval_ready": true, + "publication_status": "EVAL_READY_CANONICAL", + "identity_status": "canonical_independent_case", + "independent_benchmark": true, + "original_audited_92": false, + "phase1_semantic_disposition": "confirmed_issue", + "phase1_semantic_basis": "**confirmed P0 witness-less large-1 artifact accepted by stale cache**.", + "phase84_previous_disposition": "REFERENCE_DATA_BLOCKED", + "phase84_previous_reason": "nonaccepted=large_instance_1.json:rejected", + "resolution_note": "Generated a complete task/station/sequence certificate and exact setup objective.", + "official_pair_count": 6, + "official_accepted_count": 6, + "official_outcome_counts": { + "accepted": 6 + }, + "stored_result_mismatches": [], + "evaluator_preflight": "passed", + "mutation_raw_status": "mutation_clear", + "mutation_effective_clear": true, + "mutation_manual_disposition": null, + "static_raw_status": "static_clear", + "static_max_severity": "none", + "static_finding_codes": [], + "reference_count_in_worktree": 6, + "unproven_reference_count": 5, + "prompt_changed": false, + "formulation_changed": false, + "model_changed": true, + "checker_changed": true, + "instance_changed": false, + "solution_changed": true, + "stored_result_changed": false, + "solver_log_changed": false, + "changed_file_count": 3, + "changed_files": [ + "feasibility_check.py", + "gurobi_code.py", + "gurobi_solution/large_solution_1.json" + ] + } + ] +} diff --git a/audit/FINAL_AUDIT_REPORT_180.md b/audit/FINAL_AUDIT_REPORT_180.md new file mode 100644 index 0000000000000000000000000000000000000000..7e5de9857135ba6d43f83f542b55003e001a4f41 --- /dev/null +++ b/audit/FINAL_AUDIT_REPORT_180.md @@ -0,0 +1,34 @@ +# FrontierOR 180 题最终审计结论 + +生成时间：`2026-08-29T22:03:00.757677-04:00`（America/New_York） + +## 最终口径 + +- evaluator-ready 目录：**180/180** +- 独立、规范 case：**179** +- 透明兼容别名：**1**（`ostrowski2012` → `earl2005`） +- 官方 instance/solution 回放：**1095/1095 accepted** +- 随附 `gurobi_feasi_result`：**1095/1095 与当前 checker 一致** +- evaluator objective preflight：**180/180 passed** +- mutation audit：原始结果 **{'mutation_clear': 178, 'flagged': 2}**；人工复核两种合法证书后 **180/180 effective clear** + +因此，如果“全部修好”指 **不会再因为 description / released model / checker / +reference solution 的契约错位而在当前 eval 中失败**，答案是 **180/180 已闭环**。 + +但 180 个目录并不等于 180 篇独立题目：`ostrowski2012` 上游本来就是 +`earl2005` 的错标签重复。本版保留目录兼容性，同时在 prompt、formulation、model、checker +和 `CASE_IDENTITY.json` 中明确声明它是别名。因此严格统计是 **179 个独立题目 + 1 个兼容别名**。 + +## 重要边界 + +“accepted / eval-ready”表示 reference 是完整、有限、可由 checker 验证的 feasible baseline； +它不表示每一份历史 large incumbent 都重新取得了全局零 gap 最优证明。当前认证盘点仍有 +**546** 份 worktree reference 没有全局最优性证书。 + +原先 92 题子集在这一轮中 prompt 改动数为 +**0**，instance 改动数为 +**0**。全部 prompt 改动 case 和逐题文件级 +变更见 `FINAL_AUDIT_180.json`、`FINAL_CASE_MATRIX_180.csv`、`CHANGE_MANIFEST_180.json`。 + +修复 worktree 中保留了 `segundo2019` 的两份无对应 instance 的历史 solution artifact 供追溯； +它们不属于 1095 个正式配对，也会从发布目录中排除。 diff --git a/audit/FINAL_CASE_MATRIX_180.csv b/audit/FINAL_CASE_MATRIX_180.csv new file mode 100644 index 0000000000000000000000000000000000000000..fd80875a089d1b55e507ad3caadd8d2965ba7257 --- /dev/null +++ b/audit/FINAL_CASE_MATRIX_180.csv @@ -0,0 +1,181 @@ +case_id,eval_ready,publication_status,identity_status,independent_benchmark,original_audited_92,official_pair_count,official_accepted_count,evaluator_preflight,mutation_raw_status,mutation_effective_clear,static_raw_status,static_max_severity,reference_count_in_worktree,unproven_reference_count,prompt_changed,formulation_changed,model_changed,checker_changed,instance_changed,solution_changed,stored_result_changed,solver_log_changed,changed_file_count,resolution_note +adulyasak2015,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,5,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +alba2013,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,0,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +alfieri2020,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,medium,6,5,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +amaldi2013,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,4,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +araujo2015,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,static_clear,none,6,3,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +araujo2020,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,static_clear,none,6,0,False,False,False,True,False,True,False,False,2,Released checker/reference contract replayed and closed. +archetti2007,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,static_clear,none,6,3,False,False,False,True,False,True,False,False,2,Released checker/reference contract replayed and closed. +armbruster2012,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,7,7,passed,mutation_clear,True,flagged,medium,7,5,False,False,False,True,False,True,True,False,3,Released checker/reference contract replayed and closed. +arslan2021,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,static_clear,none,6,1,True,True,True,True,False,False,False,False,4,Released checker/reference contract replayed and closed. +bard2002,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,medium,6,4,False,False,False,True,True,True,False,False,13,Reconciled service-time encoding and regenerated complete references. +barnhart2000,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,0,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +bayani2024,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,5,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +belhoul2014,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,1,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +belvaux2000,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,static_clear,none,6,4,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +bentert2022,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,6,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +bergman2019,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,static_clear,none,6,5,False,False,False,True,False,True,False,False,2,Released checker/reference contract replayed and closed. +bertsimas2022,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,5,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +bertsimas2024,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,medium,6,4,False,False,True,True,False,True,False,False,7,Recomputed stale fixed-design objectives and strengthened the fixed-design check. +bettinelli2017,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,static_clear,none,6,6,False,False,False,True,False,True,False,False,2,Released checker/reference contract replayed and closed. +bierwirth2017,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,7,7,passed,mutation_clear,True,flagged,medium,7,5,False,False,False,True,False,True,True,False,3,Released checker/reference contract replayed and closed. +bode2012,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,0,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +bodur2017,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,medium,6,1,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +boland2019,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,6,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +bollapragada2001,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,medium,6,3,False,False,False,True,False,True,False,False,2,Released checker/reference contract replayed and closed. +borndorfer2007,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,7,7,passed,mutation_clear,True,flagged,medium,7,6,False,False,False,True,False,True,True,False,9,"Completed passenger path-flow certificates; all demand, capacity, and objective terms are exact." +bragin2022,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,static_clear,none,6,1,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +brandao2016,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,static_clear,none,6,0,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +bront2009,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,6,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +buchheim2018,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,3,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +byeon2022,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,medium,6,2,True,True,True,True,True,True,False,False,18,Released a finite integrated follower certificate and regenerated every bundled reference. +caprara1999,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,6,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +carosi2019,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,4,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +carvalho1999,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,0,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +carvalho2022,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,5,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +castro2021,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,0,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +chebil2015,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,6,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +chebil2019,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,static_clear,none,6,2,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +chen1999,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,3,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +cherkesly2015,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,medium,6,0,False,False,True,True,False,True,False,False,6,Implemented full LIFO semantics and regenerated feasible references. +colombi2017,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,4,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +contreras2011,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,0,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +cordeau2006,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,static_clear,none,6,3,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +cordeau2014,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,high,6,1,False,True,True,True,False,True,False,False,5,Repaired callbacks/cuts and replayed complete routing witnesses. +cordeau2019,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,medium,6,6,False,False,False,True,False,True,False,False,2,Released checker/reference contract replayed and closed. +cote2018,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,flagged,True,flagged,medium,6,0,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +cote2019,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,low,6,0,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +cunha2021,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,static_clear,none,6,6,False,False,False,True,False,True,False,False,2,Released checker/reference contract replayed and closed. +damcikurt2016,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,high,6,2,False,True,True,True,False,True,True,False,8,Replaced truncated policies with sparse complete-policy certificates and explicit defaults. +dayarian2019,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,low,6,6,True,True,True,True,False,True,True,False,12,Added ordered routes and complete f/beta/w/alpha staffing certificates; objective is exactly recomputed. +degraeve2007,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,2,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +delage2022,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,5,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +delorme2020,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,0,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +desaulniers2010,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,5,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +desaulniers2014,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,5,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +desaulniers2016,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,5,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +dienstknecht2024,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,static_clear,none,6,0,False,False,False,True,False,True,False,False,4,Released checker/reference contract replayed and closed. +earl2005,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,medium,6,0,True,True,False,True,False,False,False,False,3,Released checker/reference contract replayed and closed. +eiselt2009,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,medium,6,5,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +elci2022,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,0,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +engineer2012,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,7,7,passed,mutation_clear,True,flagged,medium,7,4,False,False,False,True,False,True,True,False,4,Released checker/reference contract replayed and closed. +fischetti1997,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,low,6,4,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +fischetti1998,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,static_clear,none,6,0,False,False,False,True,False,True,False,False,2,Released checker/reference contract replayed and closed. +fischetti2017,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,medium,6,3,True,False,False,True,False,True,False,False,3,Released checker/reference contract replayed and closed. +fleszar2022b,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,static_clear,none,6,5,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +forrest2006,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,0,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +fragkos2016,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,static_clear,none,6,5,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +fragkos2021,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,medium,6,6,False,False,False,True,False,True,False,False,7,Regenerated the stale references under the strengthened checker. +freeman2018,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,medium,6,0,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +freling2003,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,0,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +frey2017,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,medium,6,0,False,False,False,True,False,True,True,False,7,Corrected carousel type capacities and synchronized all feasibility results. +furini2016,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,5,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +furini2017,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,medium,6,6,False,False,False,True,False,True,False,False,2,Released checker/reference contract replayed and closed. +furini2019,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,critical,6,4,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +furini2021,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,critical,6,6,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +gamache1999,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,medium,6,0,False,False,False,True,False,True,False,False,6,Released checker/reference contract replayed and closed. +gangammanavar2020,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,medium,6,0,False,False,False,True,False,True,False,False,2,Released checker/reference contract replayed and closed. +garcia2011,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,0,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +gruson2021,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,0,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +gschwind2021,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,medium,6,5,False,False,True,True,False,True,False,False,8,Replaced restricted/heuristic certification with exact s-plex enumeration. +gualandi2012,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,static_clear,none,6,6,False,False,False,True,False,True,False,False,2,Released checker/reference contract replayed and closed. +guo2021,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,7,7,passed,mutation_clear,True,flagged,medium,7,6,False,False,False,True,False,True,True,False,3,Released checker/reference contract replayed and closed. +hadjar2006,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,medium,6,2,False,False,True,True,False,True,False,False,8,Removed false optimality claims and retained truthful bound/gap metadata. +hassannayebi2016,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,medium,6,4,False,False,False,True,False,True,False,False,2,Released checker/reference contract replayed and closed. +he2020,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,medium,6,5,True,True,False,True,False,False,False,False,3,Released checker/reference contract replayed and closed. +hewitt2024,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,6,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +hoffman1993,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,static_clear,none,6,3,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +hosseinian2020,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,5,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +huisman2005,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,7,7,passed,mutation_clear,True,flagged,medium,7,0,False,False,False,True,False,True,True,False,3,Released checker/reference contract replayed and closed. +ji2019,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,medium,6,5,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +juvin2023,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,0,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +kang2021,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,high,6,4,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +klose2005,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,3,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +knueven2020,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,0,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +kobayashi2021,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,static_clear,none,6,1,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +kobeaga2024,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,4,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +kowalczyk2018,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,0,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +kowalczyk2024,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,medium,6,1,False,False,False,True,False,True,False,False,2,Released checker/reference contract replayed and closed. +kress2019,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,high,6,5,False,False,True,True,True,True,False,False,9,Added complete landside lift/drop events and mandatory-job enforcement. +kreter2018,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,5,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +lai2021,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,5,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +laporte2003,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,2,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +lee1996,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,high,6,0,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +legrain2020,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,medium,6,5,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +legrain2024,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,high,6,3,True,True,True,True,True,True,False,False,12,Added complete skill-assignment witnesses and aligned instance flags/contract. +letelier2022,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,4,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +levin2017,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,static_clear,none,6,0,False,False,False,True,False,True,False,False,2,Released checker/reference contract replayed and closed. +li2020,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,static_clear,none,6,4,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +liao2020,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,medium,6,0,True,True,True,True,False,True,False,False,5,Released checker/reference contract replayed and closed. +lozano2017,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,medium,6,4,True,True,True,True,True,True,False,False,16,Stored the missing coefficients and exactly repaired fixed-leader follower solutions. +luo2017,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,3,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +mansini2020,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,static_clear,none,6,0,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +martello1999,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,static_clear,none,6,6,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +martin-iradi2024,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,low,6,5,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +mehrotra1996,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,medium,6,5,False,False,True,True,False,True,False,False,8,Replaced restricted column certification with exact graph coloring. +mingozzi1999,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,0,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +morrison2016,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,1,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +muter2010,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,low,6,3,False,False,True,True,False,True,False,False,8,Replaced the restricted route pool with an exact arc-flow certificate. +nagy2015,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,7,7,passed,mutation_clear,True,flagged,medium,7,6,False,False,False,True,False,True,True,False,3,Released checker/reference contract replayed and closed. +oliveira2020,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,7,7,passed,mutation_clear,True,flagged,medium,7,2,False,True,True,True,True,True,True,False,17,Released a compact disjunctive model and repaired invalid horizons/references. +ostrowski2012,True,EVAL_READY_LEGACY_ALIAS,legacy_alias_of_earl2005,False,False,6,6,passed,mutation_clear,True,flagged,medium,6,0,True,True,True,True,False,False,False,False,6,Converted the mislabeled duplicate into a disclosed compatibility alias of earl2005. +ozturk2020,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,7,7,passed,mutation_clear,True,static_clear,none,7,6,False,False,False,True,False,True,True,False,3,Released checker/reference contract replayed and closed. +pecin2017,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,7,7,passed,mutation_clear,True,flagged,high,7,6,False,False,False,True,False,True,True,False,3,Released checker/reference contract replayed and closed. +pedersen2024,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,static_clear,none,6,0,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +pessoa2021,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,medium,6,4,False,False,False,True,False,True,False,False,2,Released checker/reference contract replayed and closed. +pettersson2020,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,static_clear,none,6,6,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +pinnoi1997,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,medium,6,1,False,False,False,True,False,True,False,False,4,Released checker/reference contract replayed and closed. +potthoff2010,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,7,7,passed,mutation_clear,True,flagged,medium,7,0,True,True,True,True,True,True,True,False,22,Released a finite relief-point completion contract and regenerated all references. +rahimian2017,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,1,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +rahmaniani2017,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,medium,6,0,False,False,True,True,False,True,False,False,8,Released checker/reference contract replayed and closed. +rahmaniani2018,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,static_clear,none,6,0,False,False,True,True,False,False,False,False,2,Released checker/reference contract replayed and closed. +rahmaniani2020,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,1,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +rahmaniani2022,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,flagged,True,flagged,high,6,0,True,True,True,True,True,True,False,False,12,Aligned investment domains and bound every sparse investment to exact recourse. +reihaneh2019,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,5,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +reinhardt2016,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,high,6,5,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +ribeiro1994,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,medium,6,0,False,False,False,True,False,True,False,False,2,Released checker/reference contract replayed and closed. +riley2025,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,medium,6,0,False,False,True,True,False,False,False,False,2,Completed the omitted recourse verification for nonzero investments. +roberti2015,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,5,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +roberti2018,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,medium,6,5,False,False,False,True,False,True,False,False,2,Repaired satellite route endpoints and recomputed the exact objective. +roberti2021,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,medium,6,1,False,False,False,True,False,True,False,False,2,Released checker/reference contract replayed and closed. +ropke2009,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,7,7,passed,mutation_clear,True,flagged,medium,7,6,False,False,False,True,False,True,True,False,4,Released checker/reference contract replayed and closed. +roshanaei2017,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,medium,6,5,False,False,False,True,False,True,False,False,2,Released checker/reference contract replayed and closed. +rostami2021,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,0,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +rothenbacher2018,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,7,7,passed,mutation_clear,True,static_clear,none,7,6,False,False,False,True,False,True,True,False,3,Released checker/reference contract replayed and closed. +rothenbacher2019,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,medium,6,0,False,False,False,True,False,True,False,False,2,Released checker/reference contract replayed and closed. +ruiz2007,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,7,7,passed,mutation_clear,True,flagged,medium,7,4,False,False,False,True,False,True,True,False,4,Released checker/reference contract replayed and closed. +sadykov2013,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,static_clear,none,6,5,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +salemi2022,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,static_clear,none,6,4,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +samarghandi2017,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,static_clear,none,6,5,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +sansegundo2016,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,static_clear,none,6,0,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +sansegundo2023,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,6,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +savelsbergh1997,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,static_clear,none,6,2,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +schwerdfeger2016,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,0,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +segundo2019,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,5,5,passed,mutation_clear,True,static_clear,none,7,7,False,False,False,True,False,True,True,False,3,Released checker/reference contract replayed and closed. +segundo2019b,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,static_clear,none,6,4,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +seker2021,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,critical,6,0,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +song2021,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,low,6,1,False,False,False,True,False,True,False,False,2,Released checker/reference contract replayed and closed. +spliet2015,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,7,7,passed,mutation_clear,True,flagged,medium,7,6,False,False,False,True,False,True,True,False,3,Released checker/reference contract replayed and closed. +taninmis2022,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,high,6,0,True,True,True,True,False,False,False,False,4,Released checker/reference contract replayed and closed. +tran2018,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,0,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +vidal2011,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,7,7,passed,mutation_clear,True,flagged,medium,7,7,False,False,False,True,False,True,True,False,3,Released checker/reference contract replayed and closed. +walteros2020,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,0,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +wang2019,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,5,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +wang2025,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,6,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +wangk2020,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,static_clear,none,6,6,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +wangy2020,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,static_clear,none,6,6,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +watermeyer2020,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,5,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +watermeyer2022,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,medium,6,0,False,False,False,True,False,True,False,False,2,Released checker/reference contract replayed and closed. +watermeyer2023,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,medium,6,0,False,False,False,True,False,True,False,False,2,Released checker/reference contract replayed and closed. +wei2020,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,static_clear,none,6,5,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +wei2020b,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,static_clear,none,6,2,False,False,False,True,False,True,False,False,2,Released checker/reference contract replayed and closed. +wu2018,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,static_clear,none,6,5,False,False,False,True,False,True,False,False,2,Released checker/reference contract replayed and closed. +wu2022,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,medium,6,1,False,False,False,True,False,True,False,False,2,Regenerated the missing large incumbent and retained a bounded-memory sparse checker. +wu2023,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,high,6,6,True,True,True,True,False,True,False,False,11,Added the omitted one-stage/two-stage decision certificate and exact binding. +xie2019,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,medium,6,0,True,False,False,True,False,False,False,False,2,Released checker/reference contract replayed and closed. +xu2024,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,static_clear,none,6,6,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +yang2013,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,static_clear,none,6,5,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +zetina2019,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,medium,6,5,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +zetina2020,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,flagged,medium,6,5,True,False,False,True,False,True,False,False,4,Released checker/reference contract replayed and closed. +zhang2025,True,EVAL_READY_CANONICAL,canonical_independent_case,True,True,6,6,passed,mutation_clear,True,flagged,high,6,3,False,False,False,True,False,False,False,False,1,Released checker/reference contract replayed and closed. +zohali2022,True,EVAL_READY_CANONICAL,canonical_independent_case,True,False,6,6,passed,mutation_clear,True,static_clear,none,6,5,False,False,True,True,False,True,False,False,3,Generated a complete task/station/sequence certificate and exact setup objective. diff --git a/audit/GUROBI_RUNTIME_METADATA.csv b/audit/GUROBI_RUNTIME_METADATA.csv new file mode 100644 index 0000000000000000000000000000000000000000..857fc85c698fe3b566db05651c716956c16d151f --- /dev/null +++ b/audit/GUROBI_RUNTIME_METADATA.csv @@ -0,0 +1,1096 @@ +case_id,instance,solution,recommended_runtime_seconds,recommended_runtime_status,recommended_runtime_source,recommended_alignment_scope,current_rerun_seconds,current_rerun_process_timed_out,current_rerun_artifacts_exact_match,collaborator_archive_seconds,collaborator_archive_raw_time,collaborator_archive_status,collaborator_archive_solution_status,collaborator_archive_feasibility_status,collaborator_archive_reported_solution_value,reference_regeneration_seconds,reference_regeneration_model_outcome,reference_regeneration_checker_outcome,reference_regeneration_installed,reference_regeneration_solution_matches_current,solution_embedded_runtime_seconds,solution_embedded_runtime_field,final_checker_accepted,final_checker_elapsed_seconds,instance_sha256,solution_sha256,model_sha256,checker_sha256 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Current rerun wall-clock times are preferred; historical collaborator and repair-run values fill missing coverage.", + "interpretation": { + "recommended_runtime_seconds": "A convenient best-available runtime, selected by the documented source priority.", + "not_a_homogeneous_benchmark": "Historical and current runs were not measured in one controlled environment; do not treat the combined column as hardware-normalized solver benchmarking.", + "timeout_censored": "The run timed out, but the archive does not preserve an exact numeric threshold; null is retained instead of inventing a duration.", + "archive_mapping": "Each archive row is ordered as tiny, large_1, large_2, large_3, large_4, large_5." + }, + "selection_priority": [ + "current_rerun_20260831", + "historical_collaborator_archive", + "historical_reference_regeneration_20260829 (accepted and installed only)", + "current_solution_embedded_runtime", + "historical timeout-censored marker" + ], + "source_inputs": { + "collaborator_archive": { + "path": "/scratch/chonghej/LLM4OR/Reference/eval_results.zip", + "sha256": "6d3991152d8b5b8bf2e4e62633ec304490ebd707eb03b972ba6d89c55bdf196e", + "member": "eval_results/gurobi_results_all_new.csv", + "member_sha256": "7467653540d2d57f36f42811ffcb8e4bb3ee8de18728e03389b6340717104a0a" + }, + "current_rerun_root": "/scratch/chonghej/LLM4OR/Audit/GUROBI_RERUN_26_20260831", + "reference_regeneration_csv": { + "path": "/scratch/chonghej/LLM4OR/Audit/REPAIR_180_20260829/BATCH_1_TINY/REFERENCE_REGENERATION.csv", + "sha256": "6662130d8ba11fb0a1777fe23b528b364a202a6ccfe7d9a13dabbdb61aa977ec" + }, + "final_checker_replay": { + "path": "/scratch/chonghej/LLM4OR/Audit/FINAL_RECHECK_20260831/FINAL_RECHECK_ALL_1095.json", + "sha256": "ceff33f48339d388880b1c498c408182b2f86251426270365627a26eab6d1ce1" + }, + "publish_manifest": { + "path": "/scratch/chonghej/LLM4OR/FrontierOR_Audited_180_HF/PUBLISH_FILE_MANIFEST.json", + "sha256": 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"/scratch/chonghej/LLM4OR/Reference/eval_results.zip!/eval_results/gurobi_results_all_new.csv" + }, + "reference_regeneration": null, + "solution_embedded": { + "seconds": 30.00113010406494, + "field": "solve_time", + "scope": "solver_or_script_reported_runtime", + "currency": "embedded_in_current_reference_solution" + } + } + }, + { + "case_id": "zohali2022", + "instance": "large_instance_3.json", + "solution": "large_solution_3.json", + "recommended_runtime_seconds": 30.122377164661884, + "recommended_runtime_status": "observed", + "recommended_runtime_source": "current_rerun_20260831", + "recommended_alignment_scope": "exact_current_artifact_hash_match", + "current_artifacts": { + "instance_path": "zohali2022/instance/large_instance_3.json", + "solution_path": "zohali2022/gurobi_solution/large_solution_3.json", + "model_path": "zohali2022/gurobi_code.py", + "checker_path": "zohali2022/feasibility_check.py", + "instance_sha256": 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"accepted", + "artifact_alignment": { + "instance_sha256_matches": true, + "model_sha256_matches": true, + "checker_sha256_matches": true, + "solution_sha256_matches_generated_output": true + }, + "source": "/scratch/chonghej/LLM4OR/Audit/GUROBI_RERUN_26_20260831/records/zohali2022/large_instance_5.json" + }, + "collaborator_archive": { + "seconds": 3602.07, + "raw_time": "3602.07", + "status": "observed", + "tag": "A", + "feasibility_status": "True", + "reported_solution_value": "387.0", + "solution_status": "incumbent", + "scope": "historical_reported_gurobi_time", + "currency": "historical_case_and_positional_instance_mapping", + "source": "/scratch/chonghej/LLM4OR/Reference/eval_results.zip!/eval_results/gurobi_results_all_new.csv" + }, + "reference_regeneration": null, + "solution_embedded": { + "seconds": 30.00080680847168, + "field": "solve_time", + "scope": "solver_or_script_reported_runtime", + "currency": "embedded_in_current_reference_solution" + } + } + }, + { + "case_id": "zohali2022", + "instance": "tiny_instance.json", + "solution": "tiny_solution.json", + "recommended_runtime_seconds": 0.5188968786969781, + "recommended_runtime_status": "observed", + "recommended_runtime_source": "current_rerun_20260831", + "recommended_alignment_scope": "exact_current_artifact_hash_match", + "current_artifacts": { + "instance_path": "zohali2022/instance/tiny_instance.json", + "solution_path": "zohali2022/gurobi_solution/tiny_solution.json", + "model_path": "zohali2022/gurobi_code.py", + "checker_path": "zohali2022/feasibility_check.py", + "instance_sha256": "3d9e35394effc126b4feea5c26c25cbc47b2e522f5f831551d7dd1b4ef3b3fe6", + "solution_sha256": "fdeea74f9f42ea461ed07b0ab956e62ca371ecbf632489cbc2716f9253388f43", + "model_sha256": "b768df3f31fac6012d1f8406858b3abb46a228a324fbc5fe20ed6211f020b870", + "checker_sha256": "22d60bab378dccd1eb61330d258272c1b05c6f7cf159c6f72f79c50e9b47a928" + }, + "final_checker_replay": { + "accepted": true, + "feasible": true, + "elapsed_seconds": 0.03481673821806908, + "source": "/scratch/chonghej/LLM4OR/Audit/FINAL_RECHECK_20260831/FINAL_RECHECK_ALL_1095.json" + }, + "measurements": { + "current_rerun": { + "seconds": 0.5188968786969781, + "status": "observed", + "scope": "model_process_wall_clock", + "currency": "exact_current_model_instance_and_generated_solution_hashes", + "model_returncode": 0, + "model_process_timed_out": false, + "model_time_limit_seconds": 3600, + "checker_seconds": 0.06441178359091282, + "checker_outcome": "accepted", + "artifact_alignment": { + "instance_sha256_matches": true, + "model_sha256_matches": true, + "checker_sha256_matches": true, + "solution_sha256_matches_generated_output": true + }, + "source": "/scratch/chonghej/LLM4OR/Audit/GUROBI_RERUN_26_20260831/records/zohali2022/tiny_instance.json" + }, + "collaborator_archive": { + "seconds": 0.45, + "raw_time": "0.45", + "status": "observed", + "tag": "A", + "feasibility_status": "True", + "reported_solution_value": "61.0", + "solution_status": "optimal", + "scope": "historical_reported_gurobi_time", + "currency": "historical_case_and_positional_instance_mapping", + "source": "/scratch/chonghej/LLM4OR/Reference/eval_results.zip!/eval_results/gurobi_results_all_new.csv" + }, + "reference_regeneration": null, + "solution_embedded": { + "seconds": 0.36026906967163086, + "field": "solve_time", + "scope": "solver_or_script_reported_runtime", + "currency": "embedded_in_current_reference_solution" + } + } + } + ] +} diff --git a/audit/GUROBI_RUNTIME_METADATA_README.md b/audit/GUROBI_RUNTIME_METADATA_README.md new file mode 100644 index 0000000000000000000000000000000000000000..dc6ca48cca183b36aee3dcd31e5c47bf4c1a35da --- /dev/null +++ b/audit/GUROBI_RUNTIME_METADATA_README.md @@ -0,0 +1,51 @@ +# Gurobi runtime metadata + +This supplement adds provenance-aware runtime metadata for all 1,095 official +instance/reference-solution pairs. It does not change any instance, Gurobi model, +checker, feasibility result, or reference solution. + +## Coverage + +- Cases: **180** +- Official instance/reference pairs: **1095** +- Reference solutions accepted by the final checker replay: **1095** +- Numeric best-available runtimes: **1094 / 1095** +- Timeout-censored records without an exact numeric duration: **1** +- Fully unavailable runtimes: **0** + +Recommended-source breakdown: + +- Current 2026-08-31 reruns, exact model/instance/checker/generated-solution hash alignment: **158** +- Historical collaborator archive (`gurobi_time`): **921 numeric + 1 timeout-censored** +- Historical 2026-08-29 accepted and installed repair runs whose generated solution + hash still equals the current reference solution: **15** + +The only non-numeric entry is `castro2021/large_instance_4.json`. The collaborator +archive stores `time_out`, but no numeric timeout threshold. It remains null and is +marked `timeout_censored`; no duration was invented. + +## How to use the files + +- `GUROBI_RUNTIME_METADATA.csv` is the flat, analysis-friendly table. +- `GUROBI_RUNTIME_METADATA.json` retains all available measurements and provenance. +- Use `recommended_runtime_seconds` for convenient coverage, together with + `recommended_runtime_source` and `recommended_runtime_status`. +- Use `current_rerun_seconds` when a homogeneous interpretation of the current rerun + campaign matters. The merged recommended column is not hardware-normalized and + should not be presented as a controlled solver benchmark. + +## Mapping and caveats + +The collaborator CSV contains six-element arrays ordered as `tiny`, `large_1`, +`large_2`, `large_3`, `large_4`, `large_5`. Historical records are mapped only to +the same case ID and positional instance name. They establish a historical Gurobi +run on that benchmark slot, not that the historical solution bytes equal the current +reference solution. + +The archive contains 185 case IDs. Of the current 180 cases, 179 occur in the archive; +`ostrowski2012` is absent. Six archive-only cases are retained only in the JSON summary +and are not inserted as release records. The current reruns cover all six +`ostrowski2012` instances and the two extra repair cases needed before the accepted +repair-run fallback is used. + +Generated at `2026-09-01T00:17:02.027028+00:00`. diff --git a/audit/GUROBI_RUNTIME_METADATA_SHA256SUMS b/audit/GUROBI_RUNTIME_METADATA_SHA256SUMS new file mode 100644 index 0000000000000000000000000000000000000000..4adeeb3d778dfde0f54eac2531337b675d219297 --- /dev/null +++ b/audit/GUROBI_RUNTIME_METADATA_SHA256SUMS @@ -0,0 +1,3 @@ +51bd174bccf9593a785fd0017e6584e2ba6ba21abda0fe23a11aeed3b209cc03 GUROBI_RUNTIME_METADATA.json +4ea84be503128042b51482436625716537d464fb47fa9ab76f7942649b09da17 GUROBI_RUNTIME_METADATA.csv +d1aa53aaae64017c13b4bc70403b9825f22b017bd782e082170e3fee9c07789d GUROBI_RUNTIME_METADATA_README.md diff --git a/audit/OFFICIAL_REPLAY_180.csv b/audit/OFFICIAL_REPLAY_180.csv new file mode 100644 index 0000000000000000000000000000000000000000..44002f116e26485d91d508862c6c2a11141c0674 --- /dev/null +++ b/audit/OFFICIAL_REPLAY_180.csv @@ -0,0 +1,1096 @@ +case_id,instance,solution,outcome,feasible,stored_feasible,matches_stored,elapsed_seconds,instance_bytes,solution_bytes,error +adulyasak2015,large_instance_1.json,large_solution_1.json,accepted,True,True,True,14.460868823342025,3878865,690, +adulyasak2015,large_instance_2.json,large_solution_2.json,accepted,True,True,True,14.166020375676453,3879010,194, +adulyasak2015,large_instance_3.json,large_solution_3.json,accepted,True,True,True,13.956253913231194,3879903,179, +adulyasak2015,large_instance_4.json,large_solution_4.json,accepted,True,True,True,14.918900853022933,3878979,178, +adulyasak2015,large_instance_5.json,large_solution_5.json,accepted,True,True,True,14.662268097512424,3879025,193, +adulyasak2015,tiny_instance.json,tiny_solution.json,accepted,True,True,True,0.1543311793357134,39960,657, +alba2013,large_instance_1.json,large_solution_1.json,accepted,True,True,True,0.0350881852209568,18174,657, +alba2013,large_instance_2.json,large_solution_2.json,accepted,True,True,True,0.03193289786577225,18013,653, +alba2013,large_instance_3.json,large_solution_3.json,accepted,True,True,True,0.03729035705327988,19476,679, +alba2013,large_instance_4.json,large_solution_4.json,accepted,True,True,True,0.04271331522613764,19188,679, +alba2013,large_instance_5.json,large_solution_5.json,accepted,True,True,True,0.030811572447419167,19226,677, +alba2013,tiny_instance.json,tiny_solution.json,accepted,True,True,True,0.034240528009831905,5649,463, +alfieri2020,large_instance_1.json,large_solution_1.json,accepted,True,True,True,0.05567606445401907,1395266,29414, +alfieri2020,large_instance_2.json,large_solution_2.json,accepted,True,True,True,0.054729828611016273,1353121,29467, +alfieri2020,large_instance_3.json,large_solution_3.json,accepted,True,True,True,0.056117323227226734,1395983,29696, +alfieri2020,large_instance_4.json,large_solution_4.json,accepted,True,True,True,0.050998800434172153,1384612,30185, +alfieri2020,large_instance_5.json,large_solution_5.json,accepted,True,True,True,0.05674364883452654,1405622,29796, +alfieri2020,tiny_instance.json,tiny_solution.json,accepted,True,True,True,0.030511262826621532,2419,405, +amaldi2013,large_instance_1.json,large_solution_1.json,accepted,True,True,True,0.09392297174781561,17938,7408, +amaldi2013,large_instance_2.json,large_solution_2.json,accepted,True,True,True,0.09711228962987661,18370,7577, +amaldi2013,large_instance_3.json,large_solution_3.json,accepted,True,True,True,0.09213683102279902,18390,10213, +amaldi2013,large_instance_4.json,large_solution_4.json,accepted,True,True,True,0.11833157204091549,18626,8335, +amaldi2013,large_instance_5.json,large_solution_5.json,accepted,True,True,True,0.10785142425447702,19083,11044, +amaldi2013,tiny_instance.json,tiny_solution.json,accepted,True,True,True,0.09315706603229046,8528,4047, +araujo2015,large_instance_1.json,large_solution_1.json,accepted,True,True,True,0.04338119272142649,9148,24144, +araujo2015,large_instance_2.json,large_solution_2.json,accepted,True,True,True,0.03183860704302788,9460,24256, +araujo2015,large_instance_3.json,large_solution_3.json,accepted,True,True,True,0.03037027269601822,9447,24369, +araujo2015,large_instance_4.json,large_solution_4.json,accepted,True,True,True,0.030990218743681908,8828,23791, +araujo2015,large_instance_5.json,large_solution_5.json,accepted,True,True,True,0.03181081544607878,9337,24085, +araujo2015,tiny_instance.json,tiny_solution.json,accepted,True,True,True,0.029475956223905087,1394,2354, +araujo2020,large_instance_1.json,large_solution_1.json,accepted,True,True,True,0.03275698982179165,32281,6354, +araujo2020,large_instance_2.json,large_solution_2.json,accepted,True,True,True,0.037322474643588066,32336,6364, 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"official_accepted": true, + "objective_tamper_rejected": true, + "variable_tamper": "alpha.1,1,0,0,G1", + "variable_tamper_rejected": true, + "seconds": { + "official": 35.199577995575964, + "objective_tamper": 36.41363456379622, + "variable_tamper": 36.167177928611636 + } + }, + { + "case": "taninmis2022", + "instance": "tiny_instance.json", + "official_accepted": true, + "objective_tamper_rejected": true, + "variable_tamper": "alpha.1,1,0,0,G1", + "variable_tamper_rejected": true, + "seconds": { + "official": 0.09457169845700264, + "objective_tamper": 0.08949729986488819, + "variable_tamper": 0.09095005225390196 + } + } + ] +} diff --git a/audit/SOLVERLESS_CHECKER_AUDIT.md b/audit/SOLVERLESS_CHECKER_AUDIT.md new file mode 100644 index 0000000000000000000000000000000000000000..47bc7d70604e0c3af091e5b8d3c882afdbeb2c0f --- /dev/null +++ b/audit/SOLVERLESS_CHECKER_AUDIT.md @@ -0,0 +1,39 @@ +# Solver-free checker audit + +Date: 2026-09-05 + +Nine checkers previously performed runtime optimization. Their 54 official +solutions were regenerated or completed locally and now include every variable +needed for direct feasibility and objective checks. Sparse maps omit exact zeros. + +| Case | Complete witness representation | Runtime optimization | +| --- | --- | --- | +| adulyasak2015 | `model_variables` + trusted coefficient snapshot | none | +| bertsimas2024 | `model_variables` + trusted quadratic snapshot | none | +| cote2018 | `model_variables` + trusted arc-flow snapshot | none | +| liao2020 | `model_variables` + trusted coefficient snapshot | none | +| lozano2017 | complete leader `x` and follower `y` | none | +| rahmaniani2018 | `model_variables` + trusted coefficient snapshot | none | +| rahmaniani2022 | `model_variables` + trusted coefficient snapshot | none | +| riley2025 | `model_variables`, checked by direct equations | none | +| taninmis2022 | named dense operational maps, checked directly | none | + +## Regression result + +- Official instance/solution pairs accepted: 54/54. +- Objective-tampered candidates rejected: 54/54. +- Complete-variable-tampered candidates rejected: 54/54. +- Static imports/calls to Gurobi, SciPy optimizers, HiGHS, and CVXPY in these + checkers: 0. +- Tests set `GRB_LICENSE_FILE=/definitely/missing`. + +Machine-readable per-pair timings and mutation records are stored in +`SOLVERLESS_CHECKER_AUDIT.json`. + +## Scope + +These checkers prove feasibility and objective consistency of the submitted +complete witness. They do not run branch-and-bound or recourse optimization and +therefore do not independently prove that an integer follower or recourse vector +is globally optimal. Benchmark quality comparisons continue to use the submitted +objective value after it has been recomputed from the validated variables. diff --git a/audit/STATIC_SOLVER_DEPENDENCY_SCAN.txt b/audit/STATIC_SOLVER_DEPENDENCY_SCAN.txt new file mode 100644 index 0000000000000000000000000000000000000000..018f9db76ee1b5ac40415d2f4cd35bc6787275cb --- /dev/null +++ b/audit/STATIC_SOLVER_DEPENDENCY_SCAN.txt @@ -0,0 +1,8 @@ +Audit date: 2026-09-05 +Scope: feasibility_check.py for the nine former runtime-solver cases, plus solverless_witness.py +Patterns: gurobipy imports, scipy.optimize imports, cvxpy imports, .optimize() calls, gp. references, GRB references +Findings: 0 + +The offline generation files gurobi_code.py and build_solverless_assets.py are +outside this runtime-checker scan. They may use Gurobi to regenerate reference +solutions and trusted coefficient snapshots, but feasibility checking does not. diff --git a/audit/audit_solverless_checkers.py b/audit/audit_solverless_checkers.py new file mode 100644 index 0000000000000000000000000000000000000000..1454b651450d7192d646327b09e0b580f1a21f16 --- /dev/null +++ b/audit/audit_solverless_checkers.py @@ -0,0 +1,118 @@ +#!/usr/bin/env python3 +"""Regression audit for the nine solver-free complete-witness checkers.""" + +import argparse +import copy +import json +import os +from pathlib import Path +import subprocess +import sys +import tempfile +import time + +CASES = ( + "adulyasak2015", "bertsimas2024", "cote2018", "liao2020", + "lozano2017", "rahmaniani2018", "rahmaniani2022", "riley2025", + "taninmis2022", +) + + +def solution_for(case, instance): + name = "tiny_solution.json" if instance.name == "tiny_instance.json" else instance.name.replace("instance", "solution") + return case / "gurobi_solution" / name + + +def run_checker(case, instance, solution): + with tempfile.NamedTemporaryFile(suffix=".json") as output: + started = time.monotonic() + environment = os.environ.copy() + environment["GRB_LICENSE_FILE"] = "/definitely/missing" + process = subprocess.run( + [sys.executable, str(case / "feasibility_check.py"), + "--instance_path", str(instance), "--solution_path", str(solution), + "--result_path", output.name], + text=True, capture_output=True, timeout=600, + env=environment, + ) + elapsed = time.monotonic() - started + output.seek(0) + result = json.load(output) + return process.returncode, result, elapsed + + +def write_candidate(candidate): + handle = tempfile.NamedTemporaryFile(mode="w", suffix=".json", delete=False) + json.dump(candidate, handle, allow_nan=False) + handle.close() + return Path(handle.name) + + +def mutate_variable(case_name, solution): + mutated = copy.deepcopy(solution) + if isinstance(mutated.get("model_variables"), dict) and mutated["model_variables"]: + name = next(iter(mutated["model_variables"])) + mutated["model_variables"][name] = float(mutated["model_variables"][name]) + 0.5 + return mutated, name + if case_name == "lozano2017": + mutated["y"][0] = float(mutated["y"][0]) + 0.5 + return mutated, "y[0]" + if case_name == "taninmis2022": + name = next(iter(mutated["alpha"])) + mutated["alpha"][name] = 0.5 + return mutated, "alpha." + name + raise RuntimeError(f"no variable mutation for {case_name}") + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--root", type=Path, default=Path(__file__).with_name("current_main_code")) + parser.add_argument("--output", type=Path, default=Path(__file__).with_name("solverless_checker_audit.json")) + args = parser.parse_args() + records = [] + for case_name in CASES: + case = args.root / case_name + for instance in sorted((case / "instance").glob("*.json")): + solution_path = solution_for(case, instance) + code, result, elapsed = run_checker(case, instance, solution_path) + with open(solution_path) as handle: + solution = json.load(handle) + objective_mutation = copy.deepcopy(solution) + objective_mutation["objective_value"] = float(solution["objective_value"]) + max(10.0, abs(float(solution["objective_value"])) * 0.01) + objective_path = write_candidate(objective_mutation) + try: + _, objective_result, objective_elapsed = run_checker(case, instance, objective_path) + finally: + objective_path.unlink(missing_ok=True) + variable_mutation, variable_name = mutate_variable(case_name, solution) + variable_path = write_candidate(variable_mutation) + try: + _, variable_result, variable_elapsed = run_checker(case, instance, variable_path) + finally: + variable_path.unlink(missing_ok=True) + record = { + "case": case_name, "instance": instance.name, + "official_accepted": code == 0 and result.get("feasible") is True, + "objective_tamper_rejected": objective_result.get("feasible") is False, + "variable_tamper": variable_name, + "variable_tamper_rejected": variable_result.get("feasible") is False, + "seconds": {"official": elapsed, "objective_tamper": objective_elapsed, "variable_tamper": variable_elapsed}, + } + records.append(record) + print(json.dumps(record), flush=True) + summary = { + "cases": len(CASES), "pairs": len(records), + "official_accepted": sum(x["official_accepted"] for x in records), + "objective_tamper_rejected": sum(x["objective_tamper_rejected"] for x in records), + "variable_tamper_rejected": sum(x["variable_tamper_rejected"] for x in records), + "records": records, + } + with open(args.output, "w") as handle: + json.dump(summary, handle, indent=2) + handle.write("\n") + if any(not x[key] for x in records for key in ("official_accepted", "objective_tamper_rejected", "variable_tamper_rejected")): + raise SystemExit(1) + + +if __name__ == "__main__": + main() diff --git a/audit/final_gates/CHECKER_MUTATION_AUDIT.csv b/audit/final_gates/CHECKER_MUTATION_AUDIT.csv new file mode 100644 index 0000000000000000000000000000000000000000..7a4c002f642e026b88770f0603ebdfc017239b93 --- /dev/null +++ b/audit/final_gates/CHECKER_MUTATION_AUDIT.csv @@ -0,0 +1,181 @@ +case_id,status,max_severity,baseline,finding_codes,evidence +adulyasak2015,mutation_clear,none,accepted,, +alba2013,mutation_clear,none,accepted,, +alfieri2020,mutation_clear,none,accepted,, +amaldi2013,mutation_clear,none,accepted,, +araujo2015,mutation_clear,none,accepted,, +araujo2020,mutation_clear,none,accepted,, +archetti2007,mutation_clear,none,accepted,, +armbruster2012,mutation_clear,none,accepted,, +arslan2021,mutation_clear,none,accepted,, +bard2002,mutation_clear,none,accepted,, +barnhart2000,mutation_clear,none,accepted,, +bayani2024,mutation_clear,none,accepted,, +belhoul2014,mutation_clear,none,accepted,, +belvaux2000,mutation_clear,none,accepted,, +bentert2022,mutation_clear,none,accepted,, +bergman2019,mutation_clear,none,accepted,, +bertsimas2022,mutation_clear,none,accepted,, +bertsimas2024,mutation_clear,none,accepted,, +bettinelli2017,mutation_clear,none,accepted,, +bierwirth2017,mutation_clear,none,accepted,, +bode2012,mutation_clear,none,accepted,, +bodur2017,mutation_clear,none,accepted,, +boland2019,mutation_clear,none,accepted,, +bollapragada2001,mutation_clear,none,accepted,, +borndorfer2007,mutation_clear,none,accepted,, +bragin2022,mutation_clear,none,accepted,, +brandao2016,mutation_clear,none,accepted,, +bront2009,mutation_clear,none,accepted,, +buchheim2018,mutation_clear,none,accepted,, +byeon2022,mutation_clear,none,accepted,, +caprara1999,mutation_clear,none,accepted,, +carosi2019,mutation_clear,none,accepted,, +carvalho1999,mutation_clear,none,accepted,, +carvalho2022,mutation_clear,none,accepted,, +castro2021,mutation_clear,none,accepted,, +chebil2015,mutation_clear,none,accepted,, +chebil2019,mutation_clear,none,accepted,, +chen1999,mutation_clear,none,accepted,, +cherkesly2015,mutation_clear,none,accepted,, +colombi2017,mutation_clear,none,accepted,, +contreras2011,mutation_clear,none,accepted,, +cordeau2006,mutation_clear,none,accepted,, +cordeau2014,mutation_clear,none,accepted,, +cordeau2019,mutation_clear,none,accepted,, +cote2018,flagged,critical,accepted,checker_accepts_only_objective_fields,checker returned feasible=true for a solution without valid decisions +cote2019,mutation_clear,none,accepted,, +cunha2021,mutation_clear,none,accepted,, +damcikurt2016,mutation_clear,none,accepted,, +dayarian2019,mutation_clear,none,accepted,, +degraeve2007,mutation_clear,none,accepted,, +delage2022,mutation_clear,none,accepted,, +delorme2020,mutation_clear,none,accepted,, +desaulniers2010,mutation_clear,none,accepted,, +desaulniers2014,mutation_clear,none,accepted,, +desaulniers2016,mutation_clear,none,accepted,, +dienstknecht2024,mutation_clear,none,accepted,, +earl2005,mutation_clear,none,accepted,, +eiselt2009,mutation_clear,none,accepted,, +elci2022,mutation_clear,none,accepted,, +engineer2012,mutation_clear,none,accepted,, +fischetti1997,mutation_clear,none,accepted,, +fischetti1998,mutation_clear,none,accepted,, +fischetti2017,mutation_clear,none,accepted,, +fleszar2022b,mutation_clear,none,accepted,, +forrest2006,mutation_clear,none,accepted,, +fragkos2016,mutation_clear,none,accepted,, +fragkos2021,mutation_clear,none,accepted,, +freeman2018,mutation_clear,none,accepted,, +freling2003,mutation_clear,none,accepted,, +frey2017,mutation_clear,none,accepted,, +furini2016,mutation_clear,none,accepted,, +furini2017,mutation_clear,none,accepted,, +furini2019,mutation_clear,none,accepted,, +furini2021,mutation_clear,none,accepted,, +gamache1999,mutation_clear,none,accepted,, +gangammanavar2020,mutation_clear,none,accepted,, +garcia2011,mutation_clear,none,accepted,, +gruson2021,mutation_clear,none,accepted,, +gschwind2021,mutation_clear,none,accepted,, +gualandi2012,mutation_clear,none,accepted,, +guo2021,mutation_clear,none,accepted,, +hadjar2006,mutation_clear,none,accepted,, +hassannayebi2016,mutation_clear,none,accepted,, +he2020,mutation_clear,none,accepted,, +hewitt2024,mutation_clear,none,accepted,, +hoffman1993,mutation_clear,none,accepted,, +hosseinian2020,mutation_clear,none,accepted,, +huisman2005,mutation_clear,none,accepted,, +ji2019,mutation_clear,none,accepted,, +juvin2023,mutation_clear,none,accepted,, +kang2021,mutation_clear,none,accepted,, +klose2005,mutation_clear,none,accepted,, +knueven2020,mutation_clear,none,accepted,, +kobayashi2021,mutation_clear,none,accepted,, +kobeaga2024,mutation_clear,none,accepted,, +kowalczyk2018,mutation_clear,none,accepted,, +kowalczyk2024,mutation_clear,none,accepted,, +kress2019,mutation_clear,none,accepted,, +kreter2018,mutation_clear,none,accepted,, +lai2021,mutation_clear,none,accepted,, +laporte2003,mutation_clear,none,accepted,, +lee1996,mutation_clear,none,accepted,, +legrain2020,mutation_clear,none,accepted,, +legrain2024,mutation_clear,none,accepted,, +letelier2022,mutation_clear,none,accepted,, +levin2017,mutation_clear,none,accepted,, +li2020,mutation_clear,none,accepted,, +liao2020,mutation_clear,none,accepted,, +lozano2017,mutation_clear,none,accepted,, +luo2017,mutation_clear,none,accepted,, +mansini2020,mutation_clear,none,accepted,, +martello1999,mutation_clear,none,accepted,, +martin-iradi2024,mutation_clear,none,accepted,, +mehrotra1996,mutation_clear,none,accepted,, +mingozzi1999,mutation_clear,none,accepted,, +morrison2016,mutation_clear,none,accepted,, +muter2010,mutation_clear,none,accepted,, +nagy2015,mutation_clear,none,accepted,, +oliveira2020,mutation_clear,none,accepted,, +ostrowski2012,mutation_clear,none,accepted,, +ozturk2020,mutation_clear,none,accepted,, +pecin2017,mutation_clear,none,accepted,, +pedersen2024,mutation_clear,none,accepted,, +pessoa2021,mutation_clear,none,accepted,, +pettersson2020,mutation_clear,none,accepted,, +pinnoi1997,mutation_clear,none,accepted,, +potthoff2010,mutation_clear,none,accepted,, +rahimian2017,mutation_clear,none,accepted,, +rahmaniani2017,mutation_clear,none,accepted,, +rahmaniani2018,mutation_clear,none,accepted,, +rahmaniani2020,mutation_clear,none,accepted,, +rahmaniani2022,flagged,high,accepted,checker_accepts_erased_schema_decision_field,erase:storage_energy_investments +reihaneh2019,mutation_clear,none,accepted,, +reinhardt2016,mutation_clear,none,accepted,, +ribeiro1994,mutation_clear,none,accepted,, +riley2025,mutation_clear,none,accepted,, +roberti2015,mutation_clear,none,accepted,, +roberti2018,mutation_clear,none,accepted,, +roberti2021,mutation_clear,none,accepted,, +ropke2009,mutation_clear,none,accepted,, +roshanaei2017,mutation_clear,none,accepted,, +rostami2021,mutation_clear,none,accepted,, +rothenbacher2018,mutation_clear,none,accepted,, +rothenbacher2019,mutation_clear,none,accepted,, +ruiz2007,mutation_clear,none,accepted,, +sadykov2013,mutation_clear,none,accepted,, +salemi2022,mutation_clear,none,accepted,, +samarghandi2017,mutation_clear,none,accepted,, +sansegundo2016,mutation_clear,none,accepted,, +sansegundo2023,mutation_clear,none,accepted,, +savelsbergh1997,mutation_clear,none,accepted,, +schwerdfeger2016,mutation_clear,none,accepted,, +segundo2019,mutation_clear,none,accepted,, +segundo2019b,mutation_clear,none,accepted,, +seker2021,mutation_clear,none,accepted,, +song2021,mutation_clear,none,accepted,, +spliet2015,mutation_clear,none,accepted,, +taninmis2022,mutation_clear,none,accepted,, +tran2018,mutation_clear,none,accepted,, +vidal2011,mutation_clear,none,accepted,, +walteros2020,mutation_clear,none,accepted,, +wang2019,mutation_clear,none,accepted,, +wang2025,mutation_clear,none,accepted,, +wangk2020,mutation_clear,none,accepted,, +wangy2020,mutation_clear,none,accepted,, +watermeyer2020,mutation_clear,none,accepted,, +watermeyer2022,mutation_clear,none,accepted,, +watermeyer2023,mutation_clear,none,accepted,, +wei2020,mutation_clear,none,accepted,, +wei2020b,mutation_clear,none,accepted,, +wu2018,mutation_clear,none,accepted,, +wu2022,mutation_clear,none,accepted,, +wu2023,mutation_clear,none,accepted,, +xie2019,mutation_clear,none,accepted,, +xu2024,mutation_clear,none,accepted,, +yang2013,mutation_clear,none,accepted,, +zetina2019,mutation_clear,none,accepted,, +zetina2020,mutation_clear,none,accepted,, +zhang2025,mutation_clear,none,accepted,, +zohali2022,mutation_clear,none,accepted,, diff --git a/audit/final_gates/EVALUATOR_PREFLIGHT_AUDIT.csv b/audit/final_gates/EVALUATOR_PREFLIGHT_AUDIT.csv new file mode 100644 index 0000000000000000000000000000000000000000..afb1235372eace238a48188ee9938d256256569f --- /dev/null +++ b/audit/final_gates/EVALUATOR_PREFLIGHT_AUDIT.csv @@ -0,0 +1,181 @@ +case_id,status,overlay_used,error,effective_checker +adulyasak2015,passed,True,,/scratch/chonghej/LLM4OR/.audit_sources/frontieror-audit/code/trusted_checkers/adulyasak2015/feasibility_check.py +alba2013,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/alba2013/feasibility_check.py +alfieri2020,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/alfieri2020/feasibility_check.py +amaldi2013,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/amaldi2013/feasibility_check.py +araujo2015,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/araujo2015/feasibility_check.py +araujo2020,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/araujo2020/feasibility_check.py +archetti2007,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/archetti2007/feasibility_check.py +armbruster2012,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/armbruster2012/feasibility_check.py +arslan2021,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/arslan2021/feasibility_check.py +bard2002,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/bard2002/feasibility_check.py +barnhart2000,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/barnhart2000/feasibility_check.py +bayani2024,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/bayani2024/feasibility_check.py +belhoul2014,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/belhoul2014/feasibility_check.py +belvaux2000,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/belvaux2000/feasibility_check.py +bentert2022,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/bentert2022/feasibility_check.py +bergman2019,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/bergman2019/feasibility_check.py +bertsimas2022,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/bertsimas2022/feasibility_check.py +bertsimas2024,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/bertsimas2024/feasibility_check.py +bettinelli2017,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/bettinelli2017/feasibility_check.py +bierwirth2017,passed,True,,/scratch/chonghej/LLM4OR/.audit_sources/frontieror-audit/code/trusted_checkers/bierwirth2017/feasibility_check.py +bode2012,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/bode2012/feasibility_check.py +bodur2017,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/bodur2017/feasibility_check.py +boland2019,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/boland2019/feasibility_check.py +bollapragada2001,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/bollapragada2001/feasibility_check.py +borndorfer2007,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/borndorfer2007/feasibility_check.py +bragin2022,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/bragin2022/feasibility_check.py +brandao2016,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/brandao2016/feasibility_check.py +bront2009,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/bront2009/feasibility_check.py +buchheim2018,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/buchheim2018/feasibility_check.py +byeon2022,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/byeon2022/feasibility_check.py +caprara1999,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/caprara1999/feasibility_check.py +carosi2019,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/carosi2019/feasibility_check.py +carvalho1999,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/carvalho1999/feasibility_check.py +carvalho2022,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/carvalho2022/feasibility_check.py +castro2021,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/castro2021/feasibility_check.py +chebil2015,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/chebil2015/feasibility_check.py +chebil2019,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/chebil2019/feasibility_check.py +chen1999,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/chen1999/feasibility_check.py +cherkesly2015,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/cherkesly2015/feasibility_check.py +colombi2017,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/colombi2017/feasibility_check.py +contreras2011,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/contreras2011/feasibility_check.py +cordeau2006,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/cordeau2006/feasibility_check.py +cordeau2014,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/cordeau2014/feasibility_check.py +cordeau2019,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/cordeau2019/feasibility_check.py +cote2018,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/cote2018/feasibility_check.py +cote2019,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/cote2019/feasibility_check.py +cunha2021,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/cunha2021/feasibility_check.py +damcikurt2016,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/damcikurt2016/feasibility_check.py +dayarian2019,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/dayarian2019/feasibility_check.py +degraeve2007,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/degraeve2007/feasibility_check.py +delage2022,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/delage2022/feasibility_check.py +delorme2020,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/delorme2020/feasibility_check.py +desaulniers2010,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/desaulniers2010/feasibility_check.py +desaulniers2014,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/desaulniers2014/feasibility_check.py +desaulniers2016,passed,False,,/scratch/chonghej/LLM4OR/FrontierOR_repair_92/desaulniers2016/feasibility_check.py 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0000000000000000000000000000000000000000..32eeb9c87b1c2faf104503c249f5fe8ea2b0b1b9 --- /dev/null +++ b/audit/final_gates/REFERENCE_CERTIFICATION_AUDIT.csv @@ -0,0 +1,1098 @@ +case_id,solution,instance,instance_exists,classification,objective_value,reported_gap,status_fields,error +adulyasak2015,large_solution_1.json,large_instance_1.json,True,explicit_incumbent,24264.431698905697,0.2828023453586586,"{""status"": 9}", +adulyasak2015,large_solution_2.json,large_instance_2.json,True,explicit_incumbent,159840.11349897826,0.2965734591727916,"{""status"": 9}", +adulyasak2015,large_solution_3.json,large_instance_3.json,True,explicit_incumbent,47553.855849755346,1.0,"{""status"": 9}", +adulyasak2015,large_solution_4.json,large_instance_4.json,True,explicit_incumbent,24414.72888062327,1.0,"{""status"": 9}", +adulyasak2015,large_solution_5.json,large_instance_5.json,True,explicit_incumbent,48829.45785608655,0.3428716206683382,"{""status"": 9}", 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""TIME_LIMIT_FEASIBLE""}", +samarghandi2017,large_solution_2.json,large_instance_2.json,True,explicit_incumbent,2095.0,0.6869150085703264,"{""status"": ""TIME_LIMIT_FEASIBLE""}", +samarghandi2017,large_solution_3.json,large_instance_3.json,True,explicit_incumbent,2217.0,0.4812810103743796,"{""status"": ""TIME_LIMIT_FEASIBLE""}", +samarghandi2017,large_solution_4.json,large_instance_4.json,True,explicit_incumbent,2350.0,0.6717498500635807,"{""status"": ""TIME_LIMIT_FEASIBLE""}", +samarghandi2017,large_solution_5.json,large_instance_5.json,True,explicit_incumbent,2979.0,0.6874786377904913,"{""status"": ""TIME_LIMIT_FEASIBLE""}", +samarghandi2017,tiny_solution.json,tiny_instance.json,True,solver_optimal,683.0,0.0,"{""status"": ""OPTIMAL""}", +sansegundo2016,large_solution_1.json,large_instance_1.json,True,solver_optimal,2.0,0.0,"{""status"": 2}", +sansegundo2016,large_solution_2.json,large_instance_2.json,True,solver_optimal,3.0,0.0,"{""status"": 2}", 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""status_description"": ""OPTIMAL""}", +schwerdfeger2016,large_solution_1.json,large_instance_1.json,True,solver_optimal,0.0005373411979751675,,"{""status"": ""optimal""}", +schwerdfeger2016,large_solution_2.json,large_instance_2.json,True,solver_optimal,0.0006228828677832431,,"{""status"": ""optimal""}", +schwerdfeger2016,large_solution_3.json,large_instance_3.json,True,solver_optimal,0.0005776318640231615,,"{""status"": ""optimal""}", +schwerdfeger2016,large_solution_4.json,large_instance_4.json,True,solver_optimal,0.000815353538194365,,"{""status"": ""optimal""}", +schwerdfeger2016,large_solution_5.json,large_instance_5.json,True,solver_optimal,0.0015777107798115699,,"{""status"": ""optimal""}", +schwerdfeger2016,tiny_solution.json,tiny_instance.json,True,solver_optimal,0.31633605673949555,,"{""status"": ""optimal""}", +segundo2019,large_solution_1.json,large_instance_1.json,True,objective_without_certificate,-0.0,,{}, 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2}", +seker2021,large_solution_5.json,large_instance_5.json,True,solver_optimal,10.0,,"{""status"": 2}", +seker2021,tiny_solution.json,tiny_instance.json,True,solver_optimal,1.0,,"{""status"": 2}", +song2021,large_solution_1.json,large_instance_1.json,True,solver_optimal,230.23968253939233,0.0,"{""status"": 2, ""status_description"": ""Optimal""}", +song2021,large_solution_2.json,large_instance_2.json,True,solver_optimal,747.5833333333333,0.0,"{""status"": 2, ""status_description"": ""Optimal""}", +song2021,large_solution_3.json,large_instance_3.json,True,solver_optimal,101.51706349206489,0.0,"{""status"": 2, ""status_description"": ""Optimal""}", +song2021,large_solution_4.json,large_instance_4.json,True,explicit_incumbent,1155.6684871065836,0.002461842404401887,"{""status"": 9, ""status_description"": ""Time limit reached""}", +song2021,large_solution_5.json,large_instance_5.json,True,solver_optimal,340.39047619047676,0.0,"{""status"": 2, ""status_description"": ""Optimal""}", 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+tran2018,large_solution_1.json,large_instance_1.json,True,solver_optimal_with_tolerance,69887241.71000001,9.460762007823026e-05,"{""status"": ""optimal""}", +tran2018,large_solution_2.json,large_instance_2.json,True,solver_optimal,50145998.71,1.485777686872163e-16,"{""status"": ""optimal""}", +tran2018,large_solution_3.json,large_instance_3.json,True,solver_optimal,55853874.91,0.0,"{""status"": ""optimal""}", +tran2018,large_solution_4.json,large_instance_4.json,True,solver_optimal,71978589.78999999,0.0,"{""status"": ""optimal""}", +tran2018,large_solution_5.json,large_instance_5.json,True,solver_optimal,91706792.35999998,1.6248699589614192e-16,"{""status"": ""optimal""}", +tran2018,tiny_solution.json,tiny_instance.json,True,solver_optimal,6497155.84,0.0,"{""status"": ""optimal""}", +vidal2011,large_solution_1.json,large_instance_1.json,True,objective_without_certificate,10454.184524865155,,{}, 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+watermeyer2020,tiny_solution.json,tiny_instance.json,True,solver_optimal,34.0,,"{""status"": ""OPTIMAL""}", +watermeyer2022,large_solution_1.json,large_instance_1.json,True,solver_optimal,1654.0,,"{""status"": ""optimal""}", +watermeyer2022,large_solution_2.json,large_instance_2.json,True,solver_optimal,1655.0,,"{""status"": ""optimal""}", +watermeyer2022,large_solution_3.json,large_instance_3.json,True,solver_optimal,1669.0,,"{""status"": ""optimal""}", +watermeyer2022,large_solution_4.json,large_instance_4.json,True,solver_optimal,1707.0,,"{""status"": ""optimal""}", +watermeyer2022,large_solution_5.json,large_instance_5.json,True,solver_optimal,1690.0,,"{""status"": ""optimal""}", +watermeyer2022,tiny_solution.json,tiny_instance.json,True,solver_optimal,64.0,,"{""status"": ""optimal""}", +watermeyer2023,large_solution_1.json,large_instance_1.json,True,solver_optimal,183.0,0.0,"{""status"": ""optimal""}", +watermeyer2023,large_solution_2.json,large_instance_2.json,True,solver_optimal,205.0,0.0,"{""status"": ""optimal""}", +watermeyer2023,large_solution_3.json,large_instance_3.json,True,solver_optimal,138.0,0.0,"{""status"": ""optimal""}", +watermeyer2023,large_solution_4.json,large_instance_4.json,True,solver_optimal,109.0,0.0,"{""status"": ""optimal""}", +watermeyer2023,large_solution_5.json,large_instance_5.json,True,solver_optimal,122.0,0.0,"{""status"": ""optimal""}", +watermeyer2023,tiny_solution.json,tiny_instance.json,True,solver_optimal,17.0,0.0,"{""status"": ""optimal""}", +wei2020,large_solution_1.json,large_instance_1.json,True,explicit_incumbent,668.0,,"{""status"": ""time_limit""}", +wei2020,large_solution_2.json,large_instance_2.json,True,explicit_incumbent,410.0,,"{""status"": ""time_limit""}", +wei2020,large_solution_3.json,large_instance_3.json,True,explicit_incumbent,1003.0,,"{""status"": ""time_limit""}", +wei2020,large_solution_4.json,large_instance_4.json,True,explicit_incumbent,339.0,,"{""status"": ""time_limit""}", +wei2020,large_solution_5.json,large_instance_5.json,True,explicit_incumbent,502.0,,"{""status"": ""time_limit""}", +wei2020,tiny_solution.json,tiny_instance.json,True,solver_optimal,29.0,,"{""status"": ""optimal""}", +wei2020b,large_solution_1.json,large_instance_1.json,True,explicit_incumbent,19.0,,"{""status"": ""time_limit""}", +wei2020b,large_solution_2.json,large_instance_2.json,True,solver_optimal,20.0,,"{""status"": ""optimal""}", +wei2020b,large_solution_3.json,large_instance_3.json,True,solver_optimal,5.0,,"{""status"": ""optimal""}", +wei2020b,large_solution_4.json,large_instance_4.json,True,solver_optimal,36.0,,"{""status"": ""optimal""}", +wei2020b,large_solution_5.json,large_instance_5.json,True,explicit_incumbent,32.0,,"{""status"": ""time_limit""}", +wei2020b,tiny_solution.json,tiny_instance.json,True,solver_optimal,2.0,,"{""status"": ""optimal""}", +wu2018,large_solution_1.json,large_instance_1.json,True,explicit_incumbent,258115355.22212255,0.19435819380917366,"{""status"": 9}", +wu2018,large_solution_2.json,large_instance_2.json,True,explicit_incumbent,11220098.865032729,0.047151206661257404,"{""status"": 9}", +wu2018,large_solution_3.json,large_instance_3.json,True,explicit_incumbent,25279406.877143905,0.06040145888799066,"{""status"": 9}", +wu2018,large_solution_4.json,large_instance_4.json,True,explicit_incumbent,89032475.70413849,0.06675051966353432,"{""status"": 9}", +wu2018,large_solution_5.json,large_instance_5.json,True,explicit_incumbent,200995501.7320755,0.08224519653350305,"{""status"": 9}", +wu2018,tiny_solution.json,tiny_instance.json,True,solver_optimal,3363.0723408058716,,"{""status"": 2}", +wu2022,large_solution_1.json,large_instance_1.json,True,solver_optimal,3769703.5,0.0,"{""status"": 2}", +wu2022,large_solution_2.json,large_instance_2.json,True,explicit_incumbent,4049836.3333333335,0.00013165429569522404,"{""status"": 9}", +wu2022,large_solution_3.json,large_instance_3.json,True,solver_optimal,7302538.0,0.0,"{""status"": 2}", +wu2022,large_solution_4.json,large_instance_4.json,True,solver_optimal,4279533.0,0.0,"{""status"": 2}", +wu2022,large_solution_5.json,large_instance_5.json,True,solver_optimal,7165773.0,0.0,"{""status"": 2}", +wu2022,tiny_solution.json,tiny_instance.json,True,solver_optimal,15938.0,0.0,"{""status"": 2}", +wu2023,large_solution_1.json,large_instance_1.json,True,objective_without_certificate,0.4894080647541645,,{}, +wu2023,large_solution_2.json,large_instance_2.json,True,objective_without_certificate,0.45155278464200455,,{}, +wu2023,large_solution_3.json,large_instance_3.json,True,objective_without_certificate,0.27464280855572054,,{}, +wu2023,large_solution_4.json,large_instance_4.json,True,objective_without_certificate,0.8025180831239948,,{}, +wu2023,large_solution_5.json,large_instance_5.json,True,objective_without_certificate,0.7955172200383855,,{}, +wu2023,tiny_solution.json,tiny_instance.json,True,objective_without_certificate,1.0000000000000058,,{}, +xie2019,large_solution_1.json,large_instance_1.json,True,solver_optimal,31000.0,,"{""status"": 2, ""status_str"": ""OPTIMAL""}", +xie2019,large_solution_2.json,large_instance_2.json,True,solver_optimal,31000.0,,"{""status"": 2, ""status_str"": ""OPTIMAL""}", +xie2019,large_solution_3.json,large_instance_3.json,True,solver_optimal,31000.0,,"{""status"": 2, ""status_str"": ""OPTIMAL""}", +xie2019,large_solution_4.json,large_instance_4.json,True,solver_optimal,31000.0,,"{""status"": 2, ""status_str"": ""OPTIMAL""}", +xie2019,large_solution_5.json,large_instance_5.json,True,solver_optimal,31000.0,,"{""status"": 2, ""status_str"": ""OPTIMAL""}", +xie2019,tiny_solution.json,tiny_instance.json,True,solver_optimal,1000.0,,"{""status"": 2, ""status_str"": ""OPTIMAL""}", +xu2024,large_solution_1.json,large_instance_1.json,True,objective_without_certificate,6.904188041509924e-05,,{}, +xu2024,large_solution_2.json,large_instance_2.json,True,objective_without_certificate,2.8534478920633733e-05,,{}, +xu2024,large_solution_3.json,large_instance_3.json,True,objective_without_certificate,5.705001488687638e-05,,{}, +xu2024,large_solution_4.json,large_instance_4.json,True,objective_without_certificate,0.0003833769852871129,,{}, +xu2024,large_solution_5.json,large_instance_5.json,True,objective_without_certificate,2.2936845083378098e-05,,{}, +xu2024,tiny_solution.json,tiny_instance.json,True,objective_without_certificate,0.00021015479853834075,,{}, +yang2013,large_solution_1.json,large_instance_1.json,True,explicit_incumbent,38991017.0,0.077580980781649,"{""gurobi_status"": 9, ""status"": ""feasible""}", +yang2013,large_solution_2.json,large_instance_2.json,True,explicit_incumbent,1978139.0,0.923489290819233,"{""gurobi_status"": 9, ""status"": ""feasible""}", +yang2013,large_solution_3.json,large_instance_3.json,True,explicit_incumbent,12569179.0,0.4691542743061523,"{""gurobi_status"": 9, ""status"": ""feasible""}", +yang2013,large_solution_4.json,large_instance_4.json,True,explicit_incumbent,36778414.0,0.24224622672729124,"{""gurobi_status"": 9, ""status"": ""feasible""}", +yang2013,large_solution_5.json,large_instance_5.json,True,explicit_incumbent,171597.0,2.4691793735542267,"{""gurobi_status"": 9, ""status"": ""feasible""}", +yang2013,tiny_solution.json,tiny_instance.json,True,solver_optimal_with_tolerance,49686.0,8.050557501106951e-05,"{""gurobi_status"": 2, ""status"": ""optimal""}", +zetina2019,large_solution_1.json,large_instance_1.json,True,explicit_incumbent,639036.0,1.0,"{""status"": 9, ""status_name"": ""TIME_LIMIT""}", +zetina2019,large_solution_2.json,large_instance_2.json,True,explicit_incumbent,650284.0,1.0,"{""status"": 9, ""status_name"": ""TIME_LIMIT""}", +zetina2019,large_solution_3.json,large_instance_3.json,True,explicit_incumbent,599984.0,1.0,"{""status"": 9, ""status_name"": ""TIME_LIMIT""}", +zetina2019,large_solution_4.json,large_instance_4.json,True,explicit_incumbent,525296.0,1.0,"{""status"": 9, ""status_name"": ""TIME_LIMIT""}", +zetina2019,large_solution_5.json,large_instance_5.json,True,explicit_incumbent,533781.0,1.0,"{""status"": 9, ""status_name"": ""TIME_LIMIT""}", +zetina2019,tiny_solution.json,tiny_instance.json,True,solver_optimal,10477.0,0.0,"{""status"": 2, ""status_name"": ""OPTIMAL""}", +zetina2020,large_solution_1.json,large_instance_1.json,True,explicit_incumbent,2403.0,,"{""status"": 9, ""status_description"": ""TIME_LIMIT""}", +zetina2020,large_solution_2.json,large_instance_2.json,True,explicit_incumbent,2410.0,,"{""status"": 9, ""status_description"": ""TIME_LIMIT""}", +zetina2020,large_solution_3.json,large_instance_3.json,True,explicit_incumbent,2400.0,,"{""status"": 9, ""status_description"": ""TIME_LIMIT""}", +zetina2020,large_solution_4.json,large_instance_4.json,True,explicit_incumbent,2403.0,,"{""status"": 9, ""status_description"": ""TIME_LIMIT""}", +zetina2020,large_solution_5.json,large_instance_5.json,True,explicit_incumbent,2345.0,,"{""status"": 9, ""status_description"": ""TIME_LIMIT""}", +zetina2020,tiny_solution.json,tiny_instance.json,True,solver_optimal,2.5749612824,,"{""status"": 2, ""status_description"": ""OPTIMAL""}", +zhang2025,large_solution_1.json,large_instance_1.json,True,explicit_incumbent,5.6,,"{""status"": ""feasible""}", +zhang2025,large_solution_2.json,large_instance_2.json,True,explicit_incumbent,47.0,,"{""status"": ""feasible""}", +zhang2025,large_solution_3.json,large_instance_3.json,True,solver_optimal,218.84,,"{""status"": ""optimal""}", +zhang2025,large_solution_4.json,large_instance_4.json,True,explicit_incumbent,44.3289,,"{""status"": ""feasible""}", +zhang2025,large_solution_5.json,large_instance_5.json,True,solver_optimal,9.724699999999999,,"{""status"": ""optimal""}", +zhang2025,tiny_solution.json,tiny_instance.json,True,solver_optimal,8.0,,"{""status"": ""optimal""}", +zohali2022,large_solution_1.json,large_instance_1.json,True,explicit_incumbent,231.0,0.03896103896103896,"{""status"": ""feasible_compact_gurobi_assignment""}", +zohali2022,large_solution_2.json,large_instance_2.json,True,explicit_incumbent,229.0,0.03056768558951965,"{""status"": ""feasible""}", +zohali2022,large_solution_3.json,large_instance_3.json,True,explicit_incumbent,265.0,0.1509433962264151,"{""status"": ""feasible""}", +zohali2022,large_solution_4.json,large_instance_4.json,True,explicit_incumbent,258.0,0.12403100775193798,"{""status"": ""feasible""}", +zohali2022,large_solution_5.json,large_instance_5.json,True,explicit_incumbent,387.0,0.4134366925064599,"{""status"": ""feasible""}", +zohali2022,tiny_solution.json,tiny_instance.json,True,solver_optimal,61.0,0.0,"{""status"": ""optimal""}", diff --git a/audit/final_gates/SOURCE_SNAPSHOT.json b/audit/final_gates/SOURCE_SNAPSHOT.json new file mode 100644 index 0000000000000000000000000000000000000000..f316a1623b20d9f0f66b77e2a12f053f7e12aa87 --- /dev/null +++ b/audit/final_gates/SOURCE_SNAPSHOT.json @@ -0,0 +1,24 @@ +{ + "dataset": "SmartOR/FrontierOR", + "remote": "https://huggingface.co/datasets/SmartOR/FrontierOR", + "commit": "37ccd8b6dca3bf7f4e0c58941a6ed156832a6d9e", + "remote_head": "37ccd8b6dca3bf7f4e0c58941a6ed156832a6d9e", + "head_matches_remote": true, + "case_count": 180, + "required_files": [ + "problem_description.txt", + "mathematical_formulation.md", + "instance_schema.json", + "solution_schema.json", + "gurobi_code.py", + "feasibility_check.py" + ], + "required_file_coverage": { + "problem_description.txt": 180, + "mathematical_formulation.md": 180, + "instance_schema.json": 180, + "solution_schema.json": 180, + "gurobi_code.py": 180, + "feasibility_check.py": 180 + } +} diff --git a/audit/final_gates/STATIC_AUDIT.csv b/audit/final_gates/STATIC_AUDIT.csv new file mode 100644 index 0000000000000000000000000000000000000000..5ee200582fbc29e042474181f90f586d106ac3f5 --- /dev/null +++ b/audit/final_gates/STATIC_AUDIT.csv @@ -0,0 +1,181 @@ +case_id,status,max_severity,description_sense,formulation_sense,gurobi_sense,finding_codes,evidence +adulyasak2015,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"alpha_hat, coordinates, d_bar, epsilon || alpha_hat, coordinates, d_bar, epsilon" +alba2013,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,requests || requests +alfieri2020,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,num_edges || num_edges +amaldi2013,flagged,medium,minimize,,minimize,solution_schema_fields_not_detected_in_checker|instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"point_assignments || upper_bound_K || big_M, upper_bound_K" +araujo2015,static_clear,none,minimize,,minimize,, +araujo2020,static_clear,none,minimize,minimize,minimize,, +archetti2007,static_clear,none,minimize,,minimize,, +armbruster2012,flagged,medium,minimize,minimize,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"num_edges, total_node_weight || num_edges, total_node_weight" +arslan2021,static_clear,none,minimize,,minimize,, +bard2002,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"num_customers, primary_objective, scheduling_horizon, secondary_objective || num_customers, primary_objective, scheduling_horizon, secondary_objective" +barnhart2000,flagged,medium,minimize,,,model_objective_sense_not_detected|instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,no literal Gurobi sense found || objective || objective +bayani2024,flagged,medium,minimize,,,model_objective_sense_not_detected|instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"no literal Gurobi sense found || demands, out_of_star_density, processing_rates || demands, out_of_star_density, processing_rates" +belhoul2014,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"ideal_point, nadir_approximation || ideal_point, nadir_approximation" +belvaux2000,static_clear,none,minimize,,minimize,, +bentert2022,flagged,medium,minimize,minimize,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"c_obligatory_components, edge_weight_rule, graph_type || c_obligatory_components, edge_weight_rule, graph_type, m, n" +bergman2019,static_clear,none,maximize,maximize,maximize,, +bertsimas2022,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"gamma_formula, r || gamma_formula, r" +bertsimas2024,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"edge_lengths, node_positions || commodity_destinations, edge_lengths, node_positions" +bettinelli2017,static_clear,none,maximize,maximize,maximize,, +bierwirth2017,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"due_date_factor, num_operations || due_date_factor, num_operations" +bode2012,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"num_required_edges || num_required_edges, total_demand" +bodur2017,flagged,medium,minimize,,minimize,solution_schema_fields_not_detected_in_checker|instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"open_facilities || capacity_factor, variance_level || capacity_factor, customers, variance_level" +boland2019,flagged,medium,,,minimize,solution_schema_fields_not_detected_in_checker,pareto_front +bollapragada2001,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"expected_optimal_mass, num_discrete_areas, num_linking_groups, num_nodes, structure_type || expected_optimal_mass, num_discrete_areas, num_linking_groups, num_nodes, structure_type" +borndorfer2007,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,od_summary || od_summary +bragin2022,static_clear,none,minimize,,minimize,, +brandao2016,static_clear,none,minimize,minimize,minimize,, +bront2009,flagged,medium,maximize,,maximize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,alpha || alpha +buchheim2018,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"grid_size_k, nodes || grid_size_k, nodes" +byeon2022,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"problem_type, scale, stress_parameters || instance_id, problem_type, scale, stress_parameters" +caprara1999,flagged,medium,maximize,maximize,maximize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,density_percent || density_percent +carosi2019,flagged,medium,minimize,,minimize,solution_schema_fields_not_detected_in_checker|instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"num_vehicles, tt_arcs_used || penalty_function, planning_horizon, topology || penalty_function, planning_horizon, topology" +carvalho1999,flagged,medium,,minimize,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,num_items || num_items +carvalho2022,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"parameters || machine_eligibility, parameters, shortcut_items" +castro2021,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"customer_locations, num_arcs, num_constraints, num_variables, relative_demand_slack, supplier_locations, total_demand, total_supply || cost_type, customer_locations, num_arcs, num_constraints, num_variables, relative_demand_slack, supplier_locations, total_demand, total_supply" +chebil2015,flagged,medium,maximize,,maximize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"n_star, problem || N, n_star, problem" +chebil2019,static_clear,none,maximize,maximize,maximize,, +chen1999,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"congestion_parameter_q, machines, objective || congestion_parameter_q, machines, objective" +cherkesly2015,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"n_arcs, vehicle_fixed_cost || n_arcs, planning_horizon" +colombi2017,flagged,medium,maximize,,maximize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"num_VI_nodes, num_arcs, num_profitable_arcs, num_strong_incompatibilities, num_weak_incompatibilities || num_VI_nodes, num_arcs, num_profitable_arcs, num_strong_incompatibilities, num_weak_incompatibilities" +contreras2011,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,coordinates || coordinates +cordeau2006,static_clear,none,minimize,,minimize,, +cordeau2014,flagged,high,minimize,,minimize,solution_schema_fields_not_detected_in_model_output|instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"objective_value, tour || depot || depot, vertices" +cordeau2019,flagged,medium,maximize|minimize,maximize|minimize,maximize|minimize,multiple_model_objective_senses|instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"maximize, minimize || covering_demand_fraction, customer_coordinates, facility_coordinates, facility_to_customers_coverage, radius_of_coverage, total_demand_D_bar || covering_demand_fraction, customer_coordinates, facility_coordinates, facility_to_customers_coverage, radius_of_coverage, total_demand_D_bar" +cote2018,flagged,medium,,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"num_item_types, total_num_items || num_item_types, total_num_items" +cote2019,flagged,low,minimize,,minimize,instance_top_fields_not_detected_in_checker,depot +cunha2021,static_clear,none,minimize,,minimize,, +damcikurt2016,flagged,high,maximize,,maximize,solution_schema_fields_not_detected_in_model_output,sparse_defaults +dayarian2019,flagged,low,,,minimize,instance_top_fields_not_detected_in_checker,depot +degraeve2007,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,factor_settings || factor_settings +delage2022,flagged,medium,minimize,minimize,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"groups, num_groups || groups, num_groups" +delorme2020,flagged,medium,,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"m, n || m, n" +desaulniers2010,flagged,medium,minimize,minimize,minimize,solution_schema_fields_not_detected_in_checker|instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"deliveries, num_vehicles_used || num_vehicles_upper_bound, scheduling_horizon, service_time_independent_of_quantity, split_deliveries_allowed, travel_time_equals_distance, triangle_inequality || num_vehicles_upper_bound, scheduling_horizon, service_time_independent_of_quantity, split_deliveries_allowed, travel_time_equals_distance, triangle_inequality" +desaulniers2014,flagged,medium,minimize,,minimize,solution_schema_fields_not_detected_in_checker,solution_details +desaulniers2016,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"coordinates, num_nodes || coordinates, num_nodes" +dienstknecht2024,static_clear,none,minimize,,minimize,, +earl2005,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,v_a || v_a +eiselt2009,flagged,medium,maximize,,maximize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,r || r +elci2022,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,mean_processing_times || mean_processing_times +engineer2012,flagged,medium,minimize,,,model_objective_sense_not_detected|instance_top_fields_not_detected_in_checker,"no literal Gurobi sense found || num_demand_ports, num_supply_ports, num_vessels" +fischetti1997,flagged,low,minimize,,minimize,instance_top_fields_not_detected_in_checker,nodes +fischetti1998,static_clear,none,maximize,,maximize,, +fischetti2017,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"customer_locations_x, customer_locations_y, facility_locations_x, facility_locations_y || customer_locations_x, customer_locations_y, facility_locations_x, facility_locations_y" +fleszar2022b,static_clear,none,maximize,,maximize,, +forrest2006,flagged,medium,maximize|minimize,,maximize,solution_schema_fields_not_detected_in_checker|instance_top_fields_not_detected_in_checker,slabs_used || num_colors +fragkos2016,static_clear,none,minimize,,minimize,, +fragkos2021,flagged,medium,minimize,minimize,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,num_nodes || num_nodes +freeman2018,flagged,medium,maximize,maximize,maximize,solution_schema_fields_not_detected_in_checker,event_sites +freling2003,flagged,medium,minimize,minimize,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"customer_locations, facility_locations || customer_locations, facility_locations" +frey2017,flagged,medium,minimize,minimize,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"baggage_transfer_time_periods, carousel_config, flight_end_offset_periods, quantile_used || baggage_transfer_time_periods, carousel_config, flight_end_offset_periods, num_carousels, num_flights, quantile_used" +furini2016,flagged,medium,maximize,,maximize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"n, n_tilde, rho || n, n_tilde, rho" +furini2017,flagged,medium,minimize,,maximize|minimize,multiple_model_objective_senses|solution_schema_fields_not_detected_in_checker,"maximize, minimize || color_classes" +furini2019,flagged,critical,maximize,minimize,maximize,formulation_model_objective_mismatch|instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"formulation=minimize, model=maximize || adjacency_list, num_edges || adjacency_list, num_edges" +furini2021,flagged,critical,minimize,maximize,minimize,formulation_model_objective_mismatch|instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"formulation=maximize, model=minimize || num_edges, num_vertices || num_edges, num_vertices" +gamache1999,flagged,medium,maximize|minimize,,minimize,solution_schema_fields_not_detected_in_checker|instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"uncovered_pairings || num_aircraft_types, num_crew_members, num_daughter_pairings, num_mother_pairings, objective || num_aircraft_types, num_crew_members, num_daughter_pairings, num_mother_pairings, objective, planning_horizon_days, work_rules" +gangammanavar2020,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"first_stage, retailer_locations || first_stage, retailer_locations" +garcia2011,flagged,medium,,minimize,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,cost_matrix || cost_matrix +gruson2021,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"initial_inventory, initial_setups_imposed || initial_inventory" +gschwind2021,flagged,medium,maximize|minimize,minimize,maximize|minimize,multiple_model_objective_senses,"maximize, minimize" +gualandi2012,static_clear,none,minimize,,minimize,, +guo2021,flagged,medium,minimize,minimize,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"global_parameters, num_mandatory_patients || global_parameters, num_mandatory_patients" +hadjar2006,flagged,medium,minimize,,,model_objective_sense_not_detected|instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"no literal Gurobi sense found || depot_penalty, num_arcs, time_horizon || depot_penalty, num_arcs, num_compatible_pairs, num_depots, time_horizon" +hassannayebi2016,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,feasibility_lower_bound_AWT_minutes || feasibility_lower_bound_AWT_minutes +he2020,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"planning_horizon || inconvenience_cost_function, planning_horizon" +hewitt2024,flagged,medium,maximize,,maximize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"num_facilities, num_production_levels, num_products, num_scenarios_per_distribution, num_total_distributions_per_product, num_yield_distributions_per_product || num_facilities, num_production_levels, num_products, num_scenarios_per_distribution, num_total_distributions_per_product, num_yield_distributions_per_product" +hoffman1993,static_clear,none,minimize,,minimize,, +hosseinian2020,flagged,medium,,,maximize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"num_edges || num_edges, num_vertices" +huisman2005,flagged,medium,minimize,,,model_objective_sense_not_detected|instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"no literal Gurobi sense found || coordinate_area_km, deadhead_speed_km_per_hour, distances_km, frequency_table_minutes, lines, locations, num_depots, num_lines, num_trips, operational_speeds_km_per_hour, speed_type, trips_per_line_per_direction || coordinate_area_km, crew_parameters, deadhead_speed_km_per_hour, distances_km, duty_types, frequency_table_minutes, lines, locations, num_depots, num_lines, num_trips, operational_speeds_km_per_hour, relief_locations, speed_type, trips_per_line_per_direction, vehicle_parameters" +ji2019,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,num_chambers || num_chambers +juvin2023,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"migration_allowed, preemption_allowed || migration_allowed, preemption_allowed" +kang2021,flagged,high,minimize,,minimize,solution_schema_fields_not_detected_in_model_output|solution_schema_fields_not_detected_in_checker|instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"drone_assignments[*].delivery_node, drone_assignments[*].dispatch_node, drone_assignments[*].drone || drone_assignments[*].delivery_node, drone_assignments[*].dispatch_node, drone_assignments[*].drone || node_positions, num_customers || node_positions, num_customers" +klose2005,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"total_capacity, total_demand || capacity_tightness_ratio, total_capacity, total_demand" +knueven2020,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"num_generators, num_time_periods, time_period_hours || num_generators, num_time_periods, time_period_hours" +kobayashi2021,static_clear,none,minimize,minimize,minimize,, +kobeaga2024,flagged,medium,maximize,maximize,maximize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,coordinates || coordinates +kowalczyk2018,flagged,medium,minimize,minimize,minimize,solution_schema_fields_not_detected_in_checker,machines +kowalczyk2024,flagged,medium,minimize,,,model_objective_sense_not_detected,no literal Gurobi sense found +kress2019,flagged,high,minimize,,minimize,solution_schema_fields_not_detected_in_model_output,"crane_l_positions, crane_w_positions, landside_events, objective_value, seaside_events" +kreter2018,flagged,medium,minimize,minimize,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"num_real_activities, num_total_activities || num_real_activities, num_total_activities" +lai2021,flagged,medium,maximize,maximize,maximize,solution_schema_fields_not_detected_in_checker|instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"groups || distance_format, distance_range, distance_type, group_capacities[*], num_distance_entries || distance_format, distance_range, distance_type, group_capacities[*], num_distance_entries" +laporte2003,flagged,medium,minimize,,minimize,solution_schema_fields_not_detected_in_checker|instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"tour || coordinates, tau || coordinates, tau" +lee1996,flagged,high,minimize,,minimize,solution_schema_fields_not_detected_in_model_output|instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"active_steiner_nodes, assignments, objective_value, steiner_tree_edges || parameters || parameters" +legrain2020,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"num_shifts, num_skills || num_shifts, num_skills" +legrain2024,flagged,high,minimize,,minimize,solution_schema_fields_not_detected_in_model_output|instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,skill_assignments || weekend_definition || weekend_definition +letelier2022,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"num_real_items, num_total_items, requests || num_real_items, num_total_items, requests" +levin2017,static_clear,none,minimize,minimize,minimize,, +li2020,static_clear,none,maximize|minimize,,maximize,, +liao2020,flagged,medium,minimize,,minimize,solution_schema_fields_not_detected_in_checker|instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"c_g, c_o, c_w || num_colonoscopy_C, num_upper_endoscopy_colonoscopy_UC, procedure_types || num_colonoscopy_C, num_upper_endoscopy_colonoscopy_UC, patients, procedure_types" +lozano2017,flagged,medium,maximize,,maximize|minimize,multiple_model_objective_senses|instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"maximize, minimize || all_leader_vars_integer, n, num_integer_follower_vars || all_leader_vars_integer, n, num_integer_follower_vars" +luo2017,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,scheduling_horizon || scheduling_horizon +mansini2020,static_clear,none,maximize,,maximize,, +martello1999,static_clear,none,maximize,,maximize,, +martin-iradi2024,flagged,low,minimize,,minimize,instance_top_fields_not_detected_in_checker,planning_horizon_hours +mehrotra1996,flagged,medium,minimize,,maximize|minimize,multiple_model_objective_senses|solution_schema_fields_not_detected_in_checker,"maximize, minimize || color_classes" +mingozzi1999,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"constraints, min_vehicles_backhaul, min_vehicles_linehaul, num_total_customers || constraints, min_vehicles_backhaul, min_vehicles_linehaul, num_total_customers" +morrison2016,flagged,medium,minimize,minimize,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,bounds || bounds +muter2010,flagged,low,minimize,,minimize,instance_top_fields_not_detected_in_checker,"num_customers, num_vehicles" +nagy2015,flagged,medium,minimize,minimize,minimize,solution_schema_fields_not_detected_in_checker|instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,routes_detailed || depot || depot +oliveira2020,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"p_max, total_processing_time || p_max, total_processing_time" +ostrowski2012,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,v_a || v_a +ozturk2020,static_clear,none,minimize,minimize,minimize,, +pecin2017,flagged,high,,minimize,minimize,solution_schema_fields_not_detected_in_model_output,"objective_value, routes" +pedersen2024,static_clear,none,minimize,minimize,minimize,, +pessoa2021,flagged,medium,minimize,,,model_objective_sense_not_detected|instance_top_fields_not_detected_in_checker,no literal Gurobi sense found || depot +pettersson2020,static_clear,none,minimize,maximize|minimize,minimize,, +pinnoi1997,flagged,medium,maximize|minimize,minimize,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"num_precedence_arcs, num_tasks, station_lower_bound, total_processing_time || num_precedence_arcs, num_tasks, station_lower_bound, total_processing_time" +potthoff2010,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"active_duties, disruption, network, parameters, released_problem_variant, reserve_duties, tasks, train_lines || active_duties, disruption, network, parameters, reserve_duties, tasks, train_lines" +rahimian2017,flagged,medium,minimize,,minimize,solution_schema_fields_not_detected_in_checker|instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,solution || num_weeks || num_weeks +rahmaniani2017,flagged,medium,minimize,minimize,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"num_commodities, num_nodes || num_commodities, num_nodes" +rahmaniani2018,static_clear,none,minimize,,minimize,, +rahmaniani2020,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"customers, max_total_demand, total_capacity || customers, max_total_demand, total_capacity" +rahmaniani2022,flagged,high,minimize,,minimize,solution_schema_fields_not_detected_in_model_output|instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"investments, objective_value, storage_energy_investments, transmission_investments || consumer_segments[*], generators[*], transmission_lines[*] || availability, consumer_segments[*], generators[*], hydro_inflow, transmission_lines[*]" +reihaneh2019,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"delivery_sites, depot || delivery_sites, depot" +reinhardt2016,flagged,high,maximize|minimize,,minimize,solution_schema_fields_not_detected_in_model_output|instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"edge_sets_used, objective_value, routes || num_edge_sets || free_edges, num_edge_sets" +ribeiro1994,flagged,medium,minimize,minimize,,model_objective_sense_not_detected|instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"no literal Gurobi sense found || summary || depots, relief_points, summary" +riley2025,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"demand_sites, production_sites, scenario_probabilities || demand_sites, module_sizes, num_production_sites, num_scenarios, production_costs, production_sites, relocation_costs, scenario_probabilities, u_max" +roberti2015,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"B, delta, total_supply || B, delta, total_supply" +roberti2018,flagged,medium,minimize,,minimize,solution_schema_fields_not_detected_in_checker|instance_top_fields_not_detected_in_checker,"group_assignments || num_customers, num_groups, num_satellites" +roberti2021,flagged,medium,,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"alpha, nodes, num_nodes || alpha, nodes, num_nodes" +ropke2009,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"depot, fixed_cost_per_vehicle, num_nodes, objective, planning_horizon, requests || depot, fixed_cost_per_vehicle, num_nodes, objective, planning_horizon, requests" +roshanaei2017,flagged,medium,minimize,minimize,minimize,solution_schema_fields_not_detected_in_checker|instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"or_sequences, surgeon_sequences || ORs_per_hospital || ORs_per_hospital, num_hospitals, num_patients, num_surgeons" +rostami2021,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"base_demands, node_coordinates || base_demands, node_coordinates" +rothenbacher2018,static_clear,none,minimize,,minimize,, +rothenbacher2019,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"cost_type, distance_matrix_type, planning_horizon_cyclic || cost_type, distance_matrix_type, num_customers, planning_horizon_cyclic" +ruiz2007,flagged,medium,minimize,minimize,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,no_idle_machines || no_idle_machines +sadykov2013,static_clear,none,minimize,,minimize,, +salemi2022,static_clear,none,minimize,,minimize,, +samarghandi2017,static_clear,none,minimize,minimize,minimize,, +sansegundo2016,static_clear,none,maximize,,maximize,, +sansegundo2023,flagged,medium,maximize,,maximize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"actual_density, greedy_clique, greedy_clique_size, num_edges, target_density || actual_density, greedy_clique, greedy_clique_size, num_edges, target_density" +savelsbergh1997,static_clear,none,maximize,maximize,maximize,, +schwerdfeger2016,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model,num_jobs +segundo2019,static_clear,none,maximize,maximize,maximize,, +segundo2019b,static_clear,none,maximize,maximize,maximize,, +seker2021,flagged,critical,minimize,maximize,minimize,formulation_model_objective_mismatch,"formulation=maximize, model=minimize" +song2021,flagged,low,minimize,,minimize,instance_top_fields_not_detected_in_checker,num_orders_n +spliet2015,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,num_vehicles || num_vehicles +taninmis2022,flagged,high,minimize,,minimize,solution_schema_fields_not_detected_in_model_output,"investments, objective_value" +tran2018,flagged,medium,maximize,,maximize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,od_nodes || od_nodes +vidal2011,flagged,medium,minimize,minimize,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,problem_type || problem_type +walteros2020,flagged,medium,maximize,maximize,maximize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"clique_core_gap, degeneracy, m, max_degree, omega || clique_core_gap, degeneracy, m, max_degree, omega" +wang2019,flagged,medium,minimize,,,model_objective_sense_not_detected|instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"no literal Gurobi sense found || num_arcs, num_commodities, num_fleet_types, num_nodes || num_arcs, num_commodities, num_fleet_types, num_nodes" +wang2025,flagged,medium,maximize,maximize,maximize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"element_weights, m, type_specific_param || element_weights, m, type_specific_param" +wangk2020,static_clear,none,minimize,,minimize,, +wangy2020,static_clear,none,minimize,minimize,minimize,, +watermeyer2020,flagged,medium,minimize,minimize,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"d_bar, num_resources || d_bar, num_resources" +watermeyer2022,flagged,medium,minimize,minimize,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"num_real_activities, num_resources || max_project_duration, num_activities_total, num_resources" +watermeyer2023,flagged,medium,,minimize,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"num_partially_renewable_resources, num_real_activities, num_temporal_constraints || num_partially_renewable_resources, num_real_activities, num_temporal_constraints" +wei2020,static_clear,none,,minimize,minimize,, +wei2020b,static_clear,none,minimize,,minimize,, +wu2018,static_clear,none,maximize|minimize,,minimize,, +wu2022,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"description, plant_opening_cost_per_period, problem || description, plant_opening_cost_per_period, problem" +wu2023,flagged,high,maximize|minimize,,maximize,solution_schema_fields_not_detected_in_model_output|solution_schema_fields_not_detected_in_checker|instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"one_stage_IP_mu.*, two_stage_IP.* || one_stage_IP_mu.*, two_stage_IP.* || first_stage_costs, num_scenarios_N, problem_dimension_n, selection_problem, vertex_cover_problem || first_stage_costs, num_scenarios_N, problem_dimension_n, selection_problem, vertex_cover_problem" +xie2019,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"time_horizon_hours, total_time_steps || num_berths, time_horizon_hours, total_time_steps" +xu2024,static_clear,none,minimize,,minimize,, +yang2013,static_clear,none,maximize,,maximize,, +zetina2019,flagged,medium,minimize,,minimize,instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,num_arcs || num_arcs +zetina2020,flagged,medium,maximize|minimize,,maximize|minimize,multiple_model_objective_senses,"maximize, minimize" +zhang2025,flagged,high,minimize,,minimize,solution_schema_fields_not_detected_in_model_output|instance_top_fields_not_detected_in_model|instance_top_fields_not_detected_in_checker,"objective_value, selected_duties || trips || trips" +zohali2022,static_clear,none,minimize,,minimize,, diff --git a/audit/generate_complete_witnesses.py b/audit/generate_complete_witnesses.py new file mode 100644 index 0000000000000000000000000000000000000000..8a3fd9ce7ddfa598bda68f07240fa73439a951c8 --- /dev/null +++ b/audit/generate_complete_witnesses.py @@ -0,0 +1,284 @@ +#!/usr/bin/env python3 +"""Generate complete witnesses and trusted snapshots for audited cases.""" + +from __future__ import annotations + +import argparse +import importlib.util +import json +from pathlib import Path +import tempfile + +import gurobipy as gp +from gurobipy import GRB + +ROOT = Path(__file__).with_name("current_main_code") + + +def import_file(name, path): + spec = importlib.util.spec_from_file_location(name, path) + module = importlib.util.module_from_spec(spec) + assert spec.loader is not None + spec.loader.exec_module(module) + return module + + +assets = import_file("build_solverless_assets", ROOT / "build_solverless_assets.py") + + +def load_module(case): + return import_file(f"offline_{case}", ROOT / case / "gurobi_code.py") + + +class CaptureModels: + def __init__(self): + self.models = [] + self.original = gp.Model + + def __enter__(self): + def factory(*args, **kwargs): + model = self.original(*args, **kwargs) + self.models.append(model) + return model + gp.Model = factory + return self.models + + def __exit__(self, *_): + gp.Model = self.original + + +def pairs(case): + base = ROOT / case + for instance_path in sorted((base / "instance").glob("*.json")): + solution_name = ( + "tiny_solution.json" if instance_path.name == "tiny_instance.json" + else instance_path.name.replace("instance", "solution") + ) + yield instance_path, base / "gurobi_solution" / solution_name + + +def read_json(path): + with open(path) as handle: + return json.load(handle) + + +def snapshot_path(case, instance_path): + digest = assets.instance_digest(instance_path) + return ROOT / case / "solverless_models" / f"{digest}.npz" + + +def quiet_exact(model, time_limit=3600): + model.Params.OutputFlag = 0 + model.Params.Threads = 4 + model.Params.TimeLimit = time_limit + model.Params.MIPGap = 0 + model.Params.FeasibilityTol = 1e-7 + model.Params.OptimalityTol = 1e-8 + model.optimize() + if model.Status != GRB.OPTIMAL or model.SolCount < 1: + raise RuntimeError(f"fixed witness failed: status={model.Status}, solutions={model.SolCount}") + + +def generate_adulyasak2015(): + module = load_module("adulyasak2015") + for instance_path, solution_path in pairs("adulyasak2015"): + solution = read_json(solution_path) + with tempfile.NamedTemporaryFile(suffix=".json") as temporary: + with CaptureModels() as models: + module.solve(str(instance_path), temporary.name, 0) + model = models[-1] + assets.export_model(model, instance_path, snapshot_path("adulyasak2015", instance_path)) + for variable in model.getVars(): + name = variable.VarName + if name.startswith("y_"): + value = solution["y"].get(name[2:], 0) + elif name.startswith("z_"): + value = solution["z"].get(name[2:], 0) + elif name.startswith("x_"): + value = solution["x"].get(name[2:], 0) + else: + continue + variable.LB = variable.UB = float(value) + quiet_exact(model) + assets.update_solution(solution_path, model) + print("done", instance_path, flush=True) + + +def generate_bertsimas2024(): + module = load_module("bertsimas2024") + for instance_path, solution_path in pairs("bertsimas2024"): + data, solution = read_json(instance_path), read_json(solution_path) + with CaptureModels() as models: + module.build_and_solve(data, 0) + model = models[-1] + assets.export_model(model, instance_path, snapshot_path("bertsimas2024", instance_path)) + for edge, value in solution["z"].items(): + variable = model.getVarByName(f"z[{edge}]") + variable.LB = variable.UB = float(value) + if float(value) < 0.5: + for scenario in range(int(data["num_scenarios"])): + total = model.getVarByName(f"tf[{scenario},{edge}]") + total.UB = 0.0 + for commodity in range(int(data["num_commodities"])): + flow = model.getVarByName(f"x[{commodity},{scenario},{edge}]") + flow.UB = 0.0 + quiet_exact(model) + assets.update_solution(solution_path, model) + print("done", instance_path, flush=True) + + +def generate_cote2018(): + module = load_module("cote2018") + for instance_path, solution_path in pairs("cote2018"): + width, items = module.load_instance(str(instance_path)) + items.sort(key=lambda item: -item[0]) + widths, demands = [x[0] for x in items], [x[1] for x in items] + patterns = module.compute_normal_patterns_csp(widths, demands, width) + model, z, _ = module.build_arc_flow_model(width, widths, demands, patterns, 3600) + assets.export_model(model, instance_path, snapshot_path("cote2018", instance_path)) + z.LB = z.UB = round(float(read_json(solution_path)["objective_value"])) + quiet_exact(model) + assets.update_solution(solution_path, model) + print("done", instance_path, flush=True) + + +def generate_liao2020(): + module = load_module("liao2020") + for instance_path, solution_path in pairs("liao2020"): + solution = read_json(solution_path) + with CaptureModels() as models: + module.solve_drocs(str(instance_path), None, 0, quiet=True) + model = models[-1] + assets.export_model(model, instance_path, snapshot_path("liao2020", instance_path)) + patients = int(read_json(instance_path)["num_patients_P"]) + for patient in range(1, patients + 1): + assigned = int(solution["assignment"][str(patient)]) + for position in range(1, patients + 1): + variable = model.getVarByName(f"x[{patient},{position}]") + variable.LB = variable.UB = int(position == assigned) + for position in range(1, patients + 1): + variable = model.getVarByName(f"t[{position}]") + value = float(solution["schedule"][str(position)]) + variable.LB = variable.UB = value + quiet_exact(model) + assets.update_solution(solution_path, model) + print("done", instance_path, flush=True) + + +def generate_taninmis2022(): + module = load_module("taninmis2022") + instance_path = ROOT / "taninmis2022/instance/tiny_instance.json" + solution_path = ROOT / "taninmis2022/gurobi_solution/tiny_solution.json" + instance, old = read_json(instance_path), read_json(solution_path) + model = module.build_model(instance, time_limit=3600, quiet=True) + system = instance["system"] + families = { + "generators": ("x_gen", [x for x in system["generators"] if not x["existing"]]), + "lines": ("x_line", [x for x in system["transmission_lines"] if not x["existing"]]), + "storage": ("x_stor", [x for x in system["storage"] if not x["existing"]]), + } + for node in instance["scenario_tree"]: + node_id = node["id"] + node_solution = old["investments"][str(node_id)] + for family, (prefix, entries) in families.items(): + for entry in entries: + variable = model.getVarByName(f"{prefix}_{node_id}_{entry['id']}") + value = node_solution[family][str(entry["id"])] + variable.LB = variable.UB = value + quiet_exact(model) + complete = module.extract_solution(model, instance) + with open(solution_path, "w") as handle: + json.dump(complete, handle, indent=2, allow_nan=False) + handle.write("\n") + print("done", instance_path, flush=True) + + +def generate_rahmaniani2018(): + module = load_module("rahmaniani2018") + for instance_path, solution_path in pairs("rahmaniani2018"): + instance, solution = read_json(instance_path), read_json(solution_path) + with CaptureModels() as models: + module.build_and_solve(instance, 0, quiet=True) + template = models[-1] + assets.export_model(template, instance_path, snapshot_path("rahmaniani2018", instance_path)) + with CaptureModels() as models: + module.build_and_solve(instance, 3600, fixed_solution=solution, quiet=True) + fixed = models[-1] + if fixed.Status != GRB.OPTIMAL or fixed.SolCount < 1: + raise RuntimeError(f"rahmaniani2018 fixed solve failed: {fixed.Status}") + assets.update_solution(solution_path, fixed) + print("done", instance_path, flush=True) + + +def fix_rahmaniani2022(data, solution): + investments = solution.get("investments", {}) + storage = solution.get("storage_energy_investments", {}) + transmission = solution.get("transmission_investments", {}) + for generator in data["G"]: + entry = investments.get(str(generator), {}) + pnew, pret = float(entry.get("new_units", 0.0)), abs(float(entry.get("retired_units", 0.0))) + data["y_P_new"][generator].LB = data["y_P_new"][generator].UB = pnew + data["y_P_ret"][generator].LB = data["y_P_ret"][generator].UB = pret + for generator in data["G_STOR"]: + entry = storage.get(str(generator)) + if entry is None: + data["y_E_new"][generator].LB = data["y_E_new"][generator].UB = 0.0 + data["y_E_ret"][generator].LB = data["y_E_ret"][generator].UB = 0.0 + else: + value = float(entry["new_units"]) + data["y_E_new"][generator].LB = data["y_E_new"][generator].UB = value + value = float(entry["total_energy_MWh"]) + data["y_E"][generator].LB = data["y_E"][generator].UB = value + for line in data["L"]: + value = float(transmission.get(str(line), {}).get("new_MW", 0.0)) + data["y_F_new"][line].LB = data["y_F_new"][line].UB = value + + +def generate_rahmaniani2022(): + module = load_module("rahmaniani2022") + for instance_path, solution_path in pairs("rahmaniani2022"): + instance, solution = read_json(instance_path), read_json(solution_path) + template, _ = module.build_model(instance, formulation="MILP", quiet=True) + assets.export_model(template, instance_path, snapshot_path("rahmaniani2022", instance_path)) + fixed, data = module.build_model(instance, formulation="MILP", quiet=True) + fix_rahmaniani2022(data, solution) + quiet_exact(fixed) + assets.update_solution(solution_path, fixed) + print("done", instance_path, flush=True) + + +def generate_riley2025(): + module = load_module("riley2025") + for instance_path, solution_path in pairs("riley2025"): + instance, solution = read_json(instance_path), read_json(solution_path) + module_sizes = int(instance["num_module_sizes"]) + units = int(instance["units_per_module_size"]) + fixed_y = [[0] * units for _ in range(module_sizes)] + for module_size in range(module_sizes): + count = sum(int(round(solution["y"][f"y_{module_size}_{unit}"])) for unit in range(units)) + for unit in range(count): + fixed_y[module_size][unit] = 1 + fixed, _, _, _ = module.build_and_solve(instance, 3600, fixed_y=fixed_y, output_flag=0) + if fixed.Status != GRB.OPTIMAL or fixed.SolCount < 1: + raise RuntimeError(f"riley2025 fixed solve failed: {fixed.Status}") + assets.update_solution(solution_path, fixed) + print("done", instance_path, flush=True) + + +GENERATORS = { + "adulyasak2015": generate_adulyasak2015, + "bertsimas2024": generate_bertsimas2024, + "cote2018": generate_cote2018, + "liao2020": generate_liao2020, + "rahmaniani2018": generate_rahmaniani2018, + "rahmaniani2022": generate_rahmaniani2022, + "riley2025": generate_riley2025, + "taninmis2022": generate_taninmis2022, +} + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("case", choices=sorted(GENERATORS)) + args = parser.parse_args() + GENERATORS[args.case]() diff --git a/audit/scripts/audit_all_official_replay.py b/audit/scripts/audit_all_official_replay.py new file mode 100644 index 0000000000000000000000000000000000000000..6c9fd27723c4e2723938d4b3f9a81d32d7d61b1f --- /dev/null +++ b/audit/scripts/audit_all_official_replay.py @@ -0,0 +1,349 @@ +#!/usr/bin/env python3 +"""Replay official FrontierOR solutions without conflating packaging failures.""" + +from __future__ import annotations + +import argparse +import concurrent.futures +import csv +import json +import subprocess +import sys +import tempfile +import time +from collections import Counter +from datetime import datetime, timezone +from pathlib import Path +from typing import Any + + +def solution_name(instance_name: str) -> str: + return instance_name.replace("_instance", "_solution", 1) + + +def result_name(instance_name: str) -> str: + return instance_name.replace("_instance", "_feasi_result", 1) + + +def git_value(root: Path, *args: str) -> str | None: + proc = subprocess.run( + ["git", "-C", str(root), *args], text=True, capture_output=True, check=False + ) + value = proc.stdout.strip() + return value if proc.returncode == 0 and value else None + + +def lfs_pointer(path: Path) -> dict[str, Any] | None: + """Return pointer metadata without reading a potentially huge JSON file.""" + try: + if path.stat().st_size > 1024: + return None + lines = path.read_text(encoding="utf-8").splitlines() + except (OSError, UnicodeError): + return None + if not lines or lines[0] != "version https://git-lfs.github.com/spec/v1": + return None + metadata: dict[str, Any] = {"path": str(path)} + for line in lines[1:]: + if line.startswith("oid "): + metadata["oid"] = line[4:] + elif line.startswith("size "): + try: + metadata["advertised_size_bytes"] = int(line[5:]) + except ValueError: + metadata["advertised_size_bytes"] = line[5:] + return metadata + + +def load_stored_result( + path: Path, +) -> tuple[bool | None, dict[str, Any] | None, str | None]: + if not path.is_file(): + return None, None, "stored result is missing" + try: + payload = json.loads(path.read_text(encoding="utf-8")) + except (OSError, UnicodeError, json.JSONDecodeError) as exc: + return None, None, f"{type(exc).__name__}: {exc}" + if not isinstance(payload, dict): + return None, None, f"stored result is {type(payload).__name__}, expected object" + feasible = payload.get("feasible") + return feasible if isinstance(feasible, bool) else None, payload, None + + +def base_record( + case_id: str, instance: Path, solution: Path, stored_result: Path +) -> dict[str, Any]: + return { + "case_id": case_id, + "instance": instance.name, + "solution": solution.name, + "stored_result": stored_result.name, + "instance_bytes": instance.stat().st_size if instance.is_file() else None, + "solution_bytes": solution.stat().st_size if solution.is_file() else None, + } + + +def run_one( + case_id: str, + checker: Path, + instance: Path, + solution: Path, + stored_result: Path, + timeout: float, +) -> dict[str, Any]: + record = base_record(case_id, instance, solution, stored_result) + missing = [str(path) for path in (checker, instance, solution) if not path.is_file()] + if missing: + return { + **record, + "outcome": "missing_required_file", + "feasible": None, + "stored_feasible": None, + "matches_stored": None, + "elapsed_seconds": 0.0, + "error": ", ".join(missing), + } + + pointer_inputs = { + label: metadata + for label, path in (("instance", instance), ("solution", solution)) + if (metadata := lfs_pointer(path)) is not None + } + if pointer_inputs: + return { + **record, + "outcome": "lfs_pointer", + "feasible": None, + "stored_feasible": None, + "matches_stored": None, + "elapsed_seconds": 0.0, + "lfs_pointer_inputs": pointer_inputs, + "error": "Git LFS objects have not been materialized", + } + + empty_inputs = [str(path) for path in (instance, solution) if path.stat().st_size == 0] + if empty_inputs: + return { + **record, + "outcome": "invalid_input_file", + "feasible": None, + "stored_feasible": None, + "matches_stored": None, + "elapsed_seconds": 0.0, + "error": "empty JSON input: " + ", ".join(empty_inputs), + } + + stored_feasible, stored_payload, stored_error = load_stored_result(stored_result) + with tempfile.TemporaryDirectory(prefix="frontieror-replay-") as tmp: + result_path = Path(tmp) / "result.json" + started = time.monotonic() + try: + proc = subprocess.run( + [ + sys.executable, + str(checker), + "--instance_path", + str(instance), + "--solution_path", + str(solution), + "--result_path", + str(result_path), + ], + text=True, + capture_output=True, + timeout=timeout, + check=False, + ) + except subprocess.TimeoutExpired as exc: + return { + **record, + "outcome": "timeout", + "feasible": None, + "stored_feasible": stored_feasible, + "stored_result_payload": stored_payload, + "stored_result_error": stored_error, + "matches_stored": None, + "elapsed_seconds": time.monotonic() - started, + "stdout_tail": (exc.stdout or "")[-2000:] + if isinstance(exc.stdout, str) + else "", + "stderr_tail": (exc.stderr or "")[-2000:] + if isinstance(exc.stderr, str) + else "", + "error": f"checker exceeded {timeout:g} seconds", + } + + elapsed = time.monotonic() - started + payload: Any = None + result_error = None + if result_path.is_file(): + try: + payload = json.loads(result_path.read_text(encoding="utf-8")) + except (OSError, UnicodeError, json.JSONDecodeError) as exc: + result_error = f"{type(exc).__name__}: {exc}" + else: + result_error = "checker did not create result_path" + + feasible = payload.get("feasible") if isinstance(payload, dict) else None + if isinstance(feasible, bool): + outcome = "accepted" if feasible else "rejected" + elif proc.returncode != 0: + outcome = "crash" + else: + outcome = "invalid_result" + + return { + **record, + "outcome": outcome, + "feasible": feasible, + "stored_feasible": stored_feasible, + "stored_result_payload": stored_payload, + "stored_result_error": stored_error, + "matches_stored": ( + feasible == stored_feasible + if isinstance(feasible, bool) and isinstance(stored_feasible, bool) + else None + ), + "elapsed_seconds": elapsed, + "returncode": proc.returncode, + "checker_result_payload": payload, + "stdout_tail": proc.stdout[-2000:], + "stderr_tail": proc.stderr[-2000:], + "error": result_error, + } + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--data-root", type=Path, required=True) + parser.add_argument("--output-dir", type=Path, required=True) + parser.add_argument("--output-stem", default="ALL_OFFICIAL_REPLAY") + parser.add_argument("--timeout", type=float, default=180.0) + parser.add_argument("--workers", type=int, default=2) + parser.add_argument( + "--case", + action="append", + dest="cases", + help="Restrict to one case ID; repeat for multiple cases.", + ) + parser.add_argument( + "--skip-case", + action="append", + dest="skipped_cases", + help="Skip one resource-blocked case; repeat for multiple cases.", + ) + args = parser.parse_args() + args.data_root = args.data_root.resolve() + args.output_dir.mkdir(parents=True, exist_ok=True) + selected_cases = set(args.cases or []) + skipped_cases = set(args.skipped_cases or []) + overlap = selected_cases & skipped_cases + if overlap: + parser.error(f"cases cannot be both selected and skipped: {sorted(overlap)}") + + tasks = [] + orphan_instances = [] + discovered_instances = 0 + discovered_cases = 0 + for case_dir in sorted(args.data_root.iterdir()): + instance_dir = case_dir / "instance" + if not instance_dir.is_dir() or ( + selected_cases and case_dir.name not in selected_cases + ) or case_dir.name in skipped_cases: + continue + discovered_cases += 1 + checker = case_dir / "feasibility_check.py" + for instance in sorted(instance_dir.glob("*.json")): + discovered_instances += 1 + solution = case_dir / "gurobi_solution" / solution_name(instance.name) + stored = case_dir / "gurobi_feasi_result" / result_name(instance.name) + if not solution.is_file(): + orphan_instances.append( + { + "case_id": case_dir.name, + "instance": instance.name, + "expected_solution": solution.name, + } + ) + continue + tasks.append((case_dir.name, checker, instance, solution, stored, args.timeout)) + + with concurrent.futures.ThreadPoolExecutor(max_workers=args.workers) as pool: + results = list(pool.map(lambda values: run_one(*values), tasks)) + results.sort(key=lambda item: (item["case_id"], item["instance"])) + + summary = { + "discovered_case_count": discovered_cases, + "discovered_instance_count": discovered_instances, + "paired_run_count": len(results), + "orphan_instance_count": len(orphan_instances), + "outcome_counts": dict(Counter(item["outcome"] for item in results)), + "stored_comparison_counts": dict( + Counter( + "match" + if item["matches_stored"] is True + else "mismatch" + if item["matches_stored"] is False + else "unavailable" + for item in results + ) + ), + "nonaccepted": [ + f"{item['case_id']}/{item['instance']}:{item['outcome']}" + for item in results + if item["outcome"] != "accepted" + ], + "stored_mismatches": [ + f"{item['case_id']}/{item['instance']}" + for item in results + if item["matches_stored"] is False + ], + } + payload = { + "generated_at": datetime.now(timezone.utc).isoformat(), + "source": { + "data_root": str(args.data_root), + "git_head": git_value(args.data_root, "rev-parse", "HEAD"), + "git_origin": git_value(args.data_root, "remote", "get-url", "origin"), + "git_status_porcelain": git_value( + args.data_root, "status", "--porcelain=v1" + ) + or "", + }, + "method": { + "scope": "JSON instances having a same-name official Gurobi solution", + "selected_cases": sorted(selected_cases), + "skipped_cases": sorted(skipped_cases), + "timeout_seconds_per_checker": args.timeout, + "workers": args.workers, + "packaging_failures_are_not_executed": True, + }, + "summary": summary, + "orphan_instances": orphan_instances, + "results": results, + } + json_path = args.output_dir / f"{args.output_stem}.json" + csv_path = args.output_dir / f"{args.output_stem}.csv" + json_path.write_text(json.dumps(payload, indent=2) + "\n", encoding="utf-8") + with csv_path.open("w", encoding="utf-8", newline="") as handle: + fields = ( + "case_id", + "instance", + "solution", + "outcome", + "feasible", + "stored_feasible", + "matches_stored", + "elapsed_seconds", + "instance_bytes", + "solution_bytes", + "error", + ) + writer = csv.DictWriter(handle, fieldnames=fields) + writer.writeheader() + writer.writerows({key: item.get(key) for key in fields} for item in results) + print(json.dumps(summary, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/audit/scripts/audit_checker_mutations.py b/audit/scripts/audit_checker_mutations.py new file mode 100644 index 0000000000000000000000000000000000000000..008ff212fe4dc492a4183d6212ec5b5021d841ed --- /dev/null +++ b/audit/scripts/audit_checker_mutations.py @@ -0,0 +1,391 @@ +#!/usr/bin/env python3 +"""Replay FrontierOR checkers and probe them with schema-level mutations.""" + +from __future__ import annotations + +import argparse +import concurrent.futures +import copy +import csv +import json +import subprocess +import sys +import tempfile +from collections import Counter +from pathlib import Path +from typing import Any + + +def is_objective_field(key: str) -> bool: + """Return whether *key* is the scalar consumed by FrontierOR eval. + + The previous substring rule treated decision fields such as + ``variable_values``, ``x_values`` and ``z_values`` as objective fields + merely because their names contain ``value``. That made the + ``only_objective_fields`` mutation retain a complete witness for some + cases and produced false positives. ``one_shot_eval.py`` compares the + candidate's top-level ``objective_value``; this probe must retain exactly + that scalar and nothing else. + """ + return key == "objective_value" + + +def erased(value: Any) -> Any: + if isinstance(value, dict): + return {} + if isinstance(value, list): + return [] + if isinstance(value, bool): + return not value + if isinstance(value, (int, float)) and not isinstance(value, bool): + return value + 1_000_003 + if isinstance(value, str): + return "__FRONTIEROR_AUDIT_INVALID__" + return "__FRONTIEROR_AUDIT_INVALID__" + + +def run_checker( + checker: Path, + instance: Path, + solution: dict[str, Any], + timeout: float, +) -> dict[str, Any]: + with tempfile.TemporaryDirectory(prefix="frontieror-check-") as tmp: + tmp_path = Path(tmp) + solution_path = tmp_path / "solution.json" + result_path = tmp_path / "result.json" + solution_path.write_text( + json.dumps(solution, allow_nan=True) + "\n", encoding="utf-8" + ) + command = [ + sys.executable, + str(checker), + "--instance_path", + str(instance), + "--solution_path", + str(solution_path), + "--result_path", + str(result_path), + ] + try: + proc = subprocess.run( + command, + text=True, + capture_output=True, + timeout=timeout, + check=False, + ) + except subprocess.TimeoutExpired as exc: + return { + "outcome": "timeout", + "feasible": None, + "returncode": None, + "stdout_tail": (exc.stdout or "")[-1000:] if isinstance(exc.stdout, str) else "", + "stderr_tail": (exc.stderr or "")[-1000:] if isinstance(exc.stderr, str) else "", + } + result: Any = None + result_error: str | None = None + if result_path.is_file(): + try: + result = json.loads(result_path.read_text(encoding="utf-8")) + except (OSError, UnicodeError, json.JSONDecodeError) as exc: + result_error = f"{type(exc).__name__}: {exc}" + feasible = result.get("feasible") if isinstance(result, dict) else None + if isinstance(feasible, bool): + outcome = "accepted" if feasible else "rejected" + elif proc.returncode != 0: + outcome = "crash" + else: + outcome = "invalid_result" + return { + "outcome": outcome, + "feasible": feasible, + "returncode": proc.returncode, + "result_error": result_error, + "violated_constraints": result.get("violated_constraints") if isinstance(result, dict) else None, + "stdout_tail": proc.stdout[-1000:], + "stderr_tail": proc.stderr[-1000:], + } + + +def mutations_for( + official: dict[str, Any], solution_schema: dict[str, Any] +) -> list[tuple[str, dict[str, Any], str]]: + mutations: list[tuple[str, dict[str, Any], str]] = [] + mutations.append(("empty_solution", {}, "must_reject")) + schema_keys = list(solution_schema) + decision_keys = [key for key in schema_keys if not is_objective_field(key)] + objective_keys = [key for key in schema_keys if is_objective_field(key)] + + reference_obj = official.get("objective_value") + if ( + isinstance(reference_obj, (int, float)) + and not isinstance(reference_obj, bool) + ): + delta = max(1.0, abs(float(reference_obj)) * 0.02) + for label, forged_obj in ( + ("higher", float(reference_obj) + delta), + ("lower", float(reference_obj) - delta), + ): + tampered = copy.deepcopy(official) + tampered["objective_value"] = forged_obj + mutations.append(( + f"objective_tamper_2pct:{label}", + tampered, + "must_reject_objective_tamper", + )) + + only_objective = { + key: copy.deepcopy(value) + for key, value in official.items() + if key in objective_keys + } + mutations.append(("only_objective_fields", only_objective, "must_reject")) + + for key in schema_keys: + if key not in official: + continue + missing = copy.deepcopy(official) + missing.pop(key, None) + expectation = "should_reject_schema_missing" + mutations.append((f"remove:{key}", missing, expectation)) + changed = copy.deepcopy(official) + erased_value = erased(changed[key]) + # Do not report a mutation that is byte-for-byte/structurally equal to + # the official value (most commonly [] -> [] or {} -> {}). Earlier + # runs mislabeled acceptance of these no-op probes as an integrity gap. + if erased_value == changed[key]: + continue + changed[key] = erased_value + expectation = ( + "objective_consistency_probe" if is_objective_field(key) + else "decision_integrity_probe" + ) + mutations.append((f"erase:{key}", changed, expectation)) + + present_decisions = [key for key in decision_keys if key in official] + if present_decisions: + all_erased = copy.deepcopy(official) + for key in present_decisions: + all_erased[key] = erased(all_erased[key]) + mutations.append(("erase_all_schema_decisions", all_erased, "must_reject")) + return mutations + + +def audit_case(case_dir: Path, timeout: float) -> dict[str, Any]: + case_id = case_dir.name + checker = case_dir / "feasibility_check.py" + instance = case_dir / "instance" / "tiny_instance.json" + solution_path = case_dir / "gurobi_solution" / "tiny_solution.json" + schema_path = case_dir / "solution_schema.json" + required = [checker, instance, solution_path, schema_path] + missing = [str(path.relative_to(case_dir)) for path in required if not path.is_file()] + if missing: + return { + "case_id": case_id, + "status": "infrastructure_missing", + "max_severity": "critical", + "missing": missing, + "baseline": None, + "mutations": [], + "findings": [{ + "severity": "critical", + "code": "dynamic_input_missing", + "evidence": ", ".join(missing), + }], + } + try: + official = json.loads(solution_path.read_text(encoding="utf-8")) + schema = json.loads(schema_path.read_text(encoding="utf-8")) + if not isinstance(official, dict) or not isinstance(schema, dict): + raise TypeError("solution or schema top level is not an object") + except (OSError, UnicodeError, json.JSONDecodeError, TypeError) as exc: + return { + "case_id": case_id, + "status": "input_parse_failure", + "max_severity": "critical", + "missing": [], + "baseline": None, + "mutations": [], + "findings": [{ + "severity": "critical", + "code": "dynamic_input_parse_failure", + "evidence": f"{type(exc).__name__}: {exc}", + }], + } + + baseline = run_checker(checker, instance, official, timeout) + mutation_results: list[dict[str, Any]] = [] + for name, mutation, expectation in mutations_for(official, schema): + result = run_checker(checker, instance, mutation, timeout) + mutation_results.append({ + "name": name, + "expectation": expectation, + **result, + }) + + findings: list[dict[str, str]] = [] + if baseline["outcome"] != "accepted": + findings.append({ + "severity": "critical", + "code": "official_tiny_solution_not_accepted", + "evidence": f"outcome={baseline['outcome']}, returncode={baseline['returncode']}", + }) + by_name = {item["name"]: item for item in mutation_results} + for name in ("empty_solution", "only_objective_fields", "erase_all_schema_decisions"): + item = by_name.get(name) + if item and item["outcome"] == "accepted": + findings.append({ + "severity": "critical", + "code": f"checker_accepts_{name}", + "evidence": "checker returned feasible=true for a solution without valid decisions", + }) + elif item and item["outcome"] in {"crash", "timeout", "invalid_result"}: + findings.append({ + "severity": "medium", + "code": f"checker_nonrobust_on_{name}", + "evidence": f"outcome={item['outcome']}, returncode={item['returncode']}", + }) + accepted_decision_erases = [ + item["name"] for item in mutation_results + if item["expectation"] == "decision_integrity_probe" + and item["outcome"] == "accepted" + ] + if accepted_decision_erases: + findings.append({ + "severity": "high", + "code": "checker_accepts_erased_schema_decision_field", + "evidence": ", ".join(accepted_decision_erases), + }) + accepted_missing = [ + item["name"] for item in mutation_results + if item["expectation"] == "should_reject_schema_missing" + and item["outcome"] == "accepted" + ] + if accepted_missing: + findings.append({ + "severity": "medium", + "code": "checker_accepts_missing_solution_schema_field", + "evidence": ", ".join(accepted_missing), + }) + accepted_objective = [ + item["name"] for item in mutation_results + if item["expectation"] == "objective_consistency_probe" + and item["outcome"] == "accepted" + ] + if accepted_objective: + findings.append({ + "severity": "medium", + "code": "checker_accepts_corrupted_objective_field", + "evidence": ", ".join(accepted_objective), + }) + accepted_preflight_tamper = [ + item["name"] for item in mutation_results + if item["expectation"] == "must_reject_objective_tamper" + and item["outcome"] == "accepted" + ] + if accepted_preflight_tamper: + findings.append({ + "severity": "critical", + "code": "checker_fails_objective_tamper_preflight", + "evidence": ", ".join(accepted_preflight_tamper), + }) + + order = {"critical": 4, "high": 3, "medium": 2, "low": 1} + max_severity = max((f["severity"] for f in findings), key=lambda s: order[s], default="none") + return { + "case_id": case_id, + "status": "flagged" if findings else "mutation_clear", + "max_severity": max_severity, + "missing": [], + "official_solution_keys": sorted(official), + "solution_schema_keys": sorted(schema), + "schema_fields_absent_from_official_solution": sorted(set(schema) - set(official)), + "baseline": baseline, + "mutations": mutation_results, + "findings": findings, + } + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--data-root", type=Path, required=True) + parser.add_argument("--output-dir", type=Path, required=True) + parser.add_argument("--timeout", type=float, default=15.0) + parser.add_argument("--workers", type=int, default=8) + args = parser.parse_args() + args.output_dir.mkdir(parents=True, exist_ok=True) + case_dirs = sorted( + path for path in args.data_root.iterdir() + if path.is_dir() and (path / "problem_description.txt").is_file() + ) + with concurrent.futures.ThreadPoolExecutor(max_workers=args.workers) as pool: + future_map = { + pool.submit(audit_case, case_dir, args.timeout): case_dir.name + for case_dir in case_dirs + } + results = [] + for future in concurrent.futures.as_completed(future_map): + case_id = future_map[future] + try: + results.append(future.result()) + except Exception as exc: # preserve coverage even for unexpected harness errors + results.append({ + "case_id": case_id, + "status": "harness_failure", + "max_severity": "critical", + "findings": [{ + "severity": "critical", + "code": "mutation_harness_failure", + "evidence": f"{type(exc).__name__}: {exc}", + }], + "baseline": None, + "mutations": [], + }) + results.sort(key=lambda item: item["case_id"]) + summary = { + "case_count": len(results), + "baseline_accepted": sum( + bool(item.get("baseline") and item["baseline"]["outcome"] == "accepted") + for item in results + ), + "status_counts": dict(Counter(item["status"] for item in results)), + "max_severity_counts": dict(Counter(item["max_severity"] for item in results)), + "finding_code_counts": dict(Counter( + finding["code"] for item in results for finding in item["findings"] + )), + } + payload = { + "method": { + "baseline": "rerun official feasibility checker on official tiny instance/solution", + "mutations": "empty, objective-only, remove/erase each schema field, erase all schema decisions", + "timeout_seconds_per_run": args.timeout, + "warning": "single-field acceptance can be legitimate when fields are redundant; critical decision-free acceptance requires manual confirmation", + }, + "summary": summary, + "results": results, + } + (args.output_dir / "CHECKER_MUTATION_AUDIT.json").write_text( + json.dumps(payload, indent=2) + "\n", encoding="utf-8" + ) + with (args.output_dir / "CHECKER_MUTATION_AUDIT.csv").open("w", encoding="utf-8", newline="") as handle: + writer = csv.DictWriter( + handle, + fieldnames=("case_id", "status", "max_severity", "baseline", "finding_codes", "evidence"), + ) + writer.writeheader() + for item in results: + baseline = item.get("baseline") + writer.writerow({ + "case_id": item["case_id"], + "status": item["status"], + "max_severity": item["max_severity"], + "baseline": baseline["outcome"] if baseline else "not_run", + "finding_codes": "|".join(f["code"] for f in item["findings"]), + "evidence": " || ".join(f["evidence"] for f in item["findings"]), + }) + print(json.dumps(summary, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/audit/scripts/audit_evaluator_preflight.py b/audit/scripts/audit_evaluator_preflight.py new file mode 100644 index 0000000000000000000000000000000000000000..0d13b8e753f32e050396f4796169d62ebe73a1de --- /dev/null +++ b/audit/scripts/audit_evaluator_preflight.py @@ -0,0 +1,111 @@ +#!/usr/bin/env python3 +"""Run FrontierOR's own objective-checker preflight over every dataset case.""" + +from __future__ import annotations + +import argparse +import concurrent.futures +import csv +import hashlib +import importlib +import json +import subprocess +import sys +from collections import Counter +from pathlib import Path +from typing import Any + + +def git_output(root: Path, *args: str) -> str: + proc = subprocess.run( + ["git", "-C", str(root), *args], capture_output=True, text=True, check=False + ) + return proc.stdout.strip() + + +def sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--code-root", type=Path, required=True) + parser.add_argument("--data-root", type=Path, required=True) + parser.add_argument("--output-dir", type=Path, required=True) + parser.add_argument("--workers", type=int, default=8) + args = parser.parse_args() + args.output_dir.mkdir(parents=True, exist_ok=True) + + sys.path.insert(0, str(args.code_root)) + checkers = importlib.import_module("frontieror.infra.checkers") + validate = checkers.validate_objective_checker + resolve_checker = checkers.feasibility_checker_path + + case_dirs = sorted( + path for path in args.data_root.iterdir() + if path.is_dir() and (path / "problem_description.txt").is_file() + ) + + def run(case_dir: Path) -> dict[str, Any]: + case_id = case_dir.name + dataset_checker = case_dir / "feasibility_check.py" + effective_checker = Path(resolve_checker( + paper_dir=str(case_dir), paper_id=case_id + )).resolve() + overlay_used = effective_checker != dataset_checker.resolve() + try: + validate(paper_dir=str(case_dir), instance="tiny") + except Exception as exc: + status = "failed" + error = f"{type(exc).__name__}: {exc}" + else: + status = "passed" + error = None + return { + "case_id": case_id, + "status": status, + "error": error, + "overlay_used": overlay_used, + "dataset_checker": str(dataset_checker), + "effective_checker": str(effective_checker), + "dataset_checker_sha256": sha256(dataset_checker), + "effective_checker_sha256": sha256(effective_checker), + } + + with concurrent.futures.ThreadPoolExecutor(max_workers=args.workers) as pool: + results = list(pool.map(run, case_dirs)) + results.sort(key=lambda item: item["case_id"]) + summary = { + "case_count": len(results), + "status_counts": dict(Counter(item["status"] for item in results)), + "overlay_count": sum(item["overlay_used"] for item in results), + "failed_cases": [item["case_id"] for item in results if item["status"] == "failed"], + "overlay_cases": [item["case_id"] for item in results if item["overlay_used"]], + } + payload = { + "evaluator_source": { + "remote": git_output(args.code_root, "remote", "get-url", "origin"), + "commit": git_output(args.code_root, "rev-parse", "HEAD"), + "gate": "frontieror.infra.checkers.validate_objective_checker", + }, + "dataset_commit": git_output(args.data_root, "rev-parse", "HEAD"), + "summary": summary, + "results": results, + } + (args.output_dir / "EVALUATOR_PREFLIGHT_AUDIT.json").write_text( + json.dumps(payload, indent=2) + "\n", encoding="utf-8" + ) + with (args.output_dir / "EVALUATOR_PREFLIGHT_AUDIT.csv").open( + "w", encoding="utf-8", newline="" + ) as handle: + writer = csv.DictWriter( + handle, + fieldnames=("case_id", "status", "overlay_used", "error", "effective_checker"), + ) + writer.writeheader() + writer.writerows({key: item[key] for key in writer.fieldnames} for item in results) + print(json.dumps(summary, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/audit/scripts/audit_reference_certification.py b/audit/scripts/audit_reference_certification.py new file mode 100644 index 0000000000000000000000000000000000000000..a947f50f30ae155b0a93fd4f4a7547d67411bac5 --- /dev/null +++ b/audit/scripts/audit_reference_certification.py @@ -0,0 +1,205 @@ +#!/usr/bin/env python3 +"""Inventory certification metadata in every bundled Gurobi reference. + +This audit is deliberately separate from feasibility replay. A checker can +accept an incumbent while the JSON still lacks evidence that its objective is +globally optimal. FrontierOR scoring can use incumbents as baselines, but the +distinction must remain explicit and must not be reported as a zero-gap solve. +""" + +from __future__ import annotations + +import argparse +import csv +import json +import math +from collections import Counter, defaultdict +from pathlib import Path +from typing import Any + + +GAP_KEYS = ("mip_gap", "MIPGap", "optimality_gap", "relative_gap", "gap") + + +def finite_number(value: Any) -> float | None: + if isinstance(value, bool): + return None + try: + number = float(value) + except (TypeError, ValueError): + return None + return number if math.isfinite(number) else None + + +def status_fields(payload: dict[str, Any]) -> dict[str, Any]: + return { + key: value + for key, value in payload.items() + if "status" in key.lower() or "termination" in key.lower() + } + + +def classify(payload: dict[str, Any]) -> tuple[str, dict[str, Any], float | None]: + statuses = status_fields(payload) + gap = next( + ( + parsed + for key in GAP_KEYS + if key in payload and (parsed := finite_number(payload[key])) is not None + ), + None, + ) + objective = finite_number(payload.get("objective_value")) + if objective is None: + return "no_finite_objective", statuses, gap + + normalized = [str(value).strip().upper() for value in statuses.values()] + explicit_optimal = any( + (isinstance(value, int) and not isinstance(value, bool) and value == 2) + or (isinstance(value, str) and value.strip().upper() == "OPTIMAL") + for value in statuses.values() + ) + nonoptimal_tokens = ( + "TIME", "FEASIBLE", "CUTOFF", "SUBOPTIMAL", "INTERRUPT", + "NODE_LIMIT", "ITERATION_LIMIT", "SOLUTION_LIMIT", "OTHER_", + ) + explicit_nonoptimal = any( + (isinstance(value, int) and not isinstance(value, bool) and value in range(6, 14)) + or any(token in text for token in nonoptimal_tokens) + for value, text in zip(statuses.values(), normalized) + ) + + if explicit_optimal: + if gap is not None and abs(gap) > 1e-9: + return "solver_optimal_with_tolerance", statuses, gap + return "solver_optimal", statuses, gap + if explicit_nonoptimal: + if gap is not None and abs(gap) <= 1e-9: + return "nonoptimal_status_with_zero_gap", statuses, gap + return "explicit_incumbent", statuses, gap + if gap is not None and abs(gap) <= 1e-9: + return "zero_gap_claim_without_optimal_status", statuses, gap + if statuses: + return "ambiguous_status", statuses, gap + return "objective_without_certificate", statuses, gap + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--data-root", type=Path, required=True) + parser.add_argument("--output-dir", type=Path, required=True) + parser.add_argument("--output-stem", default="REFERENCE_CERTIFICATION_AUDIT") + args = parser.parse_args() + + results: list[dict[str, Any]] = [] + for case_dir in sorted(path for path in args.data_root.iterdir() if path.is_dir()): + solution_dir = case_dir / "gurobi_solution" + if not solution_dir.is_dir(): + continue + for solution_path in sorted(solution_dir.glob("*.json")): + instance_name = solution_path.name.replace("solution", "instance", 1) + instance_path = case_dir / "instance" / instance_name + try: + raw = solution_path.read_text(encoding="utf-8") + payload = json.loads(raw) + if not isinstance(payload, dict): + raise TypeError("top-level JSON is not an object") + except (OSError, UnicodeError, json.JSONDecodeError, TypeError) as exc: + results.append({ + "case_id": case_dir.name, + "solution": solution_path.name, + "instance": instance_name, + "instance_exists": instance_path.is_file(), + "classification": "invalid_solution_json", + "objective_value": None, + "status_fields": {}, + "reported_gap": None, + "error": f"{type(exc).__name__}: {exc}", + }) + continue + classification, statuses, gap = classify(payload) + results.append({ + "case_id": case_dir.name, + "solution": solution_path.name, + "instance": instance_name, + "instance_exists": instance_path.is_file(), + "classification": classification, + "objective_value": finite_number(payload.get("objective_value")), + "status_fields": statuses, + "reported_gap": gap, + "error": None, + }) + + by_case: dict[str, list[dict[str, Any]]] = defaultdict(list) + for row in results: + by_case[row["case_id"]].append(row) + + certified = { + "solver_optimal", + "solver_optimal_with_tolerance", + "zero_gap_claim_without_optimal_status", + } + case_results = [] + for case_id, rows in sorted(by_case.items()): + counts = Counter(row["classification"] for row in rows) + unproven = [row for row in rows if row["classification"] not in certified] + gaps = [abs(row["reported_gap"]) for row in rows if row["reported_gap"] is not None] + case_results.append({ + "case_id": case_id, + "reference_count": len(rows), + "classification_counts": dict(counts), + "unproven_count": len(unproven), + "unproven_references": [row["solution"] for row in unproven], + "max_reported_gap": max(gaps) if gaps else None, + "all_instances_present": all(row["instance_exists"] for row in rows), + }) + + summary = { + "case_count": len(by_case), + "reference_count": len(results), + "classification_counts": dict(Counter(row["classification"] for row in results)), + "cases_with_unproven_reference": sum(row["unproven_count"] > 0 for row in case_results), + "unproven_reference_count": sum(row["unproven_count"] for row in case_results), + "cases_with_invalid_or_nonfinite_reference": sum( + any( + key in row["classification_counts"] + for key in ("invalid_solution_json", "no_finite_objective") + ) + for row in case_results + ), + "reference_without_instance_count": sum(not row["instance_exists"] for row in results), + "cases_with_reference_without_instance": sum( + not row["all_instances_present"] for row in case_results + ), + } + output = { + "method": { + "scope": "all JSON files under every gurobi_solution directory", + "interpretation": ( + "Certification metadata is advisory for a scoring baseline: an incumbent may be " + "feasible, but it is not an optimality certificate." + ), + }, + "summary": summary, + "cases": case_results, + "results": results, + } + + args.output_dir.mkdir(parents=True, exist_ok=True) + json_path = args.output_dir / f"{args.output_stem}.json" + csv_path = args.output_dir / f"{args.output_stem}.csv" + json_path.write_text(json.dumps(output, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") + with csv_path.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=[ + "case_id", "solution", "instance", "instance_exists", "classification", + "objective_value", "reported_gap", "status_fields", "error", + ]) + writer.writeheader() + for row in results: + writer.writerow({**row, "status_fields": json.dumps(row["status_fields"], sort_keys=True)}) + + print(json.dumps(summary, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/audit/scripts/audit_triad_static.py b/audit/scripts/audit_triad_static.py new file mode 100644 index 0000000000000000000000000000000000000000..9925a19bcf039d4f07a6a2445ab06f26f20d63f9 --- /dev/null +++ b/audit/scripts/audit_triad_static.py @@ -0,0 +1,443 @@ +#!/usr/bin/env python3 +"""Static triad audit for FrontierOR description/model/checker coherence.""" + +from __future__ import annotations + +import argparse +import ast +import csv +import hashlib +import json +import re +import subprocess +from collections import Counter +from pathlib import Path +from typing import Any + + +REQUIRED_FILES = ( + "problem_description.txt", + "mathematical_formulation.md", + "instance_schema.json", + "solution_schema.json", + "gurobi_code.py", + "feasibility_check.py", +) + + +def sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as handle: + for chunk in iter(lambda: handle.read(1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +def git_output(root: Path, *args: str) -> str: + proc = subprocess.run( + ["git", "-C", str(root), *args], + text=True, + capture_output=True, + check=False, + ) + return proc.stdout.strip() + + +def remote_head(remote: str) -> str | None: + if not remote: + return None + proc = subprocess.run( + ["git", "ls-remote", remote, "HEAD"], + text=True, + capture_output=True, + check=False, + timeout=30, + ) + if proc.returncode != 0 or not proc.stdout.strip(): + return None + return proc.stdout.split()[0] + + +def schema_paths(value: Any, prefix: tuple[str, ...] = ()) -> list[str]: + if isinstance(value, dict): + paths: list[str] = [] + for key, child in value.items(): + child_prefix = (*prefix, str(key)) + paths.extend(schema_paths(child, child_prefix)) + return paths + return [".".join(prefix)] if prefix else [] + + +def parse_python(path: Path) -> tuple[ast.AST | None, str | None]: + try: + return ast.parse(path.read_text(encoding="utf-8"), filename=str(path)), None + except (OSError, UnicodeError, SyntaxError) as exc: + return None, f"{type(exc).__name__}: {exc}" + + +def constant_dict_keys(node: ast.AST) -> set[str]: + keys: set[str] = set() + for child in ast.walk(node): + if isinstance(child, ast.Dict): + for key in child.keys: + if isinstance(key, ast.Constant) and isinstance(key.value, str): + keys.add(key.value) + return keys + + +def all_string_subscripts(node: ast.AST) -> set[str]: + keys: set[str] = set() + for child in ast.walk(node): + if isinstance(child, ast.Subscript): + part = child.slice + if isinstance(part, ast.Constant) and isinstance(part.value, str): + keys.add(part.value) + if ( + isinstance(child, ast.Call) + and isinstance(child.func, ast.Attribute) + and child.func.attr == "get" + and child.args + and isinstance(child.args[0], ast.Constant) + and isinstance(child.args[0].value, str) + ): + keys.add(child.args[0].value) + return keys + + +def root_name(node: ast.AST) -> str | None: + current = node + while isinstance(current, (ast.Subscript, ast.Attribute)): + current = current.value + return current.id if isinstance(current, ast.Name) else None + + +def keys_read_from_roots(node: ast.AST, roots: set[str]) -> set[str]: + keys: set[str] = set() + for child in ast.walk(node): + if isinstance(child, ast.Subscript) and root_name(child) in roots: + part = child.slice + if isinstance(part, ast.Constant) and isinstance(part.value, str): + keys.add(part.value) + elif ( + isinstance(child, ast.Call) + and isinstance(child.func, ast.Attribute) + and child.func.attr == "get" + and root_name(child.func.value) in roots + and child.args + and isinstance(child.args[0], ast.Constant) + and isinstance(child.args[0].value, str) + ): + keys.add(child.args[0].value) + elif isinstance(child, ast.Compare): + if ( + isinstance(child.left, ast.Constant) + and isinstance(child.left.value, str) + and any( + isinstance(op, (ast.In, ast.NotIn)) + and root_name(comp) in roots + for op, comp in zip(child.ops, child.comparators) + ) + ): + keys.add(child.left.value) + return keys + + +def json_dump_names(node: ast.AST) -> set[str]: + names: set[str] = set() + for child in ast.walk(node): + if not isinstance(child, ast.Call) or not child.args: + continue + func = child.func + if ( + isinstance(func, ast.Attribute) + and func.attr in {"dump", "dumps"} + and isinstance(child.args[0], ast.Name) + ): + names.add(child.args[0].id) + return names + + +def keys_written_to_names(node: ast.AST, names: set[str]) -> set[str]: + keys: set[str] = set() + for child in ast.walk(node): + if isinstance(child, (ast.Assign, ast.AnnAssign)): + targets = child.targets if isinstance(child, ast.Assign) else [child.target] + value = child.value + if value is not None and isinstance(value, ast.Dict): + if any(isinstance(t, ast.Name) and t.id in names for t in targets): + for key in value.keys: + if isinstance(key, ast.Constant) and isinstance(key.value, str): + keys.add(key.value) + for target in targets: + if ( + isinstance(target, ast.Subscript) + and root_name(target) in names + and isinstance(target.slice, ast.Constant) + and isinstance(target.slice.value, str) + ): + keys.add(target.slice.value) + return keys + + +def model_senses(text: str) -> list[str]: + senses: set[str] = set() + if re.search(r"GRB\.MINIMIZE|ModelSense\s*=\s*1", text): + senses.add("minimize") + if re.search(r"GRB\.MAXIMIZE|ModelSense\s*=\s*-1", text): + senses.add("maximize") + return sorted(senses) + + +def described_senses(text: str) -> list[str]: + lower = text.lower() + senses: set[str] = set() + objective_windows = re.findall( + r".{0,100}(?:objective|goal|task).{0,220}", lower, flags=re.DOTALL + ) + focus = " ".join(objective_windows) if objective_windows else lower + if re.search(r"\bminimi[sz]|\bfewest\b|\bminimum (?:cost|number|total|time|distance)", focus): + senses.add("minimize") + if re.search(r"\bmaximi[sz]|\bmost\b|\bmaximum (?:profit|value|number|total|coverage|reward)", focus): + senses.add("maximize") + return sorted(senses) + + +def add_finding( + findings: list[dict[str, str]], severity: str, code: str, evidence: str +) -> None: + findings.append({"severity": severity, "code": code, "evidence": evidence}) + + +def audit_case(case_dir: Path) -> dict[str, Any]: + case_id = case_dir.name + missing = [name for name in REQUIRED_FILES if not (case_dir / name).is_file()] + findings: list[dict[str, str]] = [] + if missing: + severity = "high" if any( + name in missing for name in ("problem_description.txt", "gurobi_code.py", "feasibility_check.py") + ) else "medium" + add_finding(findings, severity, "missing_required_file", ", ".join(missing)) + + instance_schema: dict[str, Any] = {} + solution_schema: dict[str, Any] = {} + json_errors: dict[str, str] = {} + for name, target in ( + ("instance_schema.json", "instance"), + ("solution_schema.json", "solution"), + ): + path = case_dir / name + if not path.is_file(): + continue + try: + loaded = json.loads(path.read_text(encoding="utf-8")) + if not isinstance(loaded, dict): + raise TypeError("schema top level is not an object") + if target == "instance": + instance_schema = loaded + else: + solution_schema = loaded + except (OSError, UnicodeError, json.JSONDecodeError, TypeError) as exc: + json_errors[name] = f"{type(exc).__name__}: {exc}" + add_finding(findings, "critical", "invalid_schema_json", f"{name}: {exc}") + + model_tree, model_error = parse_python(case_dir / "gurobi_code.py") if (case_dir / "gurobi_code.py").is_file() else (None, None) + checker_tree, checker_error = parse_python(case_dir / "feasibility_check.py") if (case_dir / "feasibility_check.py").is_file() else (None, None) + if model_error: + add_finding(findings, "critical", "model_python_parse_error", model_error) + if checker_error: + add_finding(findings, "critical", "checker_python_parse_error", checker_error) + + description = (case_dir / "problem_description.txt").read_text(encoding="utf-8") if (case_dir / "problem_description.txt").is_file() else "" + formulation = (case_dir / "mathematical_formulation.md").read_text(encoding="utf-8") if (case_dir / "mathematical_formulation.md").is_file() else "" + model_text = (case_dir / "gurobi_code.py").read_text(encoding="utf-8") if (case_dir / "gurobi_code.py").is_file() else "" + checker_text = (case_dir / "feasibility_check.py").read_text(encoding="utf-8") if (case_dir / "feasibility_check.py").is_file() else "" + + description_sense = described_senses(description) + formulation_sense = described_senses(formulation) + gurobi_sense = model_senses(model_text) + if len(gurobi_sense) == 1 and len(description_sense) == 1 and gurobi_sense != description_sense: + add_finding( + findings, + "critical", + "description_model_objective_mismatch", + f"description={description_sense[0]}, model={gurobi_sense[0]}", + ) + if len(gurobi_sense) == 1 and len(formulation_sense) == 1 and gurobi_sense != formulation_sense: + add_finding( + findings, + "critical", + "formulation_model_objective_mismatch", + f"formulation={formulation_sense[0]}, model={gurobi_sense[0]}", + ) + if not gurobi_sense: + add_finding(findings, "medium", "model_objective_sense_not_detected", "no literal Gurobi sense found") + elif len(gurobi_sense) > 1: + add_finding(findings, "medium", "multiple_model_objective_senses", ", ".join(gurobi_sense)) + + instance_top = set(instance_schema) + solution_top = set(solution_schema) + model_keys = all_string_subscripts(model_tree) if model_tree else set() + checker_keys = all_string_subscripts(checker_tree) if checker_tree else set() + dumped_names = json_dump_names(model_tree) if model_tree else set() + model_output_keys = keys_written_to_names(model_tree, dumped_names) if model_tree else set() + checker_solution_keys = keys_read_from_roots( + checker_tree, {"solution", "sol", "solution_data", "candidate"} + ) if checker_tree else set() + + missing_model_output = sorted(solution_top - model_output_keys) + if missing_model_output: + add_finding( + findings, + "high", + "solution_schema_fields_not_detected_in_model_output", + ", ".join(missing_model_output), + ) + unchecked_schema_fields = sorted(solution_top - checker_solution_keys) + if unchecked_schema_fields: + add_finding( + findings, + "medium", + "solution_schema_fields_not_detected_in_checker", + ", ".join(unchecked_schema_fields), + ) + if solution_top and not (solution_top & checker_solution_keys): + add_finding( + findings, + "critical", + "checker_solution_interface_disjoint_from_schema", + f"schema={sorted(solution_top)}, checker={sorted(checker_solution_keys)}", + ) + instance_keys_absent_model = sorted(instance_top - model_keys) + instance_keys_absent_checker = sorted(instance_top - checker_keys) + if instance_keys_absent_model: + add_finding( + findings, + "medium", + "instance_top_fields_not_detected_in_model", + ", ".join(instance_keys_absent_model), + ) + if instance_keys_absent_checker: + add_finding( + findings, + "low", + "instance_top_fields_not_detected_in_checker", + ", ".join(instance_keys_absent_checker), + ) + + if "feasible" not in checker_text: + add_finding(findings, "high", "checker_feasible_result_not_detected", "literal feasible absent") + if not re.search(r"len\s*\([^)]*violat|not\s+violat|==\s*0", checker_text, flags=re.I): + add_finding(findings, "medium", "checker_acceptance_logic_needs_review", "standard violation aggregation not detected") + + severity_order = {"critical": 4, "high": 3, "medium": 2, "low": 1} + max_severity = max((item["severity"] for item in findings), key=lambda s: severity_order[s], default="none") + status = "flagged" if findings else "static_clear" + return { + "case_id": case_id, + "status": status, + "max_severity": max_severity, + "missing_files": missing, + "json_errors": json_errors, + "python_parse": {"model": model_error or "ok", "checker": checker_error or "ok"}, + "objective": { + "description": description_sense, + "formulation": formulation_sense, + "gurobi": gurobi_sense, + }, + "interfaces": { + "instance_schema_paths": schema_paths(instance_schema), + "instance_schema_top_fields": sorted(instance_top), + "solution_schema_paths": schema_paths(solution_schema), + "solution_schema_top_fields": sorted(solution_top), + "model_literal_access_keys": sorted(model_keys), + "model_json_dump_names": sorted(dumped_names), + "model_output_keys": sorted(model_output_keys), + "checker_literal_access_keys": sorted(checker_keys), + "checker_solution_keys": sorted(checker_solution_keys), + }, + "source_hashes": { + name: sha256(case_dir / name) + for name in REQUIRED_FILES + if (case_dir / name).is_file() + }, + "findings": findings, + } + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--data-root", type=Path, required=True) + parser.add_argument("--output-dir", type=Path, required=True) + args = parser.parse_args() + args.output_dir.mkdir(parents=True, exist_ok=True) + + case_dirs = sorted( + path for path in args.data_root.iterdir() + if path.is_dir() and (path / "problem_description.txt").is_file() + ) + results = [audit_case(path) for path in case_dirs] + severity_counts = Counter(item["max_severity"] for item in results) + finding_counts = Counter( + finding["code"] for item in results for finding in item["findings"] + ) + remote = git_output(args.data_root, "remote", "get-url", "origin") + commit = git_output(args.data_root, "rev-parse", "HEAD") + upstream_head = remote_head(remote) + snapshot = { + "dataset": "SmartOR/FrontierOR", + "remote": remote, + "commit": commit, + "remote_head": upstream_head, + "head_matches_remote": upstream_head == commit if upstream_head else None, + "case_count": len(case_dirs), + "required_files": list(REQUIRED_FILES), + "required_file_coverage": { + name: sum((path / name).is_file() for path in case_dirs) + for name in REQUIRED_FILES + }, + } + (args.output_dir / "SOURCE_SNAPSHOT.json").write_text( + json.dumps(snapshot, indent=2) + "\n", encoding="utf-8" + ) + payload = { + "source_snapshot": snapshot, + "method": { + "scope": "static triad interface and objective-direction screening", + "warning": "flags are screening results; semantic confirmation is separate", + }, + "summary": { + "case_count": len(results), + "status_counts": dict(Counter(item["status"] for item in results)), + "max_severity_counts": dict(severity_counts), + "finding_code_counts": dict(finding_counts), + }, + "results": results, + } + (args.output_dir / "STATIC_AUDIT.json").write_text( + json.dumps(payload, indent=2) + "\n", encoding="utf-8" + ) + with (args.output_dir / "STATIC_AUDIT.csv").open("w", encoding="utf-8", newline="") as handle: + writer = csv.DictWriter( + handle, + fieldnames=( + "case_id", "status", "max_severity", "description_sense", + "formulation_sense", "gurobi_sense", "finding_codes", "evidence", + ), + ) + writer.writeheader() + for item in results: + writer.writerow({ + "case_id": item["case_id"], + "status": item["status"], + "max_severity": item["max_severity"], + "description_sense": "|".join(item["objective"]["description"]), + "formulation_sense": "|".join(item["objective"]["formulation"]), + "gurobi_sense": "|".join(item["objective"]["gurobi"]), + "finding_codes": "|".join(f["code"] for f in item["findings"]), + "evidence": " || ".join(f["evidence"] for f in item["findings"]), + }) + print(json.dumps(payload["summary"], indent=2)) + + +if __name__ == "__main__": + main() diff --git a/audit/scripts/build_final_180_report.py b/audit/scripts/build_final_180_report.py new file mode 100644 index 0000000000000000000000000000000000000000..0a3342e84c71cf095ce2e55951ed0e42bd054064 --- /dev/null +++ b/audit/scripts/build_final_180_report.py @@ -0,0 +1,432 @@ +#!/usr/bin/env python3 +"""Build the final FrontierOR 180-case audit matrix and human summary.""" + +from __future__ import annotations + +import argparse +import csv +import json +import subprocess +from collections import Counter, defaultdict +from datetime import datetime +from pathlib import Path +from typing import Any +from zoneinfo import ZoneInfo + + +MANUAL_MUTATION_DISPOSITIONS = { + "cote2018": { + "effective_status": "clear_intentional_value_certificate", + "reason": ( + "The published solution contract is intentionally objective-only; " + "the trusted checker independently solves the finite model and accepts " + "only the exact optimum. Both objective-tamper probes are rejected." + ), + }, + "rahmaniani2022": { + "effective_status": "clear_valid_alternative_sparse_decision", + "reason": ( + "Erasing the sparse storage-energy map produces a different zero-map " + "investment decision. The checker fixes that decision, proves its exact " + "operational recourse optimum, and accepts it only because the unchanged " + "reported objective is still exact; objective-tamper probes are rejected." + ), + }, +} + + +DEEP_REPAIR_NOTES = { + "bard2002": "Reconciled service-time encoding and regenerated complete references.", + "bertsimas2024": "Recomputed stale fixed-design objectives and strengthened the fixed-design check.", + "borndorfer2007": "Completed passenger path-flow certificates; all demand, capacity, and objective terms are exact.", + "byeon2022": "Released a finite integrated follower certificate and regenerated every bundled reference.", + "cherkesly2015": "Implemented full LIFO semantics and regenerated feasible references.", + "cordeau2014": "Repaired callbacks/cuts and replayed complete routing witnesses.", + "damcikurt2016": "Replaced truncated policies with sparse complete-policy certificates and explicit defaults.", + "dayarian2019": "Added ordered routes and complete f/beta/w/alpha staffing certificates; objective is exactly recomputed.", + "fragkos2021": "Regenerated the stale references under the strengthened checker.", + "frey2017": "Corrected carousel type capacities and synchronized all feasibility results.", + "gschwind2021": "Replaced restricted/heuristic certification with exact s-plex enumeration.", + "hadjar2006": "Removed false optimality claims and retained truthful bound/gap metadata.", + "kress2019": "Added complete landside lift/drop events and mandatory-job enforcement.", + "legrain2024": "Added complete skill-assignment witnesses and aligned instance flags/contract.", + "lozano2017": "Stored the missing coefficients and exactly repaired fixed-leader follower solutions.", + "mehrotra1996": "Replaced restricted column certification with exact graph coloring.", + "muter2010": "Replaced the restricted route pool with an exact arc-flow certificate.", + "oliveira2020": "Released a compact disjunctive model and repaired invalid horizons/references.", + "ostrowski2012": "Converted the mislabeled duplicate into a disclosed compatibility alias of earl2005.", + "potthoff2010": "Released a finite relief-point completion contract and regenerated all references.", + "rahmaniani2022": "Aligned investment domains and bound every sparse investment to exact recourse.", + "riley2025": "Completed the omitted recourse verification for nonzero investments.", + "roberti2018": "Repaired satellite route endpoints and recomputed the exact objective.", + "wu2022": "Regenerated the missing large incumbent and retained a bounded-memory sparse checker.", + "wu2023": "Added the omitted one-stage/two-stage decision certificate and exact binding.", + "zohali2022": "Generated a complete task/station/sequence certificate and exact setup objective.", +} + + +def load_json(path: Path) -> dict[str, Any]: + value = json.loads(path.read_text(encoding="utf-8")) + if not isinstance(value, dict): + raise TypeError(f"expected JSON object: {path}") + return value + + +def write_json(path: Path, value: Any) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text( + json.dumps(value, indent=2, ensure_ascii=False, allow_nan=False) + "\n", + encoding="utf-8", + ) + + +def git_lines(root: Path, *args: str) -> list[str]: + proc = subprocess.run( + ["git", "-C", str(root), *args], + text=True, + capture_output=True, + check=True, + ) + return [line for line in proc.stdout.splitlines() if line] + + +def changed_paths(root: Path) -> list[str]: + paths = set(git_lines(root, "diff", "--name-only", "HEAD", "--")) + paths.update(git_lines(root, "ls-files", "--others", "--exclude-standard")) + return sorted( + path + for path in paths + if "__pycache__" not in Path(path).parts and not path.endswith(".pyc") + ) + + +def categories(paths: list[str]) -> dict[str, bool | int | list[str]]: + names = [Path(path) for path in paths] + return { + "prompt_changed": any(path.name == "problem_description.txt" for path in names), + "formulation_changed": any( + path.name in {"mathematical_formulation.md", "math_model.txt"} + for path in names + ), + "model_changed": any( + path.name in {"gurobi_code.py", "release_contract.py"} for path in names + ), + "checker_changed": any(path.name == "feasibility_check.py" for path in names), + "instance_changed": any( + path.parts and (path.parts[0] == "instance" or path.name == "instance_schema.json") + for path in names + ), + "solution_changed": any( + path.parts and (path.parts[0] == "gurobi_solution" or path.name == "solution_schema.json") + for path in names + ), + "stored_result_changed": any( + path.parts and path.parts[0] == "gurobi_feasi_result" for path in names + ), + "solver_log_changed": any( + path.parts and path.parts[0] == "gurobi_solution_log" for path in names + ), + "changed_file_count": len(paths), + "changed_files": paths, + } + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--workspace", type=Path, required=True) + parser.add_argument("--data-root", type=Path, required=True) + parser.add_argument("--replay-json", type=Path, required=True) + parser.add_argument("--gate-dir", type=Path, required=True) + parser.add_argument("--output-dir", type=Path, required=True) + args = parser.parse_args() + workspace = args.workspace.resolve() + data_root = args.data_root.resolve() + gate_dir = args.gate_dir.resolve() + output_dir = args.output_dir.resolve() + output_dir.mkdir(parents=True, exist_ok=True) + + replay = load_json(args.replay_json.resolve()) + preflight = load_json(gate_dir / "EVALUATOR_PREFLIGHT_AUDIT.json") + mutations = load_json(gate_dir / "CHECKER_MUTATION_AUDIT.json") + static = load_json(gate_dir / "STATIC_AUDIT.json") + certification = load_json(gate_dir / "REFERENCE_CERTIFICATION_AUDIT.json") + phase1 = load_json(workspace / "Audit/CASE_AUDIT_MATRIX.json") + phase84 = load_json( + workspace / "Audit/REPAIR_84/FINAL_NO_DATA/REPAIR_STATUS_84.json" + ) + release92 = load_json(data_root / "release_subset_92.json") + + phase1_by_case = {row["case_id"]: row for row in phase1["results"]} + phase84_by_case = {row["case_id"]: row for row in phase84["results"]} + preflight_by_case = {row["case_id"]: row for row in preflight["results"]} + mutation_by_case = {row["case_id"]: row for row in mutations["results"]} + static_by_case = {row["case_id"]: row for row in static["results"]} + certified_by_case = {row["case_id"]: row for row in certification["cases"]} + clean92 = {row["case_id"] for row in release92["cases"]} + + replay_by_case: dict[str, list[dict[str, Any]]] = defaultdict(list) + for row in replay["results"]: + replay_by_case[row["case_id"]].append(row) + + all_changed = changed_paths(data_root) + changed_by_case: dict[str, list[str]] = defaultdict(list) + root_changes = [] + case_ids = sorted( + path.name + for path in data_root.iterdir() + if path.is_dir() and (path / "problem_description.txt").is_file() + ) + case_set = set(case_ids) + for path in all_changed: + parts = Path(path).parts + if parts and parts[0] in case_set: + changed_by_case[parts[0]].append(Path(*parts[1:]).as_posix()) + else: + root_changes.append(path) + + rows = [] + for case_id in case_ids: + replay_rows = replay_by_case[case_id] + outcomes = Counter(row["outcome"] for row in replay_rows) + stored_mismatches = [ + row["instance"] for row in replay_rows if row["matches_stored"] is False + ] + mutation = mutation_by_case[case_id] + manual_mutation = MANUAL_MUTATION_DISPOSITIONS.get(case_id) + mutation_effective_clear = ( + mutation["status"] == "mutation_clear" or manual_mutation is not None + ) + preflight_status = preflight_by_case[case_id]["status"] + official_clear = bool(replay_rows) and outcomes == {"accepted": len(replay_rows)} + eval_ready = ( + official_clear + and not stored_mismatches + and preflight_status == "passed" + and mutation_effective_clear + ) + identity_status = ( + "legacy_alias_of_earl2005" + if case_id == "ostrowski2012" + else "canonical_independent_case" + ) + phase1_row = phase1_by_case[case_id] + phase84_row = phase84_by_case.get(case_id, {}) + change = categories(changed_by_case.get(case_id, [])) + certification_row = certified_by_case.get(case_id, {}) + rows.append( + { + "case_id": case_id, + "eval_ready": eval_ready, + "publication_status": ( + "EVAL_READY_LEGACY_ALIAS" if case_id == "ostrowski2012" + else "EVAL_READY_CANONICAL" + ) + if eval_ready + else "NOT_READY", + "identity_status": identity_status, + "independent_benchmark": case_id != "ostrowski2012", + "original_audited_92": case_id in clean92, + "phase1_semantic_disposition": phase1_row["semantic_disposition"], + "phase1_semantic_basis": phase1_row["semantic_basis"], + "phase84_previous_disposition": phase84_row.get("disposition"), + "phase84_previous_reason": phase84_row.get("reason"), + "resolution_note": DEEP_REPAIR_NOTES.get(case_id, "Released checker/reference contract replayed and closed."), + "official_pair_count": len(replay_rows), + "official_accepted_count": outcomes.get("accepted", 0), + "official_outcome_counts": dict(outcomes), + "stored_result_mismatches": stored_mismatches, + "evaluator_preflight": preflight_status, + "mutation_raw_status": mutation["status"], + "mutation_effective_clear": mutation_effective_clear, + "mutation_manual_disposition": manual_mutation, + "static_raw_status": static_by_case[case_id]["status"], + "static_max_severity": static_by_case[case_id]["max_severity"], + "static_finding_codes": [ + finding["code"] for finding in static_by_case[case_id]["findings"] + ], + "reference_count_in_worktree": certification_row.get("reference_count", 0), + "unproven_reference_count": certification_row.get("unproven_count", 0), + **change, + } + ) + + prompt_cases = [row["case_id"] for row in rows if row["prompt_changed"]] + instance_cases = [row["case_id"] for row in rows if row["instance_changed"]] + summary = { + "directory_count": len(rows), + "eval_ready_directory_count": sum(row["eval_ready"] for row in rows), + "canonical_independent_case_count": sum(row["independent_benchmark"] for row in rows), + "legacy_alias_count": sum(not row["independent_benchmark"] for row in rows), + "legacy_aliases": [ + row["case_id"] for row in rows if not row["independent_benchmark"] + ], + "official_pair_count": replay["summary"]["paired_run_count"], + "official_outcome_counts": replay["summary"]["outcome_counts"], + "official_orphan_instance_count": replay["summary"]["orphan_instance_count"], + "stored_comparison_counts": replay["summary"]["stored_comparison_counts"], + "evaluator_preflight_counts": preflight["summary"]["status_counts"], + "mutation_raw_counts": mutations["summary"]["status_counts"], + "mutation_manual_clear_count": len(MANUAL_MUTATION_DISPOSITIONS), + "mutation_effective_clear_count": sum(row["mutation_effective_clear"] for row in rows), + "static_heuristic_counts": static["summary"]["status_counts"], + "reference_certification": certification["summary"], + "prompt_changed_case_count": len(prompt_cases), + "prompt_changed_cases": prompt_cases, + "instance_changed_case_count": len(instance_cases), + "instance_changed_cases": instance_cases, + "original_audited_92_count": sum(row["original_audited_92"] for row in rows), + "original_audited_92_prompt_changed_count": sum( + row["original_audited_92"] and row["prompt_changed"] for row in rows + ), + "original_audited_92_instance_changed_count": sum( + row["original_audited_92"] and row["instance_changed"] for row in rows + ), + "changed_file_count": len(all_changed), + "root_changed_files": root_changes, + "release_exclusion_note": ( + "Two legacy segundo2019 solution artifacts without matching instances " + "remain in the repair worktree for provenance and are excluded from the " + "materialized release." + ), + "completion_interpretation": ( + "All 180 directory IDs are evaluator-ready under their disclosed released " + "contracts. They represent 179 independent benchmark cases plus one " + "transparent legacy alias; feasible baselines are not all global-optimum proofs." + ), + } + if summary["directory_count"] != 180: + raise RuntimeError(f"expected 180 case directories, got {summary['directory_count']}") + if summary["eval_ready_directory_count"] != 180: + bad = [row["case_id"] for row in rows if not row["eval_ready"]] + raise RuntimeError(f"final eval-ready gate failed: {bad}") + if summary["official_outcome_counts"] != {"accepted": summary["official_pair_count"]}: + raise RuntimeError("official replay is not all accepted") + if summary["stored_comparison_counts"] != {"match": summary["official_pair_count"]}: + raise RuntimeError("stored feasibility results do not all match") + if summary["evaluator_preflight_counts"] != {"passed": 180}: + raise RuntimeError("evaluator preflight is not 180/180") + if summary["mutation_effective_clear_count"] != 180: + raise RuntimeError("mutation gate is not effectively clear for all cases") + + generated_at = datetime.now(ZoneInfo("America/New_York")).isoformat() + payload = { + "generated_at": generated_at, + "source_commit": git_lines(data_root, "rev-parse", "HEAD")[0], + "method": { + "eval_ready_gate": ( + "all official instance/solution pairs accepted; stored results match; " + "evaluator objective preflight passes; mutation audit is raw-clear or " + "has an explicit manually reviewed alternative-certificate disposition" + ), + "global_optimality_warning": ( + "Reference feasibility and objective binding do not imply every incumbent " + "has a global zero-gap certificate." + ), + }, + "summary": summary, + "manual_mutation_dispositions": MANUAL_MUTATION_DISPOSITIONS, + "cases": rows, + } + write_json(output_dir / "FINAL_AUDIT_180.json", payload) + + csv_fields = [ + "case_id", + "eval_ready", + "publication_status", + "identity_status", + "independent_benchmark", + "original_audited_92", + "official_pair_count", + "official_accepted_count", + "evaluator_preflight", + "mutation_raw_status", + "mutation_effective_clear", + "static_raw_status", + "static_max_severity", + "reference_count_in_worktree", + "unproven_reference_count", + "prompt_changed", + "formulation_changed", + "model_changed", + "checker_changed", + "instance_changed", + "solution_changed", + "stored_result_changed", + "solver_log_changed", + "changed_file_count", + "resolution_note", + ] + with (output_dir / "FINAL_CASE_MATRIX_180.csv").open( + "w", encoding="utf-8", newline="" + ) as handle: + writer = csv.DictWriter(handle, fieldnames=csv_fields) + writer.writeheader() + writer.writerows({field: row.get(field) for field in csv_fields} for row in rows) + + change_manifest = { + "source_commit": payload["source_commit"], + "changed_file_count": len(all_changed), + "changed_files": all_changed, + "root_changed_files": root_changes, + "case_changes": { + row["case_id"]: { + key: row[key] + for key in ( + "prompt_changed", + "formulation_changed", + "model_changed", + "checker_changed", + "instance_changed", + "solution_changed", + "stored_result_changed", + "solver_log_changed", + "changed_file_count", + "changed_files", + ) + } + for row in rows + }, + } + write_json(output_dir / "CHANGE_MANIFEST_180.json", change_manifest) + + report = f"""# FrontierOR 180 题最终审计结论 + +生成时间：`{generated_at}`（America/New_York） + +## 最终口径 + +- evaluator-ready 目录：**{summary['eval_ready_directory_count']}/180** +- 独立、规范 case：**{summary['canonical_independent_case_count']}** +- 透明兼容别名：**1**（`ostrowski2012` → `earl2005`） +- 官方 instance/solution 回放：**{summary['official_pair_count']}/{summary['official_pair_count']} accepted** +- 随附 `gurobi_feasi_result`：**{summary['official_pair_count']}/{summary['official_pair_count']} 与当前 checker 一致** +- evaluator objective preflight：**180/180 passed** +- mutation audit：原始结果 **{mutations['summary']['status_counts']}**；人工复核两种合法证书后 **180/180 effective clear** + +因此，如果“全部修好”指 **不会再因为 description / released model / checker / +reference solution 的契约错位而在当前 eval 中失败**，答案是 **180/180 已闭环**。 + +但 180 个目录并不等于 180 篇独立题目：`ostrowski2012` 上游本来就是 +`earl2005` 的错标签重复。本版保留目录兼容性，同时在 prompt、formulation、model、checker +和 `CASE_IDENTITY.json` 中明确声明它是别名。因此严格统计是 **179 个独立题目 + 1 个兼容别名**。 + +## 重要边界 + +“accepted / eval-ready”表示 reference 是完整、有限、可由 checker 验证的 feasible baseline； +它不表示每一份历史 large incumbent 都重新取得了全局零 gap 最优证明。当前认证盘点仍有 +**{certification['summary']['unproven_reference_count']}** 份 worktree reference 没有全局最优性证书。 + +原先 92 题子集在这一轮中 prompt 改动数为 +**{summary['original_audited_92_prompt_changed_count']}**，instance 改动数为 +**{summary['original_audited_92_instance_changed_count']}**。全部 prompt 改动 case 和逐题文件级 +变更见 `FINAL_AUDIT_180.json`、`FINAL_CASE_MATRIX_180.csv`、`CHANGE_MANIFEST_180.json`。 + +修复 worktree 中保留了 `segundo2019` 的两份无对应 instance 的历史 solution artifact 供追溯； +它们不属于 {summary['official_pair_count']} 个正式配对，也会从发布目录中排除。 +""" + (output_dir / "FINAL_AUDIT_REPORT_180.md").write_text(report, encoding="utf-8") + print(json.dumps(summary, indent=2, ensure_ascii=False)) + + +if __name__ == "__main__": + main() diff --git a/audit/solverless_checker_audit_bundle_20260905.zip b/audit/solverless_checker_audit_bundle_20260905.zip new file mode 100644 index 0000000000000000000000000000000000000000..977279ab5bf4af29422fac133f218f9a50b4a094 --- /dev/null +++ b/audit/solverless_checker_audit_bundle_20260905.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ea72cfcdb99f2e8dd6df7ab7d0599ce1916d8d62ba66263016cbd4e9a81ec430 +size 84025 diff --git a/bard2002/feasibility_check.py b/bard2002/feasibility_check.py new file mode 100644 index 0000000000000000000000000000000000000000..981b8b4188bd9447904f985276c1502bf01f8e5f --- /dev/null +++ b/bard2002/feasibility_check.py @@ -0,0 +1,673 @@ +""" +Feasibility checker for the VRPTW formulation from: +Bard, Kontoravdis, and Yu (2002), +"A Branch-and-Cut Procedure for the Vehicle Routing Problem with Time Windows", +Transportation Science, 36(2), 250-269. + +Checks all hard constraints (2)-(8) from the mathematical formulation, +plus constraint (9): objective consistency (Tier C anti-gaming defense). +""" + +import argparse +import json +import math + +# FRONTIEROR_RELEASE_CONTRACT_V2 +_FRONTIEROR_REQUIRED_SOLUTION_FIELDS = ('departure_times', 'loads', 'num_vehicles', 'objective_value', 'routes', 'total_distance') +_FRONTIEROR_NONEMPTY_SOLUTION_FIELDS = ('departure_times', 'loads', 'num_vehicles', 'total_distance') +_FRONTIEROR_DERIVED_FIELD_RULES = () +_FRONTIEROR_CONDITIONAL_FIELD_RULES = () + + +def _frontieror_validate_solution_contract(solution): + """Return a structured rejection when the public solution contract is absent.""" + violations = [] + if not isinstance(solution, dict): + violations.append("solution must be a JSON object") + else: + missing = [ + field + for field in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS + if field not in solution or solution[field] is None + ] + if missing: + violations.append( + "missing required solution field(s): " + ", ".join(missing) + ) + + for discriminator, expected_value, fields in _FRONTIEROR_CONDITIONAL_FIELD_RULES: + if solution.get(discriminator) != expected_value: + continue + conditional_missing = [ + field + for field in fields + if field not in solution or solution[field] is None + ] + if conditional_missing: + violations.append( + "missing conditionally required solution field(s): " + + ", ".join(conditional_missing) + ) + + empty = [ + field + for field in _FRONTIEROR_NONEMPTY_SOLUTION_FIELDS + if field in solution and solution[field] in (None, [], {}) + ] + if empty: + violations.append( + "empty required decision witness field(s): " + ", ".join(empty) + ) + + for target, operation, source, source_key in _FRONTIEROR_DERIVED_FIELD_RULES: + try: + if operation == "assignment_key_set": + reported_set = set(solution[target]) + expected_set = {entry[source_key] for entry in solution[source]} + if reported_set != expected_set: + violations.append( + f"{target}={sorted(reported_set)} does not match the " + f"decision-derived value {sorted(expected_set)}" + ) + continue + + reported = float(solution[target]) + if operation == "length": + expected = float(len(solution[source])) + elif operation == "mapping_value": + expected = float(solution[source][source_key]) + else: + raise ValueError("unknown derived-field operation") + except (KeyError, TypeError, ValueError, OverflowError): + violations.append( + f"cannot derive {target} from decision witness field {source}" + ) + continue + if ( + reported != reported + or expected != expected + or reported in (float("inf"), float("-inf")) + or expected in (float("inf"), float("-inf")) + or abs(reported - expected) > 1e-6 + ): + violations.append( + f"{target}={reported} does not match the decision-derived " + f"value {expected}" + ) + + if "objective_value" in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS: + objective = solution.get("objective_value") + if ( + isinstance(objective, bool) + or not isinstance(objective, (int, float)) + or objective != objective + or objective in (float("inf"), float("-inf")) + ): + violations.append("objective_value must be a finite JSON number") + + if not violations: + return None + return { + "feasible": False, + "violated_constraints": ["solution_schema"], + "violations": violations, + "violation_magnitudes": [], + } + + +def _frontieror_write_unexpected_rejection(error): + """Turn malformed candidate/checker exceptions into a normal rejection.""" + import json as _frontieror_json + import sys as _frontieror_sys + + result_path = None + for index, argument in enumerate(_frontieror_sys.argv): + if argument == "--result_path" and index + 1 < len(_frontieror_sys.argv): + result_path = _frontieror_sys.argv[index + 1] + break + if argument.startswith("--result_path="): + result_path = argument.split("=", 1)[1] + break + if not result_path: + return False + try: + with open(result_path, "w") as result_handle: + _frontieror_json.dump( + { + "feasible": False, + "violated_constraints": ["malformed_solution"], + "violations": [ + "checker rejected malformed candidate: " + + type(error).__name__ + + ": " + + str(error)[:500] + ], + "violation_magnitudes": [], + }, + result_handle, + indent=2, + ) + except Exception: + return False + return True + + + +def load_json(path): + with open(path, "r") as f: + return json.load(f) + + +def build_distance_matrix(data): + """Build or load distance matrix from instance data.""" + depot = data["depot"] + customers = data["customers"] + n = len(customers) + num = n + 1 + nodes = [depot] + customers + + if "distance_matrix" in data and data["distance_matrix"]: + return data["distance_matrix"] + + x_c = [nodes[i]["x"] for i in range(num)] + y_c = [nodes[i]["y"] for i in range(num)] + raw_dist = [[0.0] * num for _ in range(num)] + for i in range(num): + for j in range(num): + if i != j: + d = math.sqrt((x_c[i] - x_c[j]) ** 2 + (y_c[i] - y_c[j]) ** 2) + raw_dist[i][j] = math.floor(d * 10) / 10.0 + return raw_dist + + +def build_tau(data, raw_dist): + """Build travel time matrix with service time folded in, per paper preprocessing.""" + depot = data["depot"] + customers = data["customers"] + n = len(customers) + num = n + 1 + nodes = [depot] + customers + + ready = [nodes[i]["ready_time"] for i in range(num)] + due = [nodes[i]["due_date"] for i in range(num)] + service = [nodes[i]["service_time"] for i in range(num)] + + fold = data.get("travel_time_includes_service_time", False) + + tau = [[0.0] * num for _ in range(num)] + for i in range(num): + for j in range(num): + if i == j: + continue + t = raw_dist[i][j] + if not fold: + t += service[i] + if (ready[j] - due[i]) > t: + t = ready[j] - due[i] + tau[i][j] = t + return tau + + +def extract_x_from_routes(routes, num): + """Extract binary x_{ij} variables from routes.""" + x = {} + for route in routes: + for k in range(len(route) - 1): + i, j = route[k], route[k + 1] + x[i, j] = 1 + return x + + +def compute_departure_times_and_loads(routes, tau, ready, demand): + """ + Compute departure times (t_i) and loads (y_i) from routes. + t_i = departure time from customer i (max of arrival, ready_time). + y_i = accumulated load at departure from customer i. + """ + t = {} + y = {} + for route in routes: + current_time = 0.0 + current_load = 0 + for k in range(len(route) - 1): + i = route[k] + j = route[k + 1] + arr = current_time + tau[i][j] + if j != 0: + dep = max(arr, ready[j]) + current_time = dep + current_load += demand[j] + t[j] = dep + y[j] = current_load + return t, y + + +def check_feasibility(data, solution): + """ + Check all hard constraints (2)-(8) from the VRPTW formulation, + plus constraint (9) objective-consistency check. + Returns feasibility result dictionary. + """ + tol = 1e-5 + eps = 1e-5 + + depot = data["depot"] + customers = data["customers"] + n = len(customers) + num = n + 1 # depot + customers + nodes = [depot] + customers + + Q = data["vehicle_capacity"] + demand = [nodes[i]["demand"] for i in range(num)] + ready = [nodes[i]["ready_time"] for i in range(num)] + due = [nodes[i]["due_date"] for i in range(num)] + + raw_dist = build_distance_matrix(data) + tau = build_tau(data, raw_dist) + + routes = solution.get("routes", []) + x = extract_x_from_routes(routes, num) + + # Use provided departure_times and loads if available, otherwise compute + if "departure_times" in solution and solution["departure_times"]: + t = {int(k): v for k, v in solution["departure_times"].items()} + else: + t, _ = compute_departure_times_and_loads(routes, tau, ready, demand) + + if "loads" in solution and solution["loads"]: + y = {int(k): v for k, v in solution["loads"].items()} + else: + _, y = compute_departure_times_and_loads(routes, tau, ready, demand) + + I = list(range(1, num)) # customer indices + I0 = list(range(num)) # depot + customer indices + + violated_constraints = set() + violations = [] + violation_magnitudes = [] + + # ========================================================================= + # Constraint (2): Each customer served exactly once + # sum_{j in I_0} x_{ij} = 1, for all i in I + # ========================================================================= + for i in I: + lhs_val = sum(x.get((i, j), 0) for j in I0 if j != i) + rhs_val = 1.0 + violation_amount = abs(lhs_val - rhs_val) + if violation_amount > tol: + violated_constraints.add(2) + violations.append( + f"Constraint (2): Customer {i} is visited {int(lhs_val)} time(s) " + f"instead of exactly 1" + ) + normalizer = max(abs(rhs_val), eps) + violation_magnitudes.append({ + "constraint": 2, + "lhs": lhs_val, + "rhs": rhs_val, + "raw_excess": violation_amount, + "normalizer": normalizer, + "ratio": violation_amount / normalizer, + }) + + # ========================================================================= + # Constraint (3): Flow conservation + # sum_{j in I_0} x_{ij} - sum_{j in I_0} x_{ji} = 0, for all i in I_0 + # ========================================================================= + for i in I0: + out_flow = sum(x.get((i, j), 0) for j in I0 if j != i) + in_flow = sum(x.get((j, i), 0) for j in I0 if j != i) + lhs_val = out_flow - in_flow + rhs_val = 0.0 + violation_amount = abs(lhs_val - rhs_val) + if violation_amount > tol: + violated_constraints.add(3) + node_label = f"depot (node 0)" if i == 0 else f"customer {i}" + violations.append( + f"Constraint (3): Flow conservation violated at {node_label}: " + f"out_flow={out_flow}, in_flow={in_flow}, difference={lhs_val}" + ) + normalizer = max(abs(rhs_val), eps) + violation_magnitudes.append({ + "constraint": 3, + "lhs": lhs_val, + "rhs": rhs_val, + "raw_excess": violation_amount, + "normalizer": normalizer, + "ratio": violation_amount / normalizer, + }) + + # ========================================================================= + # Constraint (4): Time linking + # t_j >= t_i + tau_{ij} * x_{ij} - T_{ij} * (1 - x_{ij}), for i,j in I + # where T_{ij} = b_i - a_j + # Only active (binding) when x_{ij} = 1, so we check: t_j >= t_i + tau_{ij} + # ========================================================================= + for i in I: + for j in I: + if i == j: + continue + x_ij = x.get((i, j), 0) + T_ij = due[i] - ready[j] + rhs_val = t.get(i, 0.0) + tau[i][j] * x_ij - T_ij * (1 - x_ij) + lhs_val = t.get(j, 0.0) + # This is a >= constraint: lhs >= rhs, so violation = rhs - lhs + violation_amount = rhs_val - lhs_val + if violation_amount > tol: + violated_constraints.add(4) + violations.append( + f"Constraint (4): Time linking violated for arc ({i},{j}): " + f"t_{j}={lhs_val:.2f} < t_{i} + tau*x - T*(1-x) = {rhs_val:.2f}" + ) + normalizer = max(abs(rhs_val), eps) + violation_magnitudes.append({ + "constraint": 4, + "lhs": lhs_val, + "rhs": rhs_val, + "raw_excess": violation_amount, + "normalizer": normalizer, + "ratio": violation_amount / normalizer, + }) + + # ========================================================================= + # Constraint (5): Load linking + # y_j >= y_i + q_j - Q_j * (1 - x_{ij}), for i,j in I + # where Q_j = Q - q_j + # ========================================================================= + for i in I: + for j in I: + if i == j: + continue + x_ij = x.get((i, j), 0) + Q_j = Q - demand[j] + rhs_val = y.get(i, 0.0) + demand[j] - Q_j * (1 - x_ij) + lhs_val = y.get(j, 0.0) + # >= constraint: violation = rhs - lhs + violation_amount = rhs_val - lhs_val + if violation_amount > tol: + violated_constraints.add(5) + violations.append( + f"Constraint (5): Load linking violated for arc ({i},{j}): " + f"y_{j}={lhs_val:.2f} < y_{i} + q_{j} - Q_{j}*(1-x) = {rhs_val:.2f}" + ) + normalizer = max(abs(rhs_val), eps) + violation_magnitudes.append({ + "constraint": 5, + "lhs": lhs_val, + "rhs": rhs_val, + "raw_excess": violation_amount, + "normalizer": normalizer, + "ratio": violation_amount / normalizer, + }) + + # ========================================================================= + # Constraint (6): Vehicle capacity + # q_i <= y_i <= Q, for all i in I + # Two parts: (a) y_i >= q_i and (b) y_i <= Q + # ========================================================================= + for i in I: + y_i = y.get(i, 0.0) + # (a) y_i >= q_i + lhs_val_a = y_i + rhs_val_a = float(demand[i]) + violation_amount_a = rhs_val_a - lhs_val_a # >= constraint + if violation_amount_a > tol: + violated_constraints.add(6) + violations.append( + f"Constraint (6): Load at customer {i} too low: " + f"y_{i}={lhs_val_a:.2f} < q_{i}={rhs_val_a:.2f}" + ) + normalizer = max(abs(rhs_val_a), eps) + violation_magnitudes.append({ + "constraint": 6, + "lhs": lhs_val_a, + "rhs": rhs_val_a, + "raw_excess": violation_amount_a, + "normalizer": normalizer, + "ratio": violation_amount_a / normalizer, + }) + + # (b) y_i <= Q + lhs_val_b = y_i + rhs_val_b = float(Q) + violation_amount_b = lhs_val_b - rhs_val_b # <= constraint + if violation_amount_b > tol: + violated_constraints.add(6) + violations.append( + f"Constraint (6): Load at customer {i} exceeds capacity: " + f"y_{i}={lhs_val_b:.2f} > Q={rhs_val_b:.2f}" + ) + normalizer = max(abs(rhs_val_b), eps) + violation_magnitudes.append({ + "constraint": 6, + "lhs": lhs_val_b, + "rhs": rhs_val_b, + "raw_excess": violation_amount_b, + "normalizer": normalizer, + "ratio": violation_amount_b / normalizer, + }) + + # ========================================================================= + # Constraint (7): Time window + # a_i <= t_i <= b_i, for all i in I + # Two parts: (a) t_i >= a_i and (b) t_i <= b_i + # ========================================================================= + for i in I: + t_i = t.get(i, 0.0) + # (a) t_i >= a_i + lhs_val_a = t_i + rhs_val_a = float(ready[i]) + violation_amount_a = rhs_val_a - lhs_val_a # >= constraint + if violation_amount_a > tol: + violated_constraints.add(7) + violations.append( + f"Constraint (7): Departure time at customer {i} too early: " + f"t_{i}={lhs_val_a:.2f} < a_{i}={rhs_val_a:.2f}" + ) + normalizer = max(abs(rhs_val_a), eps) + violation_magnitudes.append({ + "constraint": 7, + "lhs": lhs_val_a, + "rhs": rhs_val_a, + "raw_excess": violation_amount_a, + "normalizer": normalizer, + "ratio": violation_amount_a / normalizer, + }) + + # (b) t_i <= b_i + lhs_val_b = t_i + rhs_val_b = float(due[i]) + violation_amount_b = lhs_val_b - rhs_val_b # <= constraint + if violation_amount_b > tol: + violated_constraints.add(7) + violations.append( + f"Constraint (7): Departure time at customer {i} too late: " + f"t_{i}={lhs_val_b:.2f} > b_{i}={rhs_val_b:.2f}" + ) + normalizer = max(abs(rhs_val_b), eps) + violation_magnitudes.append({ + "constraint": 7, + "lhs": lhs_val_b, + "rhs": rhs_val_b, + "raw_excess": violation_amount_b, + "normalizer": normalizer, + "ratio": violation_amount_b / normalizer, + }) + + # ========================================================================= + # Constraint (8): Binary constraint + # x_{ij} in {0, 1}, for all i,j in I_0 + # ========================================================================= + for i in I0: + for j in I0: + if i == j: + continue + x_ij = x.get((i, j), 0) + if x_ij not in (0, 1): + violation_amount = min(abs(x_ij - 0), abs(x_ij - 1)) + if violation_amount > tol: + violated_constraints.add(8) + violations.append( + f"Constraint (8): x_{{{i},{j}}} = {x_ij} is not binary" + ) + normalizer = max(1.0, eps) + violation_magnitudes.append({ + "constraint": 8, + "lhs": float(x_ij), + "rhs": round(x_ij), + "raw_excess": violation_amount, + "normalizer": normalizer, + "ratio": violation_amount / normalizer, + }) + + # ========================================================================= + # Constraint (9): Objective-consistency (Tier C anti-gaming) + # Recompute the VRPTW objective from the solution routes and compare to + # the reported objective_value. The math model defines the objective as + # min sum_{i=1..n} x_{0i} = number of vehicles dispatched from depot + # which equals the number of (0, j) arcs with j in I, i.e., the count + # of routes that actually leave the depot to visit a customer. + # Since the objective is an integer count, use tol=0.5 (any integer + # mismatch by >= 1 fires). + # ========================================================================= + reported_obj = solution.get("objective_value") + if reported_obj is not None: + try: + reported = float(reported_obj) + except (TypeError, ValueError): + reported = None + if reported is not None: + true_obj = float(sum(x.get((0, j), 0) for j in I)) + abs_diff = abs(reported - true_obj) + obj_tol = 0.5 + if abs_diff > obj_tol: + violated_constraints.add(9) + violations.append( + f"Constraint (9): Objective consistency violated: " + f"reported objective_value={reported} differs from " + f"recomputed sum_{{j in I}} x_{{0,j}}={true_obj} " + f"(|diff|={abs_diff:.3g}, tol={obj_tol})" + ) + normalizer = max(abs(true_obj), eps) + violation_magnitudes.append({ + "constraint": 9, + "lhs": reported, + "rhs": true_obj, + "raw_excess": abs_diff, + "normalizer": normalizer, + "ratio": abs_diff / normalizer, + }) + + # Bind serialized summary fields to the route witness. They are useful to + # consumers, but are not independent decisions and must not be accepted + # after being corrupted while the routes remain unchanged. + derived_num_vehicles = len(routes) + reported_num_vehicles = solution.get("num_vehicles") + if (isinstance(reported_num_vehicles, bool) + or not isinstance(reported_num_vehicles, (int, float)) + or abs(float(reported_num_vehicles) - derived_num_vehicles) > tol): + violated_constraints.add(9) + violations.append( + f"Constraint (9): num_vehicles={reported_num_vehicles!r} does not " + f"equal len(routes)={derived_num_vehicles}" + ) + violation_magnitudes.append({ + "constraint": 9, + "lhs": float(reported_num_vehicles) + if isinstance(reported_num_vehicles, (int, float)) + and not isinstance(reported_num_vehicles, bool) else 0.0, + "rhs": float(derived_num_vehicles), + "raw_excess": 1.0, + "normalizer": max(float(derived_num_vehicles), eps), + "ratio": 1.0 / max(float(derived_num_vehicles), eps), + }) + + derived_total_distance = round(sum( + float(raw_dist[route[pos]][route[pos + 1]]) + for route in routes + for pos in range(len(route) - 1) + ), 1) + reported_total_distance = solution.get("total_distance") + distance_ok = ( + not isinstance(reported_total_distance, bool) + and isinstance(reported_total_distance, (int, float)) + and abs(float(reported_total_distance) - derived_total_distance) <= 0.05 + ) + if not distance_ok: + violated_constraints.add(9) + violations.append( + f"Constraint (9): total_distance={reported_total_distance!r} does " + f"not equal the route-derived distance={derived_total_distance}" + ) + violation_magnitudes.append({ + "constraint": 9, + "lhs": float(reported_total_distance) + if isinstance(reported_total_distance, (int, float)) + and not isinstance(reported_total_distance, bool) else 0.0, + "rhs": float(derived_total_distance), + "raw_excess": abs(float(reported_total_distance) + - derived_total_distance) + if isinstance(reported_total_distance, (int, float)) + and not isinstance(reported_total_distance, bool) else 1.0, + "normalizer": max(abs(float(derived_total_distance)), eps), + "ratio": (abs(float(reported_total_distance) + - derived_total_distance) + / max(abs(float(derived_total_distance)), eps)) + if isinstance(reported_total_distance, (int, float)) + and not isinstance(reported_total_distance, bool) else 1.0, + }) + + # Build result + feasible = len(violated_constraints) == 0 + result = { + "feasible": feasible, + "violated_constraints": sorted(violated_constraints), + "violations": violations, + "violation_magnitudes": violation_magnitudes, + } + return result + + +def main(): + parser = argparse.ArgumentParser( + description="Feasibility checker for VRPTW (Bard et al. 2002)") + parser.add_argument("--instance_path", type=str, required=True, + help="Path to the JSON file containing the data instance") + parser.add_argument("--solution_path", type=str, required=True, + help="Path to the JSON file containing the candidate solution") + parser.add_argument("--result_path", type=str, required=True, + help="Path to write the JSON file containing the feasibility result") + args = parser.parse_args() + + data = load_json(args.instance_path) + solution = load_json(args.solution_path) + _frontieror_contract_result = _frontieror_validate_solution_contract(solution) + if _frontieror_contract_result is not None: + with open(args.result_path, "w") as _frontieror_result_handle: + json.dump( + _frontieror_contract_result, + _frontieror_result_handle, + indent=2, + ) + return + result = check_feasibility(data, solution) + + with open(args.result_path, "w") as f: + json.dump(result, f, indent=2) + + if result["feasible"]: + print(f"FEASIBLE - no constraints violated") + else: + print(f"INFEASIBLE - violated constraints: {result['violated_constraints']}") + for v in result["violations"]: + print(f" {v}") + + +if __name__ == "__main__": + try: + main() + except Exception as _frontieror_unexpected_error: + if not _frontieror_write_unexpected_rejection( + _frontieror_unexpected_error + ): + raise diff --git a/bard2002/gurobi_code.py b/bard2002/gurobi_code.py new file mode 100644 index 0000000000000000000000000000000000000000..a259c83160bf08ab49ce62e63fe23043ebb50fc8 --- /dev/null +++ b/bard2002/gurobi_code.py @@ -0,0 +1,351 @@ +""" +Gurobi implementation of the VRPTW formulation from: +Bard, Kontoravdis, and Yu (2002), +"A Branch-and-Cut Procedure for the Vehicle Routing Problem with Time Windows", +Transportation Science, 36(2), 250-269. + +The model minimizes the number of vehicles (primary objective). +""" + +import argparse +import json +import math +import gurobipy as gp +from gurobipy import GRB +import os as _os, sys as _sys +# Walk up from this file's directory to find repo root (containing scripts/). +_repo = _os.path.dirname(_os.path.abspath(__file__)) +while _repo != _os.path.dirname(_repo) and not _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _repo = _os.path.dirname(_repo) +if _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _sys.path.insert(0, _repo) +try: + from scripts.utils.gurobi_log_helper import install_gurobi_logger +except ImportError: + def install_gurobi_logger(log_path): # no-op fallback when scripts/ unavailable + pass + + +def load_instance(path): + """Load a VRPTW instance from JSON.""" + with open(path, "r") as f: + data = json.load(f) + return data + + +def build_and_solve(instance_path, solution_path, time_limit): + data = load_instance(instance_path) + + depot = data["depot"] + customers = data["customers"] + n = len(customers) + Q = data["vehicle_capacity"] + + # Build node list: 0 = depot, 1..n = customers (using their original ids) + # Map customer id -> index in our arrays (1-based) + # We assume customers are listed in order with ids 1..n + nodes = [depot] + customers # index 0 = depot, 1..n = customers + num_nodes = n + 1 # 0..n + + # Extract coordinates, demands, time windows, service times + x_coord = [nodes[i]["x"] for i in range(num_nodes)] + y_coord = [nodes[i]["y"] for i in range(num_nodes)] + demand = [nodes[i]["demand"] for i in range(num_nodes)] + ready = [nodes[i]["ready_time"] for i in range(num_nodes)] + due = [nodes[i]["due_date"] for i in range(num_nodes)] + service = [nodes[i]["service_time"] for i in range(num_nodes)] + + # ------------------------------------------------------------------------- + # Use the distance matrix from the instance if provided, otherwise compute + # ------------------------------------------------------------------------- + if "distance_matrix" in data and data["distance_matrix"]: + raw_dist = data["distance_matrix"] + else: + # Compute Euclidean distances truncated to 1 decimal + raw_dist = [[0.0] * num_nodes for _ in range(num_nodes)] + for i in range(num_nodes): + for j in range(num_nodes): + if i != j: + d = math.sqrt((x_coord[i] - x_coord[j]) ** 2 + + (y_coord[i] - y_coord[j]) ** 2) + raw_dist[i][j] = math.floor(d * 10) / 10.0 + + # ------------------------------------------------------------------------- + # Preprocessing: compute travel times tau[i][j] + # Paper convention: + # 1) tau[i][j] = distance[i][j] (velocity = 1) + # 2) Fold service time into travel time: tau[i][j] += sigma_i + # 3) If (a_j - b_i) > tau[i][j], set tau[i][j] = a_j - b_i + # ------------------------------------------------------------------------- + # NOTE: The instance JSON may already indicate travel_time_includes_service_time. + # We check and only fold service time if needed. + fold_service = data.get("travel_time_includes_service_time", False) + + tau = [[0.0] * num_nodes for _ in range(num_nodes)] + for i in range(num_nodes): + for j in range(num_nodes): + if i == j: + tau[i][j] = 0.0 + continue + t = raw_dist[i][j] + # Fold in service time of node i (departure node) + if not fold_service: + t += service[i] + # If (a_j - b_i) > t, replace t + if (ready[j] - due[i]) > t: + t = ready[j] - due[i] + tau[i][j] = t + + # NOTE: When travel_time_includes_service_time is True, the distance_matrix + # already includes service time. The paper says tau_{ij} <- tau_{ij} + sigma_i. + # If the instance says it's already included, we skip that step. + # However, looking at the instance data, the distance matrix appears to be + # raw Euclidean distances (not including service time). Let's re-check: + # The flag says True but the distances look like raw Euclidean. + # ASSUMPTION: We trust the flag. If distances seem raw, we still respect the flag. + + # ------------------------------------------------------------------------- + # Determine infeasible arcs + # Arc (i,j) is infeasible if: + # - Visiting i then j violates time windows: a_i + tau[i][j] > b_j + # (earliest departure from i + travel time exceeds latest arrival at j) + # - Or capacity: demand[i] + demand[j] > Q (for i,j both customers) + # ------------------------------------------------------------------------- + I = list(range(1, num_nodes)) # customer indices 1..n + I0 = list(range(num_nodes)) # all nodes 0..n + + feasible_arc = [[False] * num_nodes for _ in range(num_nodes)] + for i in I0: + for j in I0: + if i == j: + continue + # Check time feasibility + # Earliest departure from i: ready[i] (+ service time if not folded) + earliest_dep_i = ready[i] + if not fold_service and i > 0: + earliest_dep_i += service[i] + # Actually, with service time folded into tau, departure from i at + # time ready[i] means arrival at j at ready[i] + tau[i][j]. + # For feasibility: ready[i] + tau[i][j] <= due[j] + # But we also need: the arrival time at j >= ready[j] (wait is ok) + # The binding constraint is: ready[i] + tau[i][j] <= due[j] + if ready[i] + tau[i][j] > due[j]: + continue + # Check capacity feasibility (only for customer-customer arcs) + if i > 0 and j > 0: + if demand[i] + demand[j] > Q: + continue + feasible_arc[i][j] = True + + # ------------------------------------------------------------------------- + # Build Gurobi model + # ------------------------------------------------------------------------- + model = gp.Model("VRPTW") + model.setParam("Threads", 1) + model.setParam("TimeLimit", time_limit) + model.setParam("OutputFlag", 1) + + # Decision variables + # x[i][j]: binary, 1 if vehicle travels from i to j + x = {} + for i in I0: + for j in I0: + if i != j and feasible_arc[i][j]: + x[i, j] = model.addVar(vtype=GRB.BINARY, name=f"x_{i}_{j}") + + # t[i]: departure time from customer i (continuous) + t = {} + for i in I: + t[i] = model.addVar(lb=ready[i], ub=due[i], vtype=GRB.CONTINUOUS, + name=f"t_{i}") + + # y[i]: vehicle load at departure from customer i (continuous) + y = {} + for i in I: + y[i] = model.addVar(lb=demand[i], ub=Q, vtype=GRB.CONTINUOUS, + name=f"y_{i}") + + model.update() + + # ------------------------------------------------------------------------- + # Objective: minimize number of vehicles = sum of x[0][j] for j in I + # ------------------------------------------------------------------------- + model.setObjective( + gp.quicksum(x[0, j] for j in I if (0, j) in x), + GRB.MINIMIZE + ) + + # ------------------------------------------------------------------------- + # Constraint (2): Each customer served by exactly one vehicle + # sum_j x[i][j] = 1 for all i in I + # ------------------------------------------------------------------------- + for i in I: + model.addConstr( + gp.quicksum(x[i, j] for j in I0 if (i, j) in x) == 1, + name=f"serve_{i}" + ) + + # ------------------------------------------------------------------------- + # Constraint (3): Flow conservation + # sum_j x[i][j] - sum_j x[j][i] = 0 for all i in I_0 + # ------------------------------------------------------------------------- + for i in I0: + model.addConstr( + gp.quicksum(x[i, j] for j in I0 if (i, j) in x) - + gp.quicksum(x[j, i] for j in I0 if (j, i) in x) == 0, + name=f"flow_{i}" + ) + + # ------------------------------------------------------------------------- + # Constraint (4): Time linking constraints + # t[j] >= t[i] + tau[i][j] * x[i][j] - T[i][j] * (1 - x[i][j]) + # where T[i][j] = b_i - a_j + # For i, j in I (customers only) + # ------------------------------------------------------------------------- + for i in I: + for j in I: + if i != j and (i, j) in x: + T_ij = due[i] - ready[j] + model.addConstr( + t[j] >= t[i] + tau[i][j] * x[i, j] - T_ij * (1 - x[i, j]), + name=f"time_{i}_{j}" + ) + # NOTE: Constraint (4) with x_{ij}=0 reduces to t_j >= t_i - (b_i - a_j), + # which is always satisfied by variable bounds (t_i <= b_i, t_j >= a_j). + + # ------------------------------------------------------------------------- + # Constraint (5): Load linking constraints + # y[j] >= y[i] + q_j - Q_j * (1 - x[i][j]) + # where Q_j = Q - q_j + # For i, j in I (customers only) + # ------------------------------------------------------------------------- + for i in I: + for j in I: + if i != j: + Q_j = Q - demand[j] + if (i, j) in x: + model.addConstr( + y[j] >= y[i] + demand[j] - Q_j * (1 - x[i, j]), + name=f"load_{i}_{j}" + ) + else: + # Arc excluded (x_{ij}=0): constraint becomes y_j >= y_i + q_j - Q_j + model.addConstr( + y[j] >= y[i] + demand[j] - Q_j, + name=f"load_{i}_{j}" + ) + + # ------------------------------------------------------------------------- + # Constraints (6) and (7) are handled by variable bounds: + # q_i <= y_i <= Q (set as lb/ub on y[i]) + # a_i <= t_i <= b_i (set as lb/ub on t[i]) + # ------------------------------------------------------------------------- + + # ------------------------------------------------------------------------- + # Solve + # ------------------------------------------------------------------------- + model.optimize() + + # ------------------------------------------------------------------------- + # Extract solution + # ------------------------------------------------------------------------- + result = {} + if model.SolCount > 0: + obj_val = model.ObjVal + result["objective_value"] = obj_val + result["status"] = model.Status + result["status_description"] = { + GRB.OPTIMAL: "OPTIMAL", + GRB.TIME_LIMIT: "TIME_LIMIT", + GRB.SUBOPTIMAL: "SUBOPTIMAL", + }.get(model.Status, f"STATUS_{model.Status}") + + # Extract routes + routes = [] + # Find all arcs leaving the depot + depot_successors = [] + for j in I: + if (0, j) in x and x[0, j].X > 0.5: + depot_successors.append(j) + + for start in depot_successors: + route = [0, start] + current = start + while current != 0: + next_node = None + for j in I0: + if (current, j) in x and x[current, j].X > 0.5: + next_node = j + break + if next_node is None or next_node == 0: + route.append(0) + break + route.append(next_node) + current = next_node + routes.append(route) + + result["num_vehicles"] = len(routes) + result["routes"] = routes + + # Compute total distance + total_dist = 0.0 + for i_node in I0: + for j_node in I0: + if (i_node, j_node) in x and x[i_node, j_node].X > 0.5: + total_dist += raw_dist[i_node][j_node] + result["total_distance"] = round(total_dist, 1) + + # Extract departure times and loads + departure_times = {} + loads = {} + for i in I: + departure_times[str(i)] = round(t[i].X, 2) + loads[str(i)] = round(y[i].X, 2) + result["departure_times"] = departure_times + result["loads"] = loads + + else: + result["objective_value"] = None + result["status"] = model.Status + result["status_description"] = "NO_SOLUTION_FOUND" + result["num_vehicles"] = None + result["routes"] = [] + + # Add solver info + result["solver"] = "Gurobi" + result["time_limit"] = time_limit + if hasattr(model, "Runtime"): + result["runtime_seconds"] = round(model.Runtime, 2) + if hasattr(model, "MIPGap") and model.SolCount > 0: + result["mip_gap"] = round(model.MIPGap, 6) + + # Write solution + with open(solution_path, "w") as f: + json.dump(result, f, indent=2) + + print(f"Solution written to {solution_path}") + print(f"Objective value: {result['objective_value']}") + if result.get("num_vehicles") is not None: + print(f"Number of vehicles: {result['num_vehicles']}") + print(f"Total distance: {result.get('total_distance')}") + return result + + +def main(): + parser = argparse.ArgumentParser( + description="VRPTW Gurobi solver (Bard et al. 2002 formulation)") + parser.add_argument("--instance_path", type=str, required=True, + help="Path to the JSON instance file") + parser.add_argument("--solution_path", type=str, required=True, + help="Path for the output solution JSON file") + parser.add_argument("--time_limit", type=int, required=True, + help="Maximum solver runtime in seconds") + parser.add_argument("--log_path", type=str, default=None, help="Path to log incumbent solutions") + args = parser.parse_args() + install_gurobi_logger(args.log_path) + + build_and_solve(args.instance_path, args.solution_path, args.time_limit) + + +if __name__ == "__main__": + main() diff --git a/bard2002/gurobi_feasi_result/large_feasi_result_1.json b/bard2002/gurobi_feasi_result/large_feasi_result_1.json new file mode 100644 index 0000000000000000000000000000000000000000..e305b82c3d6732211d5033415df1a105476e9cf1 --- /dev/null +++ b/bard2002/gurobi_feasi_result/large_feasi_result_1.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3269392edcbb7720882e77caadb88b4a9cf2fda04cf482cebd23baace4a531fd +size 102 diff --git a/bard2002/gurobi_feasi_result/large_feasi_result_2.json b/bard2002/gurobi_feasi_result/large_feasi_result_2.json new file mode 100644 index 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b/bard2002/instance_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..81b11b3335b947a1f9711c9fa0c8841919e555de --- /dev/null +++ b/bard2002/instance_schema.json @@ -0,0 +1,29 @@ +{ + "num_customers": " Number of customers to be visited.", + "vehicle_capacity": " Maximum total demand that a single vehicle can carry on one route.", + "depot": { + "id": " Identifier for the depot node.", + "x": " Horizontal coordinate of the depot on the Euclidean grid.", + "y": " Vertical coordinate of the depot on the Euclidean grid.", + "demand": " Goods to be collected at the depot (always zero).", + "ready_time": " Earliest time a vehicle may depart from the depot.", + "due_date": " Latest time by which all vehicles must return to the depot.", + "service_time": " Time spent servicing at the depot (always zero)." + }, + "customers": [ + { + "id": " Unique identifier for the customer, numbered 1 through num_customers.", + "x": " Horizontal coordinate of the customer on the Euclidean grid.", + "y": " Vertical coordinate of the customer on the Euclidean grid.", + "demand": " Quantity of goods to be collected from this customer.", + "ready_time": " Earliest time at which a vehicle may depart from this customer.", + "due_date": " Latest time by which a vehicle must depart from this customer.", + "service_time": " Time a vehicle spends serving this customer before it can depart." + } + ], + "distance_matrix": " Euclidean distance between each pair of locations, where row/column 0 is the depot and rows/columns 1 through num_customers correspond to customers.", + "scheduling_horizon": " End of the planning period within which all routes must be completed.", + "travel_time_includes_service_time": " Whether the distance matrix already incorporates each customer's service time into outbound travel times.", + "primary_objective": " The first-priority goal for the optimization.", + "secondary_objective": " The second-priority goal for the optimization, applied as a tiebreaker." +} \ No newline at end of file diff --git a/bard2002/mathematical_formulation.md b/bard2002/mathematical_formulation.md new file mode 100644 index 0000000000000000000000000000000000000000..9292c4e56161fa9044cfba70a48a0f385a2350d9 --- /dev/null +++ b/bard2002/mathematical_formulation.md @@ -0,0 +1,41 @@ +# Original Formulation: Vehicle Routing Problem with Time Windows (VRPTW) + +*Source: “A Branch-and-Cut Procedure for the Vehicle Routing Problem with Time Windows,” Bard, Kontoravdis, and Yu, Transportation Science 36(2):250–269, 2002.* + +## Sets and Parameters + +- $I = \{1,\ldots,n\}$: set of customers; $I_0 = I \cup \{0\}$ includes the depot $0$. + +- $G_0 = (V, E)$: connected graph with $V = I_0$. + +- $q_i > 0$, $\sigma_i \ge 0$, $[a_i, b_i]$: demand, service time, and time window for customer $i \in I$. + +- $Q$: vehicle capacity; $\tau_{ij}$: travel time between nodes $i,j \in I_0$ (service time $\sigma_i$ folded in; if $a_j - b_i > \tau_{ij}$, replace $\tau_{ij}$ by $a_j - b_i$). + +- Derived: $T_{ij} = b_i - a_j$; $Q_j = Q - q_j$. + +## Decision Variables + +- $x_{ij} \in \{0,1\}$ for $i,j \in I_0$: $1$ if a vehicle traverses arc $(i,j)$. + +- $t_i \in \mathbb{R}$ for $i \in I$: departure time from customer $i$. + +- $y_i \in \mathbb{R}$ for $i \in I$: accumulated vehicle load at departure from customer $i$. + +## Objective + +$$\begin{equation} +(\text{VRPTW}) \quad \min \sum_{i=1}^{n} x_{0i} \tag{1} +\end{equation}$$ + +## Constraints + +$$\begin{align} +\sum_{j \in I_0} x_{ij} &= 1, & i \in I \tag{2} \\ +\sum_{j \in I_0} x_{ij} - \sum_{j \in I_0} x_{ji} &= 0, & i \in I_0 \tag{3} \\ +t_j &\ge t_i + \tau_{ij} x_{ij} - T_{ij}(1 - x_{ij}), & i,j \in I \tag{4} \\ +y_j &\ge y_i + q_j - Q_j(1 - x_{ij}), & i,j \in I \tag{5} \\ +q_i \le y_i &\le Q, & i \in I \tag{6} \\ +a_i \le t_i &\le b_i, & i \in I \tag{7} \\ +x_{ij} &\in \{0,1\}, & i,j \in I_0 \tag{8} +\end{align}$$ diff --git a/bard2002/problem_description.txt b/bard2002/problem_description.txt new file mode 100644 index 0000000000000000000000000000000000000000..b5afc37421cd99ccdeae6049010b3b29a7dea8aa --- /dev/null +++ b/bard2002/problem_description.txt @@ -0,0 +1,7 @@ +# Problem Description + +A fleet of identical vehicles, all based at a single depot, must pick up goods from a set of customers. Each customer has a known positive demand, a known service time, and a time window defined by an earliest ready time and a latest due date. The depot has coordinates, a scheduling horizon, and zero demand and service time. Every pair of locations (including the depot) has a known travel time, which equals the Euclidean distance between them truncated to one decimal digit; vehicle speed is one, so travel time and distance are interchangeable. Service time at each customer is folded into the outbound travel times from that customer: the effective travel time from any customer to any other location equals the base travel time plus the service time at the origin customer. Furthermore, if the difference between the destination's ready time and the origin customer's due date exceeds this adjusted travel time, the effective travel time is replaced by that difference. The resulting travel times satisfy the triangle inequality. + +The goal is to determine a set of routes, each starting and ending at the depot, that collectively visit every customer exactly once, while using the fewest vehicles possible. Each route is a sequence of customers served by a single vehicle. The accumulated load picked up along any route must never exceed the vehicle capacity. A vehicle departing from a customer must do so no earlier than that customer's ready time and no later than that customer's due date. When a vehicle travels directly from one customer to another, its departure time at the second customer must be at least as large as its departure time at the first customer plus the effective travel time between them. The accumulated load aboard a vehicle when it departs a customer must be at least as large as that customer's demand and must also be at least as large as the accumulated load at the preceding customer on the route plus the current customer's demand. Arcs between pairs of customers that are infeasible due to time window or capacity incompatibility are excluded in advance. + +The primary quantity to be minimized is the number of vehicles dispatched from the depot, which equals the number of routes used. diff --git a/bard2002/solution_logger.py b/bard2002/solution_logger.py new file mode 100644 index 0000000000000000000000000000000000000000..5393fa935ce71df9da95cee04fe5199077cb8461 --- /dev/null +++ b/bard2002/solution_logger.py @@ -0,0 +1,61 @@ +""" +Convergence logger for optimization algorithms. + +Records incumbent solutions with timestamps to a JSONL file. +This module is provided to LLM-generated programs — they only need to call +`log(objective_value)` whenever a better feasible solution is found. + +Usage in generated code: + from solution_logger import SolutionLogger + logger = SolutionLogger(log_path, sense="minimize") # or "maximize" + # ... inside algorithm loop: + logger.log(objective_value) +""" + +import json +import time + + +class SolutionLogger: + def __init__(self, log_path, sense="minimize"): + """ + Args: + log_path: Path to the JSONL output file. + sense: "minimize" or "maximize". + """ + self.log_path = log_path + self.sense = sense + self.start_time = time.time() + self.best_obj = None + self.min_interval = 0.1 # seconds, avoid excessive writes + + self._last_log_time = 0.0 + # Clear the file + with open(self.log_path, "w") as f: + pass + + def log(self, objective_value): + """Record a new incumbent if it improves on the best known.""" + if objective_value is None: + return + + # Check if this is an improvement + if self.best_obj is not None: + if self.sense == "minimize" and objective_value >= self.best_obj: + return + if self.sense == "maximize" and objective_value <= self.best_obj: + return + + elapsed = time.time() - self.start_time + + # Throttle writes + if self.best_obj is not None and elapsed - self._last_log_time < self.min_interval: + self.best_obj = objective_value + return + + self.best_obj = objective_value + self._last_log_time = elapsed + + with open(self.log_path, "a") as f: + f.write(json.dumps({"time": round(elapsed, 3), + "objective_value": objective_value}) + "\n") diff --git a/bard2002/solution_schema.json b/bard2002/solution_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..e50bc083cbf3e7b6bb6847c7be857113b517501f --- /dev/null +++ b/bard2002/solution_schema.json @@ -0,0 +1,8 @@ +{ + "objective_value": " Number of vehicles used, which is the primary quantity being minimized.", + "num_vehicles": " Number of vehicle routes dispatched from the depot.", + "routes": " Ordered sequence of node identifiers visited on each route, starting and ending at the depot (node 0).", + "total_distance": " Sum of Euclidean distances traveled across all routes.", + "departure_times": " Time at which the vehicle departs from each customer.", + "loads": " Cumulative demand aboard the vehicle when it departs from each customer." +} \ No newline at end of file diff --git a/barnhart2000/feasibility_check.py b/barnhart2000/feasibility_check.py new file mode 100644 index 0000000000000000000000000000000000000000..300d9deb4d0b23f0c80e8a9aa6e8e124cdf6c990 --- /dev/null +++ b/barnhart2000/feasibility_check.py @@ -0,0 +1,567 @@ +""" +Feasibility Checker for the Origin-Destination Integer Multicommodity Flow (ODIMCF) Problem. + +Checks candidate solutions against the mathematical formulation from: +Barnhart, Hane, and Vance (2000), Operations Research 48(2), 318-326. + +Hard constraints checked (Formulation 1 — Node-Arc): + Constraint (2): Capacity constraints on arcs + Constraint (3): Flow conservation at each node for each commodity + Constraint (4): Binary (integrality) domain for arc-flow variables + Constraint (5): Objective consistency — reported objective_value must equal the + value recomputed from the routing decisions present in the + solution: sum over commodities of either (artificial_arc_cost * + demand) when rejected, else sum_{ij in path_arcs}(cost[ij] * demand). +""" + +import argparse +import json +from collections import defaultdict + +# FRONTIEROR_RELEASE_CONTRACT_V2 +_FRONTIEROR_REQUIRED_SOLUTION_FIELDS = ('commodities', 'objective_value') +_FRONTIEROR_NONEMPTY_SOLUTION_FIELDS = () +_FRONTIEROR_DERIVED_FIELD_RULES = () +_FRONTIEROR_CONDITIONAL_FIELD_RULES = () + + +def _frontieror_validate_solution_contract(solution): + """Return a structured rejection when the public solution contract is absent.""" + violations = [] + if not isinstance(solution, dict): + violations.append("solution must be a JSON object") + else: + missing = [ + field + for field in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS + if field not in solution or solution[field] is None + ] + if missing: + violations.append( + "missing required solution field(s): " + ", ".join(missing) + ) + + for discriminator, expected_value, fields in _FRONTIEROR_CONDITIONAL_FIELD_RULES: + if solution.get(discriminator) != expected_value: + continue + conditional_missing = [ + field + for field in fields + if field not in solution or solution[field] is None + ] + if conditional_missing: + violations.append( + "missing conditionally required solution field(s): " + + ", ".join(conditional_missing) + ) + + empty = [ + field + for field in _FRONTIEROR_NONEMPTY_SOLUTION_FIELDS + if field in solution and solution[field] in (None, [], {}) + ] + if empty: + violations.append( + "empty required decision witness field(s): " + ", ".join(empty) + ) + + for target, operation, source, source_key in _FRONTIEROR_DERIVED_FIELD_RULES: + try: + reported = float(solution[target]) + if operation == "length": + expected = float(len(solution[source])) + elif operation == "mapping_value": + expected = float(solution[source][source_key]) + else: + raise ValueError("unknown derived-field operation") + except (KeyError, TypeError, ValueError, OverflowError): + violations.append( + f"cannot derive {target} from decision witness field {source}" + ) + continue + if ( + reported != reported + or expected != expected + or reported in (float("inf"), float("-inf")) + or expected in (float("inf"), float("-inf")) + or abs(reported - expected) > 1e-6 + ): + violations.append( + f"{target}={reported} does not match the decision-derived " + f"value {expected}" + ) + + if "objective_value" in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS: + objective = solution.get("objective_value") + if ( + isinstance(objective, bool) + or not isinstance(objective, (int, float)) + or objective != objective + or objective in (float("inf"), float("-inf")) + ): + violations.append("objective_value must be a finite JSON number") + + if not violations: + return None + return { + "feasible": False, + "violated_constraints": ["solution_schema"], + "violations": violations, + "violation_magnitudes": [], + } + + +def _frontieror_write_unexpected_rejection(error): + """Turn malformed candidate/checker exceptions into a normal rejection.""" + import json as _frontieror_json + import sys as _frontieror_sys + + result_path = None + for index, argument in enumerate(_frontieror_sys.argv): + if argument == "--result_path" and index + 1 < len(_frontieror_sys.argv): + result_path = _frontieror_sys.argv[index + 1] + break + if argument.startswith("--result_path="): + result_path = argument.split("=", 1)[1] + break + if not result_path: + return False + try: + with open(result_path, "w") as result_handle: + _frontieror_json.dump( + { + "feasible": False, + "violated_constraints": ["malformed_solution"], + "violations": [ + "checker rejected malformed candidate: " + + type(error).__name__ + + ": " + + str(error)[:500] + ], + "violation_magnitudes": [], + }, + result_handle, + indent=2, + ) + except Exception: + return False + return True + + + +def check_feasibility(instance_data, solution_data): + """ + Check all hard constraints of the ODIMCF formulation. + + Returns a dict with feasibility results. + """ + tol = 1e-5 + eps = 1e-5 + + violations = [] + violation_magnitudes = [] + violated_constraints_set = set() + + # ---- Parse instance ---- + nodes = instance_data["network"]["nodes"] + num_nodes = instance_data["network"]["num_nodes"] + arcs = instance_data["network"]["arcs"] + num_arcs = instance_data["network"]["num_arcs"] + commodities = instance_data["commodities"]["commodity_list"] + num_commodities = instance_data["commodities"]["num_commodities"] + + # Build arc lookup: arc_id -> arc info + arc_by_id = {} + for arc in arcs: + arc_by_id[arc["arc_id"]] = arc + + # Build adjacency info for arc validation + arc_set = set() + for arc in arcs: + arc_set.add((arc["from_node"], arc["to_node"], arc["arc_id"])) + + # ---- Parse solution ---- + sol_commodities = solution_data.get("commodities", []) + + # Handle infeasible/empty solutions + if not sol_commodities: + # No commodities in solution — check if this is a "no solution" marker + status = solution_data.get("status", "") + if status == "no_feasible_solution": + return { + "feasible": False, + "violated_constraints": [], + "violations": ["Solution status is 'no_feasible_solution': no candidate to check."], + "violation_magnitudes": [] + } + # Empty commodities but not explicitly marked — treat as missing all assignments + # This violates constraint (3) for every commodity + for comm in commodities: + k = comm["commodity_id"] + origin = comm["origin"] + violated_constraints_set.add(3) + violations.append( + f"Commodity {k}: no assignment found in solution (flow conservation violated at origin {origin})" + ) + rhs_val = 1.0 + normalizer = max(abs(rhs_val), eps) + violation_magnitudes.append({ + "constraint": 3, + "lhs": 0.0, + "rhs": rhs_val, + "raw_excess": abs(rhs_val), + "normalizer": normalizer, + "ratio": abs(rhs_val) / normalizer + }) + return { + "feasible": False, + "violated_constraints": sorted(violated_constraints_set), + "violations": violations, + "violation_magnitudes": violation_magnitudes + } + + # Build commodity lookup from solution + sol_comm_by_id = {} + for sc in sol_commodities: + sol_comm_by_id[sc["commodity_id"]] = sc + + # ---- Reconstruct arc-flow variables x^k_{ij} ---- + # x^k_{ij} = 1 if commodity k uses arc ij (not rejected), 0 otherwise + # For the path-based solution, commodity k uses the arcs in its path_arcs list + x = defaultdict(lambda: defaultdict(float)) # x[k][arc_id] = 0 or 1 + + for comm in commodities: + k = comm["commodity_id"] + sc = sol_comm_by_id.get(k) + if sc is None: + continue + if sc.get("rejected", False): + # Commodity rejected (uses artificial arc) — no real arcs used + continue + for arc_entry in sc.get("path_arcs", []): + aid = arc_entry["arc_id"] + x[k][aid] = 1.0 + + # ==================================================================== + # Constraint (4): Binary variable domain + # x^k_{ij} in {0, 1} for all ij in A, for all k in K + # + # In the path-based solution encoding, each commodity is either rejected + # or assigned exactly one path with binary arc usage. We check: + # (a) Each x^k_{ij} value is 0 or 1 + # (b) Each commodity has exactly one assignment (one path or rejected) + # (c) Arc IDs referenced in the solution are valid arcs in the instance + # ==================================================================== + for comm in commodities: + k = comm["commodity_id"] + sc = sol_comm_by_id.get(k) + if sc is None: + # Commodity missing from solution entirely — this is an assignment issue + # Treated under constraint (3) flow conservation + continue + + rejected = sc.get("rejected", False) + path_arcs = sc.get("path_arcs", []) + + # Check: commodity must be either rejected or have a non-empty path + if not rejected and len(path_arcs) == 0: + violated_constraints_set.add(4) + violations.append( + f"Commodity {k}: neither rejected nor assigned a path " + f"(binary/assignment constraint violated)" + ) + # violation_amount: should be 1 path assigned, have 0 + rhs_val = 1.0 + violation_magnitudes.append({ + "constraint": 4, + "lhs": 0.0, + "rhs": rhs_val, + "raw_excess": 1.0, + "normalizer": max(abs(rhs_val), eps), + "ratio": 1.0 / max(abs(rhs_val), eps) + }) + + # Check arc validity: every arc referenced must exist in the instance + for arc_entry in path_arcs: + aid = arc_entry["arc_id"] + if aid not in arc_by_id: + violated_constraints_set.add(4) + violations.append( + f"Commodity {k}: references non-existent arc_id {aid}" + ) + rhs_val = 1.0 + violation_magnitudes.append({ + "constraint": 4, + "lhs": 0.0, + "rhs": rhs_val, + "raw_excess": 1.0, + "normalizer": max(abs(rhs_val), eps), + "ratio": 1.0 / max(abs(rhs_val), eps) + }) + + # ==================================================================== + # Constraint (2): Capacity constraints + # sum_{k in K} q^k * x^k_{ij} <= d_{ij}, for all ij in A + # ==================================================================== + for arc in arcs: + aid = arc["arc_id"] + capacity = arc["capacity"] + from_node = arc["from_node"] + to_node = arc["to_node"] + + # Compute LHS: sum of demands of commodities using this arc + lhs = 0.0 + for comm in commodities: + k = comm["commodity_id"] + demand = comm["demand"] + lhs += demand * x[k][aid] + + rhs_val = float(capacity) + violation_amount = lhs - rhs_val # For <= constraint + + if violation_amount > tol: + violated_constraints_set.add(2) + violations.append( + f"Arc {aid} ({from_node}->{to_node}): capacity exceeded, " + f"flow={lhs:.4f} > capacity={rhs_val:.4f}" + ) + normalizer = max(abs(rhs_val), eps) + violation_magnitudes.append({ + "constraint": 2, + "lhs": lhs, + "rhs": rhs_val, + "raw_excess": violation_amount, + "normalizer": normalizer, + "ratio": violation_amount / normalizer + }) + + # ==================================================================== + # Constraint (3): Flow conservation + # sum_{ij in A} x^k_{ij} - sum_{ji in A} x^k_{ji} = b^k_i, + # for all i in N, for all k in K + # where b^k_i = 1 if i is origin of k, -1 if destination, 0 otherwise + # + # For rejected commodities (using the artificial arc from origin to + # destination), the flow on real arcs is 0. The artificial arc provides + # the unit flow, so net flow at origin = 1 (via artificial), at + # destination = -1. Since artificial arcs are not in the network arc set, + # for rejected commodities: all b^k_i checks with real arcs yield + # net flow = 0 at every node. This is consistent because the artificial + # arc is outside the real network — the model accounts for it separately. + # + # We check flow conservation on real arcs only for non-rejected + # commodities. For rejected commodities, real-arc flow must be 0 at all + # nodes (which is trivially satisfied since they have no path_arcs). + # ==================================================================== + # Build outgoing and incoming arc lookups + outgoing_arcs = defaultdict(list) # node -> [arc_id, ...] + incoming_arcs = defaultdict(list) # node -> [arc_id, ...] + for arc in arcs: + outgoing_arcs[arc["from_node"]].append(arc["arc_id"]) + incoming_arcs[arc["to_node"]].append(arc["arc_id"]) + + for comm in commodities: + k = comm["commodity_id"] + origin = comm["origin"] + destination = comm["destination"] + + sc = sol_comm_by_id.get(k) + if sc is None: + # Commodity missing — violated at origin node + violated_constraints_set.add(3) + violations.append( + f"Commodity {k}: missing from solution " + f"(flow conservation violated)" + ) + rhs_val = 1.0 + normalizer = max(abs(rhs_val), eps) + violation_magnitudes.append({ + "constraint": 3, + "lhs": 0.0, + "rhs": rhs_val, + "raw_excess": 1.0, + "normalizer": normalizer, + "ratio": 1.0 / normalizer + }) + continue + + rejected = sc.get("rejected", False) + + if rejected: + # For rejected commodity, all real arc flows should be 0. + # This is trivially satisfied if path_arcs is empty. + # But check anyway in case solution has path_arcs AND rejected=true + if sc.get("path_arcs", []): + violated_constraints_set.add(3) + violations.append( + f"Commodity {k}: marked as rejected but has path_arcs " + f"(flow conservation inconsistency)" + ) + rhs_val = 0.0 + flow_sum = float(len(sc["path_arcs"])) + violation_amount = abs(flow_sum) + normalizer = max(abs(rhs_val), eps) + violation_magnitudes.append({ + "constraint": 3, + "lhs": flow_sum, + "rhs": rhs_val, + "raw_excess": violation_amount, + "normalizer": normalizer, + "ratio": violation_amount / normalizer + }) + continue + + # Non-rejected commodity: check flow conservation at every node + for node in nodes: + # b^k_i + if node == origin: + b_ki = 1.0 + elif node == destination: + b_ki = -1.0 + else: + b_ki = 0.0 + + # sum of x^k_{ij} for arcs leaving node i + out_flow = 0.0 + for aid in outgoing_arcs[node]: + out_flow += x[k][aid] + + # sum of x^k_{ji} for arcs entering node i + in_flow = 0.0 + for aid in incoming_arcs[node]: + in_flow += x[k][aid] + + lhs = out_flow - in_flow + rhs_val = b_ki + violation_amount = abs(lhs - rhs_val) + + if violation_amount > tol: + violated_constraints_set.add(3) + violations.append( + f"Commodity {k} at node {node}: flow conservation violated, " + f"net_flow={lhs:.4f}, expected={rhs_val:.4f}" + ) + normalizer = max(abs(rhs_val), eps) + violation_magnitudes.append({ + "constraint": 3, + "lhs": lhs, + "rhs": rhs_val, + "raw_excess": violation_amount, + "normalizer": normalizer, + "ratio": violation_amount / normalizer + }) + + # ==================================================================== + # Constraint (5): Objective consistency (Tier C defense vs score-gaming) + # reported objective_value must equal the value recomputed directly + # from the routing variables in the solution: + # true_obj = sum_{rejected k} (artificial_arc_cost[k] * demand[k]) + # + sum_{non-rejected k} sum_{ij in path_arcs[k]} (cost[ij] * demand[k]) + # All obj-determining variables (rejected flag + path_arcs) are in the + # solution, so a full recompute is exact. + # ==================================================================== + reported_obj_raw = solution_data.get("objective_value") + if reported_obj_raw is not None: + try: + reported = float(reported_obj_raw) + except (TypeError, ValueError): + reported = None + if reported is not None: + true_obj = 0.0 + comm_by_id = {c["commodity_id"]: c for c in commodities} + for comm in commodities: + k = comm["commodity_id"] + demand = comm["demand"] + sc = sol_comm_by_id.get(k) + if sc is None: + continue + if sc.get("rejected", False): + true_obj += float(comm["artificial_arc_cost"]) * float(demand) + else: + for arc_entry in sc.get("path_arcs", []): + aid = arc_entry["arc_id"] + arc = arc_by_id.get(aid) + if arc is None: + # already flagged under constraint (4); skip + continue + true_obj += float(arc["cost"]) * float(demand) + + abs_diff = abs(reported - true_obj) + # 0.1% relative tolerance with 1e-3 absolute floor + obj_tol = max(1e-3, 1e-3 * abs(true_obj)) + if abs_diff > obj_tol: + violated_constraints_set.add(5) + violations.append( + f"Objective consistency violated: reported objective_value=" + f"{reported} differs from recomputed total cost=" + f"{true_obj} (|diff|={abs_diff:.3g}, tol={obj_tol:.3g})" + ) + normalizer = max(abs(true_obj), eps) + violation_magnitudes.append({ + "constraint": 5, + "lhs": reported, + "rhs": true_obj, + "raw_excess": abs_diff, + "normalizer": normalizer, + "ratio": abs_diff / normalizer + }) + + # ---- Assemble result ---- + feasible = len(violated_constraints_set) == 0 + result = { + "feasible": feasible, + "violated_constraints": sorted(violated_constraints_set), + "violations": violations, + "violation_magnitudes": violation_magnitudes + } + return result + + +def main(): + parser = argparse.ArgumentParser( + description="Feasibility checker for ODIMCF (Barnhart et al. 2000)" + ) + parser.add_argument("--instance_path", type=str, required=True, + help="Path to the JSON file containing the data instance") + parser.add_argument("--solution_path", type=str, required=True, + help="Path to the JSON file containing the candidate solution") + parser.add_argument("--result_path", type=str, required=True, + help="Path to write the JSON file containing the feasibility result") + args = parser.parse_args() + + with open(args.instance_path, "r") as f: + instance_data = json.load(f) + + with open(args.solution_path, "r") as f: + solution_data = json.load(f) + _frontieror_contract_result = _frontieror_validate_solution_contract(solution_data) + if _frontieror_contract_result is not None: + with open(args.result_path, "w") as _frontieror_result_handle: + json.dump( + _frontieror_contract_result, + _frontieror_result_handle, + indent=2, + ) + return + + result = check_feasibility(instance_data, solution_data) + + with open(args.result_path, "w") as f: + json.dump(result, f, indent=2) + + if result["feasible"]: + print(f"FEASIBLE: No constraint violations found.") + else: + print(f"INFEASIBLE: {len(result['violated_constraints'])} constraint(s) violated.") + for v in result["violations"]: + print(f" - {v}") + + +if __name__ == "__main__": + try: + main() + except Exception as _frontieror_unexpected_error: + if not _frontieror_write_unexpected_rejection( + _frontieror_unexpected_error + ): + raise diff --git a/barnhart2000/gurobi_code.py b/barnhart2000/gurobi_code.py new file mode 100644 index 0000000000000000000000000000000000000000..159a6ad377d102b5582486ab6dc0c6068ae253a6 --- /dev/null +++ b/barnhart2000/gurobi_code.py @@ -0,0 +1,210 @@ +""" +Gurobi implementation of the Origin-Destination Integer Multicommodity Flow (ODIMCF) problem. + +Based on: Barnhart, Hane, and Vance (2000), "Using Branch-and-Price-and-Cut to Solve +Origin-Destination Integer Multicommodity Flow Problems", Operations Research 48(2), 318-326. + +This implements the Node-Arc (Formulation 1) from the paper: + min sum_{k in K} sum_{ij in A} c^k_{ij} * q^k * x^k_{ij} + s.t. sum_{k in K} q^k * x^k_{ij} <= d_{ij}, for all ij in A (capacity) + sum_{ij} x^k_{ij} - sum_{ji} x^k_{ji} = b^k_i, for all i, k (flow conservation) + x^k_{ij} in {0,1} (binary) + +Each commodity has an artificial arc from origin to destination with cost = revenue, +representing rejection. Original arcs have cost 0. +""" + +import argparse +import json +import os +import gurobipy as gp +from gurobipy import GRB +import os as _os, sys as _sys +# Walk up from this file's directory to find repo root (containing scripts/). +_repo = _os.path.dirname(_os.path.abspath(__file__)) +while _repo != _os.path.dirname(_repo) and not _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _repo = _os.path.dirname(_repo) +if _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _sys.path.insert(0, _repo) +try: + from scripts.utils.gurobi_log_helper import install_gurobi_logger +except ImportError: + def install_gurobi_logger(log_path): # no-op fallback when scripts/ unavailable + pass + + +def load_instance(instance_path): + """Load the problem instance from a JSON file.""" + with open(instance_path, "r") as f: + data = json.load(f) + return data + + +def build_and_solve(data, time_limit): + """Build and solve the ODIMCF model using Gurobi.""" + + nodes = data["network"]["nodes"] + arcs = data["network"]["arcs"] + commodities = data["commodities"]["commodity_list"] + + num_nodes = data["network"]["num_nodes"] + num_arcs = data["network"]["num_arcs"] + num_commodities = data["commodities"]["num_commodities"] + + # Build adjacency: arc index -> (from, to, capacity, cost) + arc_list = [] + for arc in arcs: + arc_list.append((arc["from_node"], arc["to_node"], arc["capacity"], arc["cost"])) + + model = gp.Model("ODIMCF") + model.setParam("Threads", 1) + model.setParam("TimeLimit", time_limit) + model.setParam("OutputFlag", 1) + + # --- Decision Variables --- + # x[k][a] = 1 if commodity k uses arc a (original arcs) + x = {} + for k_idx, comm in enumerate(commodities): + for a_idx, (i, j, cap, cost) in enumerate(arc_list): + x[k_idx, a_idx] = model.addVar( + vtype=GRB.BINARY, + name=f"x_{k_idx}_{a_idx}", + obj=cost * comm["demand"] # c^k_{ij} * q^k + ) + + # x_art[k] = 1 if commodity k is rejected (uses artificial arc) + x_art = {} + for k_idx, comm in enumerate(commodities): + x_art[k_idx] = model.addVar( + vtype=GRB.BINARY, + name=f"x_art_{k_idx}", + obj=comm["artificial_arc_cost"] * comm["demand"] # revenue * q^k + ) + + model.update() + + # --- Capacity Constraints --- + # sum_{k in K} q^k * x^k_{ij} <= d_{ij}, for all ij in A + for a_idx, (i, j, cap, cost) in enumerate(arc_list): + model.addConstr( + gp.quicksum( + commodities[k_idx]["demand"] * x[k_idx, a_idx] + for k_idx in range(num_commodities) + ) <= cap, + name=f"cap_{a_idx}" + ) + + # --- Flow Conservation Constraints --- + # For each commodity k, for each node i: + # sum_{ij in A} x^k_{ij} - sum_{ji in A} x^k_{ji} = b^k_i + # The artificial arc goes from origin to destination directly. + for k_idx, comm in enumerate(commodities): + origin = comm["origin"] + destination = comm["destination"] + for node in nodes: + # Compute b^k_i + if node == origin: + b_ki = 1 + elif node == destination: + b_ki = -1 + else: + b_ki = 0 + + # Outflow: arcs leaving node (including artificial if node == origin) + outflow = gp.quicksum( + x[k_idx, a_idx] + for a_idx, (i, j, cap, cost) in enumerate(arc_list) + if i == node + ) + if node == origin: + outflow += x_art[k_idx] + + # Inflow: arcs entering node (including artificial if node == destination) + inflow = gp.quicksum( + x[k_idx, a_idx] + for a_idx, (i, j, cap, cost) in enumerate(arc_list) + if j == node + ) + if node == destination: + inflow += x_art[k_idx] + + model.addConstr( + outflow - inflow == b_ki, + name=f"flow_{k_idx}_{node}" + ) + + # --- Optimize --- + model.optimize() + + # --- Extract Solution --- + solution = { + "objective_value": None, + "status": None, + "commodities": [] + } + + if model.SolCount > 0: + solution["objective_value"] = model.ObjVal + solution["status"] = "optimal" if model.Status == GRB.OPTIMAL else "feasible" + + for k_idx, comm in enumerate(commodities): + comm_sol = { + "commodity_id": comm["commodity_id"], + "origin": comm["origin"], + "destination": comm["destination"], + "demand": comm["demand"], + "rejected": False, + "path_arcs": [] + } + + if x_art[k_idx].X > 0.5: + comm_sol["rejected"] = True + else: + for a_idx, (i, j, cap, cost) in enumerate(arc_list): + if x[k_idx, a_idx].X > 0.5: + comm_sol["path_arcs"].append({"from": i, "to": j, "arc_id": a_idx}) + + solution["commodities"].append(comm_sol) + + # Compute gap + if model.Status == GRB.OPTIMAL: + solution["gap"] = 0.0 + else: + solution["gap"] = model.MIPGap + else: + solution["objective_value"] = None + solution["status"] = "infeasible_or_no_solution" + + return solution + + +def main(): + parser = argparse.ArgumentParser( + description="Solve ODIMCF using Gurobi (Node-Arc formulation)" + ) + parser.add_argument("--instance_path", type=str, required=True, + help="Path to the JSON instance file") + parser.add_argument("--solution_path", type=str, required=True, + help="Path for the output solution JSON file") + parser.add_argument("--time_limit", type=int, default=3600, + help="Maximum solver runtime in seconds") + parser.add_argument("--log_path", type=str, default=None, help="Path to log incumbent solutions") + args = parser.parse_args() + install_gurobi_logger(args.log_path) + + data = load_instance(args.instance_path) + solution = build_and_solve(data, args.time_limit) + + with open(args.solution_path, "w") as f: + json.dump(solution, f, indent=2) + + print(f"Solution written to {args.solution_path}") + if solution["objective_value"] is not None: + print(f"Objective value: {solution['objective_value']}") + print(f"Status: {solution['status']}") + else: + print("No feasible solution found.") + + +if __name__ == "__main__": + main() diff --git a/barnhart2000/gurobi_feasi_result/large_feasi_result_1.json b/barnhart2000/gurobi_feasi_result/large_feasi_result_1.json new file mode 100644 index 0000000000000000000000000000000000000000..e305b82c3d6732211d5033415df1a105476e9cf1 --- /dev/null +++ b/barnhart2000/gurobi_feasi_result/large_feasi_result_1.json @@ 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@@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8edd9796644c92ff067fe439db6d9a0aa59ab5a59e3acb0869fc9e0343713414 +size 117828 diff --git a/barnhart2000/instance/tiny_instance.json b/barnhart2000/instance/tiny_instance.json new file mode 100644 index 0000000000000000000000000000000000000000..b3c0f7ad589d285881b96232b2954e2279b7863d --- /dev/null +++ b/barnhart2000/instance/tiny_instance.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:483f7ac69127be4feb8faad5ffa5f63d3687d3480c392a33f36b6f11e0600835 +size 6957 diff --git a/barnhart2000/instance_schema.json b/barnhart2000/instance_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..b5d574e8542645870e4f880fd5eaab7bc19b96c4 --- /dev/null +++ b/barnhart2000/instance_schema.json @@ -0,0 +1,30 @@ +{ + "network": { + "num_nodes": " Total number of nodes in the directed network.", + "num_arcs": " Total number of directed physical arcs in the network.", + "nodes": " Identifiers for all nodes in the network.", + "arcs": [ + { + "arc_id": " Unique identifier for this arc.", + "from_node": " Node where this directed arc originates.", + "to_node": " Node where this directed arc terminates.", + "capacity": " Maximum total bandwidth that can flow through this arc across all commodities.", + "cost": " Per-unit routing cost for any commodity using this arc." + } + ] + }, + "commodities": { + "num_commodities": " Total number of commodities (call groups) to be routed or rejected.", + "commodity_list": [ + { + "commodity_id": " Unique identifier for this commodity.", + "origin": " Node where this commodity originates.", + "destination": " Node where this commodity must be delivered.", + "demand": " Bandwidth required by this commodity.", + "revenue": " Revenue earned if this commodity is successfully routed through the physical network.", + "artificial_arc_cost": " Cost incurred if this commodity is rejected instead of being routed." + } + ] + }, + "objective": " Direction of optimization for the total routing and rejection cost." +} diff --git a/barnhart2000/mathematical_formulation.md b/barnhart2000/mathematical_formulation.md new file mode 100644 index 0000000000000000000000000000000000000000..f4f6db157ed208f846333fc76c916e413baf7198 --- /dev/null +++ b/barnhart2000/mathematical_formulation.md @@ -0,0 +1,35 @@ +# Original Formulation: Origin-Destination Integer Multicommodity Flow (ODIMCF) + +*Source: “Using Branch-and-Price-and-Cut to Solve Origin-Destination Integer Multicommodity Flow Problems,” Barnhart, Hane, and Vance, Operations Research 48(2):318–326, 2000.* + +## Sets and Parameters + +- $G = (N, A)$: directed network with node set $N$ and arc set $A$. + +- $K$: set of commodities; each commodity $k \in K$ is defined by an origin–destination pair. + +- $q^k$: quantity (demand) of commodity $k$. + +- $c^k_{ij}$: unit flow cost for commodity $k$ on arc $ij$. + +- $d_{ij}$: capacity of arc $ij$, for $ij \in A$. + +- $b^k_i = 1$ if $i$ is the origin of $k$, $-1$ if destination of $k$, $0$ otherwise. + +## Decision Variables + +- $x^k_{ij} \in \{0,1\}$: $1$ if the entire quantity $q^k$ of commodity $k$ is assigned to arc $ij$, $0$ otherwise. + +## Objective + +$$\begin{equation} +\min \sum_{k \in K} \sum_{ij \in A} c^k_{ij}\, q^k\, x^k_{ij} \tag{1} +\end{equation}$$ + +## Constraints + +$$\begin{align} +\sum_{k \in K} q^k\, x^k_{ij} &\le d_{ij}, & \forall\, ij \in A \tag{2} \\ +\sum_{ij \in A} x^k_{ij} - \sum_{ji \in A} x^k_{ji} &= b^k_i, & \forall\, i \in N,\; \forall\, k \in K \tag{3} \\ +x^k_{ij} &\in \{0,1\}, & \forall\, ij \in A,\; \forall\, k \in K \tag{4} +\end{align}$$ diff --git a/barnhart2000/problem_description.txt b/barnhart2000/problem_description.txt new file mode 100644 index 0000000000000000000000000000000000000000..99b608797901e97af8437688da5e960809b2553b --- /dev/null +++ b/barnhart2000/problem_description.txt @@ -0,0 +1,9 @@ +# Problem Description + +A telecommunications network operator manages a directed network consisting of a set of nodes and a set of directed arcs connecting pairs of nodes. Each arc has a known capacity, expressed in units of bandwidth. The operator must handle a set of commodities, where each commodity represents a group of calls or messages sharing a common origin node and a common destination node. Each commodity has a known demand quantity representing the bandwidth it requires. + +Every commodity must be routed as a single, unsplittable unit along exactly one directed path from its origin node to its destination node, or it may be rejected entirely. To allow rejection, the network includes, for each commodity, an artificial direct arc from the commodity's origin to its destination. The artificial arc has no capacity limit and carries a cost equal to the revenue that would have been earned by successfully routing that commodity. All physical arcs in the network have zero routing cost for every commodity. + +The operator must choose, for each commodity, exactly one origin-to-destination path (which may be the artificial rejection arc or any path through the physical network). The total demand flowing through any physical arc, summed across all commodities whose chosen paths include that arc, must not exceed the arc's capacity. Flow must be conserved at every node for every commodity: at the origin node of a commodity, exactly one unit of that commodity's flow is produced; at the destination node, exactly one unit is absorbed; and at every intermediate node, the total flow of that commodity entering the node equals the total flow leaving it. + +The goal is to minimize the total cost. For each combination of commodity and arc, the cost contribution equals the commodity's demand multiplied by the per-unit cost for that commodity on that arc, counted only when that arc lies on the commodity's chosen path. The total cost is the sum of these contributions across all commodities and all arcs. Because physical arcs have zero cost, the total cost reduces to the sum of artificial-arc costs for rejected commodities, which equals the total lost revenue from commodities not routed through the physical network. Equivalently, minimizing total cost maximizes the total revenue earned from commodities successfully carried on the physical network. diff --git a/barnhart2000/solution_logger.py b/barnhart2000/solution_logger.py new file mode 100644 index 0000000000000000000000000000000000000000..5393fa935ce71df9da95cee04fe5199077cb8461 --- /dev/null +++ b/barnhart2000/solution_logger.py @@ -0,0 +1,61 @@ +""" +Convergence logger for optimization algorithms. + +Records incumbent solutions with timestamps to a JSONL file. +This module is provided to LLM-generated programs — they only need to call +`log(objective_value)` whenever a better feasible solution is found. + +Usage in generated code: + from solution_logger import SolutionLogger + logger = SolutionLogger(log_path, sense="minimize") # or "maximize" + # ... inside algorithm loop: + logger.log(objective_value) +""" + +import json +import time + + +class SolutionLogger: + def __init__(self, log_path, sense="minimize"): + """ + Args: + log_path: Path to the JSONL output file. + sense: "minimize" or "maximize". + """ + self.log_path = log_path + self.sense = sense + self.start_time = time.time() + self.best_obj = None + self.min_interval = 0.1 # seconds, avoid excessive writes + + self._last_log_time = 0.0 + # Clear the file + with open(self.log_path, "w") as f: + pass + + def log(self, objective_value): + """Record a new incumbent if it improves on the best known.""" + if objective_value is None: + return + + # Check if this is an improvement + if self.best_obj is not None: + if self.sense == "minimize" and objective_value >= self.best_obj: + return + if self.sense == "maximize" and objective_value <= self.best_obj: + return + + elapsed = time.time() - self.start_time + + # Throttle writes + if self.best_obj is not None and elapsed - self._last_log_time < self.min_interval: + self.best_obj = objective_value + return + + self.best_obj = objective_value + self._last_log_time = elapsed + + with open(self.log_path, "a") as f: + f.write(json.dumps({"time": round(elapsed, 3), + "objective_value": objective_value}) + "\n") diff --git a/barnhart2000/solution_schema.json b/barnhart2000/solution_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..a5790908ce73a94190b4c1ad57b11b353124e0c1 --- /dev/null +++ b/barnhart2000/solution_schema.json @@ -0,0 +1,16 @@ +{ + "objective_value": " Total cost of rejected commodities across the entire routing plan.", + "commodities": [ + { + "commodity_id": " Identifier of the commodity whose routing outcome is described in this entry.", + "rejected": " Whether this commodity is rejected and sent on its artificial arc instead of being routed through the network.", + "path_arcs": [ + { + "from": " Origin node of an arc used on this commodity's routing path.", + "to": " Destination node of an arc used on this commodity's routing path.", + "arc_id": " Identifier of an arc used on this commodity's routing path." + } + ] + } + ] +} diff --git a/bayani2024/feasibility_check.py b/bayani2024/feasibility_check.py new file mode 100644 index 0000000000000000000000000000000000000000..92dfe3ae6a986262b0933e8cd07c01b4eedb10cb --- /dev/null +++ b/bayani2024/feasibility_check.py @@ -0,0 +1,375 @@ +#!/usr/bin/env python3 +""" +Feasibility checker for QSAP/AQSAP (Quadratic Semi-Assignment Problem). +Paper: Bayani, Rostami, Adulyasak, Rousseau (2021) +"A dual bounding framework for binary quadratic combinatorial optimization" + +Checks the compact formulation (Equations 12-14): + Constraint 1 (Eq 13): Each client assigned to exactly one server + Constraint 2 (Eq 14): Assignment variables are binary (0 or 1) + Constraint 3: Server indices are valid (0 <= j < h) + Constraint 4: All clients are assigned + Constraint 5: Objective value matches the computed cost +""" + +import json +import argparse + +# FRONTIEROR_RELEASE_CONTRACT_V2 +_FRONTIEROR_REQUIRED_SOLUTION_FIELDS = ('assignment', 'objective_value') +_FRONTIEROR_NONEMPTY_SOLUTION_FIELDS = ('assignment',) +_FRONTIEROR_DERIVED_FIELD_RULES = () +_FRONTIEROR_CONDITIONAL_FIELD_RULES = () + + +def _frontieror_validate_solution_contract(solution): + """Return a structured rejection when the public solution contract is absent.""" + violations = [] + if not isinstance(solution, dict): + violations.append("solution must be a JSON object") + else: + missing = [ + field + for field in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS + if field not in solution or solution[field] is None + ] + if missing: + violations.append( + "missing required solution field(s): " + ", ".join(missing) + ) + + for discriminator, expected_value, fields in _FRONTIEROR_CONDITIONAL_FIELD_RULES: + if solution.get(discriminator) != expected_value: + continue + conditional_missing = [ + field + for field in fields + if field not in solution or solution[field] is None + ] + if conditional_missing: + violations.append( + "missing conditionally required solution field(s): " + + ", ".join(conditional_missing) + ) + + empty = [ + field + for field in _FRONTIEROR_NONEMPTY_SOLUTION_FIELDS + if field in solution and solution[field] in (None, [], {}) + ] + if empty: + violations.append( + "empty required decision witness field(s): " + ", ".join(empty) + ) + + for target, operation, source, source_key in _FRONTIEROR_DERIVED_FIELD_RULES: + try: + reported = float(solution[target]) + if operation == "length": + expected = float(len(solution[source])) + elif operation == "mapping_value": + expected = float(solution[source][source_key]) + else: + raise ValueError("unknown derived-field operation") + except (KeyError, TypeError, ValueError, OverflowError): + violations.append( + f"cannot derive {target} from decision witness field {source}" + ) + continue + if ( + reported != reported + or expected != expected + or reported in (float("inf"), float("-inf")) + or expected in (float("inf"), float("-inf")) + or abs(reported - expected) > 1e-6 + ): + violations.append( + f"{target}={reported} does not match the decision-derived " + f"value {expected}" + ) + + if "objective_value" in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS: + objective = solution.get("objective_value") + if ( + isinstance(objective, bool) + or not isinstance(objective, (int, float)) + or objective != objective + or objective in (float("inf"), float("-inf")) + ): + violations.append("objective_value must be a finite JSON number") + + if not violations: + return None + return { + "feasible": False, + "violated_constraints": ["solution_schema"], + "violations": violations, + "violation_magnitudes": [], + } + + +def _frontieror_write_unexpected_rejection(error): + """Turn malformed candidate/checker exceptions into a normal rejection.""" + import json as _frontieror_json + import sys as _frontieror_sys + + result_path = None + for index, argument in enumerate(_frontieror_sys.argv): + if argument == "--result_path" and index + 1 < len(_frontieror_sys.argv): + result_path = _frontieror_sys.argv[index + 1] + break + if argument.startswith("--result_path="): + result_path = argument.split("=", 1)[1] + break + if not result_path: + return False + try: + with open(result_path, "w") as result_handle: + _frontieror_json.dump( + { + "feasible": False, + "violated_constraints": ["malformed_solution"], + "violations": [ + "checker rejected malformed candidate: " + + type(error).__name__ + + ": " + + str(error)[:500] + ], + "violation_magnitudes": [], + }, + result_handle, + indent=2, + ) + except Exception: + return False + return True + + + +def main(): + parser = argparse.ArgumentParser( + description="Feasibility checker for QSAP/AQSAP (Bayani et al. 2021)" + ) + parser.add_argument("--instance_path", type=str, required=True, + help="Path to the JSON instance file.") + parser.add_argument("--solution_path", type=str, required=True, + help="Path to the JSON solution file.") + parser.add_argument("--result_path", type=str, required=True, + help="Path to write the JSON feasibility result.") + args = parser.parse_args() + + tol = 1e-4 + + # ---- Load instance ---- + with open(args.instance_path, "r") as f: + data = json.load(f) + + # ---- Load solution ---- + with open(args.solution_path, "r") as f: + sol = json.load(f) + _frontieror_contract_result = _frontieror_validate_solution_contract(sol) + if _frontieror_contract_result is not None: + with open(args.result_path, "w") as _frontieror_result_handle: + json.dump( + _frontieror_contract_result, + _frontieror_result_handle, + indent=2, + ) + return + + n = data["n_clients"] + h = data["n_servers"] + linear_costs = data["linear_costs"] + quadratic_costs = data["quadratic_costs"] + + violated_constraints = set() + violations = [] + violation_magnitudes = [] + + # Check if solution has a valid objective value + obj_val = sol.get("objective_value") + assignment = sol.get("assignment", {}) + + if obj_val is None or not assignment: + result = { + "feasible": None, + "violated_constraints": [], + "violations": ["No feasible solution found (objective_value is null or assignment is empty)"], + "violation_magnitudes": [] + } + with open(args.result_path, "w") as f: + json.dump(result, f, indent=2) + print("Feasibility check: No solution to check (null objective or empty assignment)") + return + + # Convert assignment keys to int + assign = {} + for k, v in assignment.items(): + assign[int(k)] = int(v) + + # ================================================================ + # Constraint 1 (Eq 13): Each client assigned to exactly one server + # sum_{e in delta(i)} x_e = 1 for all i in N + # ================================================================ + for i in range(n): + if i not in assign: + violated_constraints.add(1) + violations.append( + f"Constraint 1 (Assignment): Client {i} is not assigned to any server" + ) + violation_magnitudes.append({ + "constraint": 1, + "lhs": 0, + "rhs": 1, + "raw_excess": 1.0, + "normalizer": 1.0, + "ratio": 1.0 + }) + + # Check no client is assigned multiple times (shouldn't happen with dict) + # but verify all clients are covered + assigned_clients = set(assign.keys()) + expected_clients = set(range(n)) + missing = expected_clients - assigned_clients + if missing: + violated_constraints.add(1) + violations.append( + f"Constraint 1 (Assignment): Missing clients: {sorted(missing)}" + ) + violation_magnitudes.append({ + "constraint": 1, + "lhs": len(assigned_clients), + "rhs": n, + "raw_excess": float(len(missing)), + "normalizer": float(n), + "ratio": float(len(missing)) / n + }) + + # ================================================================ + # Constraint 2 (Eq 14): Valid server indices + # x_e in {0,1}, server j in {0,...,h-1} + # ================================================================ + for i, j in assign.items(): + if j < 0 or j >= h: + violated_constraints.add(2) + violations.append( + f"Constraint 2 (Server range): Client {i} assigned to server {j}, " + f"but valid range is [0, {h-1}]" + ) + violation_magnitudes.append({ + "constraint": 2, + "lhs": j, + "rhs": h - 1, + "raw_excess": float(max(j - (h-1), -j)), + "normalizer": float(h), + "ratio": float(max(j - (h-1), -j)) / h + }) + + # ================================================================ + # Constraint 3: Objective value check + # obj = sum_{e} c_e x_e + sum_{(e,f)} q_{ef} x_e x_f + # ================================================================ + if not violated_constraints: + # Compute linear cost + computed_linear = 0.0 + for i in range(n): + j = assign[i] + computed_linear += linear_costs[i][j] + + # Build quadratic cost lookup + quad_dict = {} + for entry in quadratic_costs: + i1, j1, i2, j2 = int(entry[0]), int(entry[1]), int(entry[2]), int(entry[3]) + q_val = entry[4] + e = (i1, j1) + f = (i2, j2) + if (e, f) not in quad_dict and (f, e) not in quad_dict: + quad_dict[(e, f)] = q_val + + # Compute quadratic cost + computed_quad = 0.0 + for (e, f), q_val in quad_dict.items(): + i1, j1 = e + i2, j2 = f + # x_e = 1 iff client i1 assigned to server j1 + # x_f = 1 iff client i2 assigned to server j2 + if assign.get(i1) == j1 and assign.get(i2) == j2: + computed_quad += q_val + + computed_obj = computed_linear + computed_quad + + obj_diff = abs(computed_obj - obj_val) + normalizer = max(abs(obj_val), abs(computed_obj), 1e-5) + relative_diff = obj_diff / normalizer + + if relative_diff > tol: + violated_constraints.add(3) + violations.append( + f"Constraint 3 (Objective): Reported objective = {obj_val:.6f}, " + f"computed objective = {computed_obj:.6f}, " + f"relative diff = {relative_diff:.6e}" + ) + violation_magnitudes.append({ + "constraint": 3, + "lhs": round(obj_val, 10), + "rhs": round(computed_obj, 10), + "raw_excess": round(obj_diff, 10), + "normalizer": round(normalizer, 10), + "ratio": round(relative_diff, 10) + }) + + # ---- Build result ---- + _domain_check_vars_binary = [] # assignment is facility index, not binary + _domain_check_vars_integer = [] + + # ===================================================================== + # Variable Domain Checks (auto-generated by add_domain_checks.py) + # ===================================================================== + # Constraint 4: Binary domain — variables must be 0 or 1 + for var_name, var_dict in _domain_check_vars_binary: + if isinstance(var_dict, dict): + for key, val in var_dict.items(): + try: + v = float(val) + except (TypeError, ValueError): + continue + if abs(v - round(v)) > tol or round(v) not in (0, 1): + viol = min(abs(v - 0), abs(v - 1)) + if viol > tol: + violated_constraints.add(4) + violations.append( + f"Constraint 4 (binary domain): {var_name}[{key}] = {v} not in {0, 1}") + violation_magnitudes.append({ + "constraint": 4, + "lhs": v, + "rhs": 1.0, + "raw_excess": float(viol), + "normalizer": 1.0, + "ratio": float(viol), + }) + + feasible = len(violated_constraints) == 0 + result = { + "feasible": feasible, + "violated_constraints": sorted(violated_constraints), + "violations": violations, + "violation_magnitudes": violation_magnitudes if not feasible else [] + } + + with open(args.result_path, "w") as f: + json.dump(result, f, indent=2) + + print(f"Feasibility check complete. Result: {'FEASIBLE' if feasible else 'INFEASIBLE'}") + if not feasible: + print(f"Violated constraints: {sorted(violated_constraints)}") + print(f"Number of violations: {len(violations)}") + + +if __name__ == "__main__": + try: + main() + except Exception as _frontieror_unexpected_error: + if not _frontieror_write_unexpected_rejection( + _frontieror_unexpected_error + ): + raise diff --git a/bayani2024/gurobi_code.py b/bayani2024/gurobi_code.py new file mode 100644 index 0000000000000000000000000000000000000000..cafe82a620fb7087f7a3ef4fb129ba0c03779b1e --- /dev/null +++ b/bayani2024/gurobi_code.py @@ -0,0 +1,189 @@ +#!/usr/bin/env python3 +""" +Gurobi implementation of the QSAP/AQSAP compact BQP formulation. + +Paper: "A dual bounding framework for binary quadratic combinatorial optimization" +Authors: M. Bayani, B. Rostami, Y. Adulyasak, L.-M. Rousseau (2021) + +This implements the compact QSAP formulation (Equations 12-14) with +Standard Linearization Technique (SLT, Appendix A, Equations A.1-A.4) +to linearize quadratic terms, solved directly by Gurobi (GUROBI+SLT method). + +For AQSAP instances (out_of_star_density == 0), quadratic costs only exist +between adjacent edges (edges sharing a server endpoint), and q_{ef} = c_e + c_f. +For general QSAP instances, out-of-star quadratic interactions also exist. +""" + +import argparse +import json +import sys + +import gurobipy as gp +from gurobipy import GRB +import os as _os, sys as _sys +# Walk up from this file's directory to find repo root (containing scripts/). +_repo = _os.path.dirname(_os.path.abspath(__file__)) +while _repo != _os.path.dirname(_repo) and not _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _repo = _os.path.dirname(_repo) +if _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _sys.path.insert(0, _repo) +try: + from scripts.utils.gurobi_log_helper import install_gurobi_logger +except ImportError: + def install_gurobi_logger(log_path): # no-op fallback when scripts/ unavailable + pass +def load_instance(instance_path): + """Load a QSAP/AQSAP instance from JSON.""" + with open(instance_path, 'r') as f: + data = json.load(f) + return data + + +def build_and_solve(data, time_limit): + """ + Build the linearized QSAP model (GUROBI+SLT) and solve it. + + The compact formulation is: + min sum_{e in A} c_e x_e + sum_{(e,f) in E} q_{ef} x_e x_f + s.t. sum_{e in delta(i)} x_e = 1 for all i in N (Eq. 13) + x_e in {0,1} for all e in A (Eq. 14) + + We linearize the quadratic terms using SLT: + y_{ef} >= x_e + x_f - 1 (A.1) + y_{ef} <= x_e (A.2) + y_{ef} <= x_f (A.3) + y_{ef} >= 0 (A.4) + + So the linearized model becomes: + min sum_{e in A} c_e x_e + sum_{(e,f)} q_{ef} y_{ef} + s.t. assignment constraints (13) + SLT constraints (A.1)-(A.4) + x_e in {0,1}, y_{ef} >= 0 + """ + n = data["n_clients"] + h = data["n_servers"] + linear_costs = data["linear_costs"] # n x h matrix + quadratic_costs = data["quadratic_costs"] # list of [i, j, k, l, q_val] + + model = gp.Model("QSAP_SLT") + model.setParam("Threads", 1) + model.setParam("TimeLimit", time_limit) + model.setParam("OutputFlag", 1) + + # Decision variables: x[i][j] = 1 if client i assigned to server j + x = {} + for i in range(n): + for j in range(h): + x[i, j] = model.addVar(vtype=GRB.BINARY, name=f"x_{i}_{j}", + obj=linear_costs[i][j]) + + # Parse quadratic costs into a dictionary keyed by a canonical unordered pair. + # Per Eq. (12) the objective sums q_{ef} x_e x_f over ordered pairs in + # E = E x E, so every listed entry must contribute its q_val; we accumulate + # on the canonical key so any redundant (e,f)/(f,e) listings are summed + # rather than silently dropped. + quad_dict = {} + for entry in quadratic_costs: + i, j, k, l = int(entry[0]), int(entry[1]), int(entry[2]), int(entry[3]) + q_val = entry[4] + e = (i, j) + f = (k, l) + key = (e, f) if e <= f else (f, e) + quad_dict[key] = quad_dict.get(key, 0.0) + q_val + + # Linearization variables y_{ef} for each quadratic pair + y = {} + for (e, f), q_val in quad_dict.items(): + y[(e, f)] = model.addVar(vtype=GRB.CONTINUOUS, lb=0.0, + name=f"y_{e[0]}{e[1]}_{f[0]}{f[1]}", + obj=q_val) + + model.update() + + # Constraint (13): each client assigned to exactly one server + for i in range(n): + model.addConstr( + gp.quicksum(x[i, j] for j in range(h)) == 1, + name=f"assign_{i}" + ) + + # SLT constraints for each quadratic pair + for (e, f), q_val in quad_dict.items(): + xe = x[e[0], e[1]] + xf = x[f[0], f[1]] + yef = y[(e, f)] + + # (A.1) y_{ef} >= x_e + x_f - 1 + model.addConstr(yef >= xe + xf - 1, + name=f"slt_a1_{e}_{f}") + # (A.2) y_{ef} <= x_e + model.addConstr(yef <= xe, + name=f"slt_a2_{e}_{f}") + # (A.3) y_{ef} <= x_f + model.addConstr(yef <= xf, + name=f"slt_a3_{e}_{f}") + # (A.4) y_{ef} >= 0 (already enforced by lb=0.0) + + # Optimize + model.optimize() + + # Extract solution + objective_value = None + assignment = {} + + if model.SolCount > 0: + objective_value = model.ObjVal + for i in range(n): + for j in range(h): + if x[i, j].X > 0.5: + assignment[i] = j + break + else: + # No feasible solution found + objective_value = None + + return objective_value, assignment, model + + +def main(): + parser = argparse.ArgumentParser( + description="Solve QSAP/AQSAP using Gurobi with SLT linearization" + ) + parser.add_argument("--instance_path", type=str, required=True, + help="Path to the JSON instance file") + parser.add_argument("--solution_path", type=str, required=True, + help="Path for the output solution JSON file") + parser.add_argument("--time_limit", type=int, required=True, + help="Maximum solver runtime in seconds") + parser.add_argument("--log_path", type=str, default=None, help="Path to log incumbent solutions") + args = parser.parse_args() + install_gurobi_logger(args.log_path) + + # Load instance + data = load_instance(args.instance_path) + + # Solve + objective_value, assignment, model = build_and_solve(data, args.time_limit) + + # Build solution output + solution = { + "objective_value": objective_value, + "assignment": {str(k): v for k, v in assignment.items()}, + "solver_status": model.Status, + "mip_gap": model.MIPGap if model.SolCount > 0 else None, + "solve_time": model.Runtime, + } + + # Write solution + with open(args.solution_path, 'w') as f: + json.dump(solution, f, indent=2) + + print(f"Solution written to {args.solution_path}") + if objective_value is not None: + print(f"Objective value: {objective_value}") + else: + print("No feasible solution found within the time limit.") + + +if __name__ == "__main__": + main() diff --git a/bayani2024/gurobi_feasi_result/large_feasi_result_1.json b/bayani2024/gurobi_feasi_result/large_feasi_result_1.json new file mode 100644 index 0000000000000000000000000000000000000000..e305b82c3d6732211d5033415df1a105476e9cf1 --- /dev/null +++ b/bayani2024/gurobi_feasi_result/large_feasi_result_1.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3269392edcbb7720882e77caadb88b4a9cf2fda04cf482cebd23baace4a531fd +size 102 diff --git a/bayani2024/gurobi_feasi_result/large_feasi_result_2.json b/bayani2024/gurobi_feasi_result/large_feasi_result_2.json new file mode 100644 index 0000000000000000000000000000000000000000..e305b82c3d6732211d5033415df1a105476e9cf1 --- /dev/null +++ b/bayani2024/gurobi_feasi_result/large_feasi_result_2.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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--- /dev/null +++ b/bayani2024/instance/large_instance_5.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a07873903fafc30b47fb9cfd039d6d851adc05549f1491ecee8e1656b68466e5 +size 2001704 diff --git a/bayani2024/instance/tiny_instance.json b/bayani2024/instance/tiny_instance.json new file mode 100644 index 0000000000000000000000000000000000000000..b368238bb216c78d75366f8b1452e443f93bd24c --- /dev/null +++ b/bayani2024/instance/tiny_instance.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fad46746fed2c0d202f319bde49cf92deb7fe1044ec26c92a7003a440f44860d +size 4807 diff --git a/bayani2024/instance_schema.json b/bayani2024/instance_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..9a1e4162b8b9b5bdb5f589096745c302d1d57e99 --- /dev/null +++ b/bayani2024/instance_schema.json @@ -0,0 +1,9 @@ +{ + "n_clients": " Number of clients that must each be assigned to a server.", + "n_servers": " Number of servers available to process client demands.", + "out_of_star_density": " Fraction of cross-server assignment pairs that carry a nonzero pairwise interaction cost.", + "demands": " Processing demand of each client.", + "processing_rates": " Processing rate of each server.", + "linear_costs": " Cost of assigning each client to each server, equal to the product of the client's demand and the server's processing rate.", + "quadratic_costs": " Pairwise interaction costs, where each entry is [client_1, server_1, client_2, server_2, cost] representing the joint cost incurred when client_1 is assigned to server_1 and client_2 is assigned to server_2 simultaneously." +} diff --git a/bayani2024/mathematical_formulation.md b/bayani2024/mathematical_formulation.md new file mode 100644 index 0000000000000000000000000000000000000000..9cfa4718abf2e8227e02adb6c3f9f11bfc20dfdb --- /dev/null +++ b/bayani2024/mathematical_formulation.md @@ -0,0 +1,38 @@ +# Original Formulation: Quadratic Semi-Assignment Problem (QSAP) + +*Source: A dual bounding framework for binary quadratic combinatorial optimization, M. Bayani, B. Rostami, Y. Adulyasak, L.-M. Rousseau, 2021 (Les Cahiers du GERAD, G–2021–43).* + +## Sets and Indices + +- $N = \{1, \dots, n\}$: set of clients. + +- $H = \{1, \dots, h\}$: set of servers. + +- $A$: set of edges (client–server assignment pairs). An edge $e = (i,j)$, $i \in N$, $j \in H$, represents assigning client $i$ to server $j$. ($A$ is the set of edges with one endpoint in $H$; here, with $N$ the clients and $H$ the servers, $A = E$.) + +- $\delta(i) \subseteq A$: set of edges incident to client $i \in N$ (i.e. all assignments of client $i$). + +- $\mathcal{E} = E \times E$: set of all (ordered) pairs of edges. + +## Parameters + +- $c_e = c_{ij} \in \mathbb{R}$: linear cost of assigning client $i$ to server $j$ (edge $e = (i,j)$). + +- $q_{ef} = q_{ijkl} \in \mathbb{R}$: quadratic cost incurred when client $i$ is assigned to server $j$ *and* client $k$ is assigned to server $l$ simultaneously, for the pair of edges $e = (i,j)$, $f = (k,l)$. + +## Decision Variables + +- $x_e \in \{0,1\}$: equals $1$ if the assignment corresponding to edge $e = (i,j)$ is selected (client $i$ assigned to server $j$), $0$ otherwise. + +## Objective + +$$\begin{align} +\min \quad & \sum_{e \in A} c_e x_e + \sum_{(e,f) \in \mathcal{E}} q_{ef}\, x_e x_f \tag{12} +\end{align}$$ + +## Constraints + +$$\begin{align} +& \sum_{e \in \delta(i)} x_e = 1 && \forall i \in N \tag{13} \\ +& x_e \in \{0,1\} && \forall e \in A \tag{14} +\end{align}$$ diff --git a/bayani2024/problem_description.txt b/bayani2024/problem_description.txt new file mode 100644 index 0000000000000000000000000000000000000000..939457cd147034f4f6de5c30c0056555e630783c --- /dev/null +++ b/bayani2024/problem_description.txt @@ -0,0 +1,7 @@ +# Problem Description + +A distributed processing system consists of a set of clients and a set of servers. Each client has a known demand value drawn from a continuous range, and each server has a known processing rate. The linear processing cost of assigning a particular client to a particular server equals the product of that client's demand and that server's processing rate. In addition to these linear costs, there are pairwise quadratic interaction costs that arise whenever two client-server assignments are both active simultaneously. A quadratic cost is specified for each pair of client-server assignments and represents the joint cost incurred when both assignments are in effect at the same time. Two types of quadratic interactions exist: adjacent interactions, which occur between two assignments that share the same server, and out-of-star interactions, which occur between two assignments directed at different servers. For adjacent interactions where two clients share the same server, the quadratic cost equals the sum of the two individual linear processing costs of those assignments. Out-of-star interaction costs, when present, are specified independently for a subset of cross-server assignment pairs at a given density level. + +Each client must be assigned to exactly one server, but a server may serve any number of clients. The total cost of an assignment consists of two additive components: the sum of linear processing costs over all active client-server assignments, and the sum of quadratic interaction costs over all pairs of assignments that are both active. Specifically, for every pair of active assignments that has a specified quadratic cost, that cost coefficient is added to the total. The goal is to find an assignment of every client to exactly one server that minimizes this total cost. + +The input data provides the number of clients, the number of servers, a demand value for each client, a processing rate for each server, a matrix of linear costs for each client-server pair, and a list of quadratic cost entries each specified as a tuple of two client-server pairs together with the associated pairwise cost value. A density parameter indicates what fraction of non-adjacent assignment pairs carry a nonzero quadratic cost; when this density is zero, only adjacent (same-server) quadratic interactions exist. diff --git a/bayani2024/solution_logger.py b/bayani2024/solution_logger.py new file mode 100644 index 0000000000000000000000000000000000000000..5393fa935ce71df9da95cee04fe5199077cb8461 --- /dev/null +++ b/bayani2024/solution_logger.py @@ -0,0 +1,61 @@ +""" +Convergence logger for optimization algorithms. + +Records incumbent solutions with timestamps to a JSONL file. +This module is provided to LLM-generated programs — they only need to call +`log(objective_value)` whenever a better feasible solution is found. + +Usage in generated code: + from solution_logger import SolutionLogger + logger = SolutionLogger(log_path, sense="minimize") # or "maximize" + # ... inside algorithm loop: + logger.log(objective_value) +""" + +import json +import time + + +class SolutionLogger: + def __init__(self, log_path, sense="minimize"): + """ + Args: + log_path: Path to the JSONL output file. + sense: "minimize" or "maximize". + """ + self.log_path = log_path + self.sense = sense + self.start_time = time.time() + self.best_obj = None + self.min_interval = 0.1 # seconds, avoid excessive writes + + self._last_log_time = 0.0 + # Clear the file + with open(self.log_path, "w") as f: + pass + + def log(self, objective_value): + """Record a new incumbent if it improves on the best known.""" + if objective_value is None: + return + + # Check if this is an improvement + if self.best_obj is not None: + if self.sense == "minimize" and objective_value >= self.best_obj: + return + if self.sense == "maximize" and objective_value <= self.best_obj: + return + + elapsed = time.time() - self.start_time + + # Throttle writes + if self.best_obj is not None and elapsed - self._last_log_time < self.min_interval: + self.best_obj = objective_value + return + + self.best_obj = objective_value + self._last_log_time = elapsed + + with open(self.log_path, "a") as f: + f.write(json.dumps({"time": round(elapsed, 3), + "objective_value": objective_value}) + "\n") diff --git a/bayani2024/solution_schema.json b/bayani2024/solution_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..146d51d313ba830aa039b4f5513d6be5d71a3b2b --- /dev/null +++ b/bayani2024/solution_schema.json @@ -0,0 +1,4 @@ +{ + "objective_value": " Total cost of the assignment, combining linear processing costs and all active quadratic interaction costs.", + "assignment": " Index of the server that each client is assigned to." +} diff --git a/belhoul2014/feasibility_check.py b/belhoul2014/feasibility_check.py new file mode 100644 index 0000000000000000000000000000000000000000..afe7870a6e908ddaf3c4c3bf3bca4d55d2f46aac --- /dev/null +++ b/belhoul2014/feasibility_check.py @@ -0,0 +1,483 @@ +#!/usr/bin/env python3 +""" +Feasibility checker for the Multi-Objective Assignment Problem (MOAP) / +Linearized Compromise Assignment Problem (LCAP) from Zheng et al. (2014). + +Checks all hard constraints listed in math_model.txt, numbered strictly +from top to bottom: + + Constraint 1 (MOAP-1): sum_j x_ij = 1, i=1,...,n + Constraint 2 (MOAP-2): sum_i x_ij = 1, j=1,...,n + Constraint 3 (MOAP-3): x_ij in {0,1} + Constraint 4 (CAP-1): sum_j x_ij = 1, i=1,...,n + Constraint 5 (CAP-2): sum_i x_ij = 1, j=1,...,n + Constraint 6 (CAP-3): x_ij in {0,1} + Constraint 7 (LCAP-1): mu >= lambda_k * (sum_ij c^k_ij x_ij - z_bar_k), k=1,...,p + Constraint 8 (LCAP-2): sum_j x_ij = 1, i=1,...,n + Constraint 9 (LCAP-3): sum_i x_ij = 1, j=1,...,n + Constraint 10 (LCAP-4): x_ij in {0,1} + Constraint 11 (LCAP-5): mu unrestricted + Constraint 12 (RLCAP-1): sum_j x_ij = 1, i=1,...,n + Constraint 13 (RLCAP-2): sum_i x_ij = 1, j=1,...,n + Constraint 14 (RLCAP-3): mu >= lambda_k * (sum_ij c^k_ij x_ij - z_bar_k), k=1,...,p + Constraint 15 (RLCAP-4): x_ij >= 0 + Constraint 16 (RLCAP-5): mu unrestricted + Constraint 17 (2a): u_i + v_j <= sum_k lambda_k c^k_ij omega_k (dual; skipped) + Constraint 18 (2b): sum_k omega_k = 1 (dual; skipped) + Constraint 19 (2c): omega_k > 0 (dual; skipped) + Constraint 20 (obj): objective_value == max_k lambda_k * (z_k - z_bar_k) + (Tier C: catches reported-objective fabrication; the + LCAP-1 inequality alone only rules out under-reporting, + so a candidate that inflates mu to sys.float_info.max + would otherwise slip through.) +""" + +import argparse +import json + +# FRONTIEROR_RELEASE_CONTRACT_V2 +_FRONTIEROR_REQUIRED_SOLUTION_FIELDS = ('assignment', 'assignment_matrix', 'objective_value') +_FRONTIEROR_NONEMPTY_SOLUTION_FIELDS = () +_FRONTIEROR_DERIVED_FIELD_RULES = () +_FRONTIEROR_CONDITIONAL_FIELD_RULES = () + + +def _frontieror_validate_solution_contract(solution): + """Return a structured rejection when the public solution contract is absent.""" + violations = [] + if not isinstance(solution, dict): + violations.append("solution must be a JSON object") + else: + missing = [ + field + for field in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS + if field not in solution or solution[field] is None + ] + if missing: + violations.append( + "missing required solution field(s): " + ", ".join(missing) + ) + + for discriminator, expected_value, fields in _FRONTIEROR_CONDITIONAL_FIELD_RULES: + if solution.get(discriminator) != expected_value: + continue + conditional_missing = [ + field + for field in fields + if field not in solution or solution[field] is None + ] + if conditional_missing: + violations.append( + "missing conditionally required solution field(s): " + + ", ".join(conditional_missing) + ) + + empty = [ + field + for field in _FRONTIEROR_NONEMPTY_SOLUTION_FIELDS + if field in solution and solution[field] in (None, [], {}) + ] + if empty: + violations.append( + "empty required decision witness field(s): " + ", ".join(empty) + ) + + for target, operation, source, source_key in _FRONTIEROR_DERIVED_FIELD_RULES: + try: + reported = float(solution[target]) + if operation == "length": + expected = float(len(solution[source])) + elif operation == "mapping_value": + expected = float(solution[source][source_key]) + else: + raise ValueError("unknown derived-field operation") + except (KeyError, TypeError, ValueError, OverflowError): + violations.append( + f"cannot derive {target} from decision witness field {source}" + ) + continue + if ( + reported != reported + or expected != expected + or reported in (float("inf"), float("-inf")) + or expected in (float("inf"), float("-inf")) + or abs(reported - expected) > 1e-6 + ): + violations.append( + f"{target}={reported} does not match the decision-derived " + f"value {expected}" + ) + + if "objective_value" in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS: + objective = solution.get("objective_value") + if ( + isinstance(objective, bool) + or not isinstance(objective, (int, float)) + or objective != objective + or objective in (float("inf"), float("-inf")) + ): + violations.append("objective_value must be a finite JSON number") + + if not violations: + return None + return { + "feasible": False, + "violated_constraints": ["solution_schema"], + "violations": violations, + "violation_magnitudes": [], + } + + +def _frontieror_write_unexpected_rejection(error): + """Turn malformed candidate/checker exceptions into a normal rejection.""" + import json as _frontieror_json + import sys as _frontieror_sys + + result_path = None + for index, argument in enumerate(_frontieror_sys.argv): + if argument == "--result_path" and index + 1 < len(_frontieror_sys.argv): + result_path = _frontieror_sys.argv[index + 1] + break + if argument.startswith("--result_path="): + result_path = argument.split("=", 1)[1] + break + if not result_path: + return False + try: + with open(result_path, "w") as result_handle: + _frontieror_json.dump( + { + "feasible": False, + "violated_constraints": ["malformed_solution"], + "violations": [ + "checker rejected malformed candidate: " + + type(error).__name__ + + ": " + + str(error)[:500] + ], + "violation_magnitudes": [], + }, + result_handle, + indent=2, + ) + except Exception: + return False + return True + + +TOL = 1e-5 +EPS = 1e-5 + + +def load_json(path): + with open(path) as f: + return json.load(f) + + +def build_assignment_matrix(assignment, n): + """Build n x n binary matrix from assignment list.""" + x = [[0] * n for _ in range(n)] + for i, j in enumerate(assignment): + x[i][j] = 1 + return x + + +def compute_objective_per_criterion(assignment, cost_matrices, n, p): + """z_k(x) = sum_i sum_j c^k_ij * x_ij for each k.""" + z = [0.0] * p + for k in range(p): + for i in range(n): + j = assignment[i] + z[k] += cost_matrices[k][i][j] + return z + + +def add_violation(violations_list, magnitudes_list, constraint_idx, msg, lhs, rhs): + """Record a constraint violation with normalized magnitude.""" + raw_excess = abs(lhs - rhs) + # For = constraints, raw_excess is |LHS - RHS| + # For >= constraints (mu >= rhs_val), raw_excess is max(0, rhs - lhs) + # Caller computes the appropriate raw_excess and passes lhs, rhs accordingly. + normalizer = max(abs(rhs), EPS) + ratio = raw_excess / normalizer + violations_list.append(msg) + magnitudes_list.append({ + "constraint": constraint_idx, + "lhs": lhs, + "rhs": rhs, + "raw_excess": raw_excess, + "normalizer": normalizer, + "ratio": ratio, + }) + + +def check_row_assignment(x_mat, n, constraint_idx, label): + """Check sum_j x_ij = 1 for all i (= constraint).""" + violated_msgs = [] + violated_mags = [] + for i in range(n): + row_sum = sum(x_mat[i][j] for j in range(n)) + diff = abs(row_sum - 1.0) + if diff > TOL: + add_violation( + violated_msgs, violated_mags, constraint_idx, + f"{label}: Row {i} sum = {row_sum}, expected 1", + row_sum, 1.0, + ) + return violated_msgs, violated_mags + + +def check_col_assignment(x_mat, n, constraint_idx, label): + """Check sum_i x_ij = 1 for all j (= constraint).""" + violated_msgs = [] + violated_mags = [] + for j in range(n): + col_sum = sum(x_mat[i][j] for i in range(n)) + diff = abs(col_sum - 1.0) + if diff > TOL: + add_violation( + violated_msgs, violated_mags, constraint_idx, + f"{label}: Column {j} sum = {col_sum}, expected 1", + col_sum, 1.0, + ) + return violated_msgs, violated_mags + + +def check_binary(x_mat, n, constraint_idx, label): + """Check x_ij in {0, 1} for all i, j.""" + violated_msgs = [] + violated_mags = [] + for i in range(n): + for j in range(n): + val = x_mat[i][j] + dist_to_nearest = min(abs(val - 0.0), abs(val - 1.0)) + if dist_to_nearest > TOL: + # LHS = distance from binary, RHS = 0 (should be zero distance) + add_violation( + violated_msgs, violated_mags, constraint_idx, + f"{label}: x[{i}][{j}] = {val}, not binary", + dist_to_nearest, 0.0, + ) + return violated_msgs, violated_mags + + +def check_lcap_linearization(mu, z_vals, lam, z_bar, p, constraint_idx, label): + """Check mu >= lambda_k * (z_k - z_bar_k) for all k (>= constraint).""" + violated_msgs = [] + violated_mags = [] + for k in range(p): + rhs_val = lam[k] * (z_vals[k] - z_bar[k]) + violation_amount = rhs_val - mu # positive if mu < rhs_val + if violation_amount > TOL: + add_violation( + violated_msgs, violated_mags, constraint_idx, + f"{label}: mu ({mu:.6f}) < lambda_{k}*(z_{k} - z_bar_{k}) = {rhs_val:.6f}", + mu, rhs_val, + ) + return violated_msgs, violated_mags + + +def check_nonnegativity(x_mat, n, constraint_idx, label): + """Check x_ij >= 0 for all i, j (>= constraint).""" + violated_msgs = [] + violated_mags = [] + for i in range(n): + for j in range(n): + val = x_mat[i][j] + if val < -TOL: + violation_amount = -val + add_violation( + violated_msgs, violated_mags, constraint_idx, + f"{label}: x[{i}][{j}] = {val} < 0", + val, 0.0, + ) + return violated_msgs, violated_mags + + +def run_feasibility_check(instance, solution): + n = instance["n"] + p = instance["p"] + cost_matrices = instance["cost_matrices"] + z_bar = instance["reference_point"] + lam = instance["search_direction_lambda"] + + assignment = solution["assignment"] + + # Build assignment matrix + x_mat = build_assignment_matrix(assignment, n) + + # Use assignment_matrix from solution if available (for consistency check) + if "assignment_matrix" in solution: + x_mat_sol = solution["assignment_matrix"] + # Check consistency between assignment list and matrix + for i in range(n): + for j in range(n): + if x_mat[i][j] != x_mat_sol[i][j]: + # Use the assignment list as ground truth + pass + + # Compute objective values per criterion from the assignment + z_vals = compute_objective_per_criterion(assignment, cost_matrices, n, p) + + # Determine mu (objective_value) + mu = solution.get("objective_value") + if mu is None: + # Compute from the achievement function + mu = max(lam[k] * (z_vals[k] - z_bar[k]) for k in range(p)) + + violated_constraints = set() + all_msgs = [] + all_mags = [] + + # Helper to accumulate results + def accumulate(constraint_idx, msgs, mags): + if msgs: + violated_constraints.add(constraint_idx) + all_msgs.extend(msgs) + all_mags.extend(mags) + + # --- Constraint 1 (MOAP-1): sum_j x_ij = 1 for all i --- + msgs, mags = check_row_assignment(x_mat, n, 1, "MOAP-1") + accumulate(1, msgs, mags) + + # --- Constraint 2 (MOAP-2): sum_i x_ij = 1 for all j --- + msgs, mags = check_col_assignment(x_mat, n, 2, "MOAP-2") + accumulate(2, msgs, mags) + + # --- Constraint 3 (MOAP-3): x_ij in {0,1} --- + msgs, mags = check_binary(x_mat, n, 3, "MOAP-3") + accumulate(3, msgs, mags) + + # --- Constraint 4 (CAP-1): sum_j x_ij = 1 for all i --- + msgs, mags = check_row_assignment(x_mat, n, 4, "CAP-1") + accumulate(4, msgs, mags) + + # --- Constraint 5 (CAP-2): sum_i x_ij = 1 for all j --- + msgs, mags = check_col_assignment(x_mat, n, 5, "CAP-2") + accumulate(5, msgs, mags) + + # --- Constraint 6 (CAP-3): x_ij in {0,1} --- + msgs, mags = check_binary(x_mat, n, 6, "CAP-3") + accumulate(6, msgs, mags) + + # --- Constraint 7 (LCAP-1): mu >= lambda_k*(z_k - z_bar_k) for all k --- + msgs, mags = check_lcap_linearization(mu, z_vals, lam, z_bar, p, 7, "LCAP-1") + accumulate(7, msgs, mags) + + # --- Constraint 8 (LCAP-2): sum_j x_ij = 1 for all i --- + msgs, mags = check_row_assignment(x_mat, n, 8, "LCAP-2") + accumulate(8, msgs, mags) + + # --- Constraint 9 (LCAP-3): sum_i x_ij = 1 for all j --- + msgs, mags = check_col_assignment(x_mat, n, 9, "LCAP-3") + accumulate(9, msgs, mags) + + # --- Constraint 10 (LCAP-4): x_ij in {0,1} --- + msgs, mags = check_binary(x_mat, n, 10, "LCAP-4") + accumulate(10, msgs, mags) + + # --- Constraint 11 (LCAP-5): mu unrestricted --- + # Trivially satisfied for any real mu; no check needed. + + # --- Constraints 12-16 (RLCAP-1..RLCAP-5) SKIPPED --- + # These are the LP-relaxation (RLCAP) constraints. For any binary x + # that satisfies the LCAP constraints (assignment 6, 7; binary 10), + # the relaxation is automatically satisfied (row/col assignment is + # identical; x_ij >= 0 is weaker than x_ij in {0,1}). Checking them + # only duplicates the LCAP checks above. + # + # --- Constraints 17-19 (Dual: 2a, 2b, 2c) SKIPPED --- + # Dual variables (u, v, omega) are not part of the primal solution + # structure and not produced by a generated algorithm. + + # --- Constraint 20 (obj consistency): mu == max_k lambda_k*(z_k - z_bar_k) --- + # The LCAP linearization in constraint 7 only enforces mu >= achievement, + # so a candidate could pass by reporting an arbitrarily large mu. The + # actual achievement-function value at the chosen assignment is + # max_k lambda_k*(z_k - z_bar_k); the reported objective_value must + # equal it within tolerance. All variables that determine the achievement + # function (the assignment) are present in the solution, so we perform + # a full recompute rather than a lower-bound check. + reported_obj = solution.get("objective_value") + if reported_obj is not None: + try: + reported = float(reported_obj) + except (TypeError, ValueError): + reported = None + if reported is not None: + true_obj = max(lam[k] * (z_vals[k] - z_bar[k]) for k in range(p)) + abs_diff = abs(reported - true_obj) + # 0.1% relative tolerance with 1e-3 absolute floor + tol = max(1e-3, 1e-3 * abs(true_obj)) + if abs_diff > tol: + add_violation( + all_msgs, all_mags, 20, + f"Objective consistency violated: reported objective_value=" + f"{reported} differs from recomputed " + f"max_k lambda_k*(z_k - z_bar_k)={true_obj} " + f"(|diff|={abs_diff:.3g}, tol={tol:.3g})", + reported, true_obj, + ) + violated_constraints.add(20) + + feasible = len(violated_constraints) == 0 + result = { + "feasible": feasible, + "violated_constraints": sorted(violated_constraints), + "violations": all_msgs, + "violation_magnitudes": all_mags, + } + return result + + +def main(): + parser = argparse.ArgumentParser( + description="Feasibility checker for MOAP/LCAP (Zheng et al. 2014)" + ) + parser.add_argument( + "--instance_path", type=str, required=True, + help="Path to the JSON instance file", + ) + parser.add_argument( + "--solution_path", type=str, required=True, + help="Path to the JSON solution file", + ) + parser.add_argument( + "--result_path", type=str, required=True, + help="Path to write the JSON feasibility result", + ) + args = parser.parse_args() + + instance = load_json(args.instance_path) + solution = load_json(args.solution_path) + _frontieror_contract_result = _frontieror_validate_solution_contract(solution) + if _frontieror_contract_result is not None: + with open(args.result_path, "w") as _frontieror_result_handle: + json.dump( + _frontieror_contract_result, + _frontieror_result_handle, + indent=2, + ) + return + + result = run_feasibility_check(instance, solution) + + with open(args.result_path, "w") as f: + json.dump(result, f, indent=2) + + if result["feasible"]: + print("FEASIBLE: All hard constraints satisfied.") + else: + print(f"INFEASIBLE: Violated constraints: {result['violated_constraints']}") + for msg in result["violations"]: + print(f" - {msg}") + + +if __name__ == "__main__": + try: + main() + except Exception as _frontieror_unexpected_error: + if not _frontieror_write_unexpected_rejection( + _frontieror_unexpected_error + ): + raise diff --git a/belhoul2014/gurobi_code.py b/belhoul2014/gurobi_code.py new file mode 100644 index 0000000000000000000000000000000000000000..b8b3a7c1cfad25b88c9070f34d5da5ba52930ea1 --- /dev/null +++ b/belhoul2014/gurobi_code.py @@ -0,0 +1,193 @@ +#!/usr/bin/env python3 +""" +Gurobi implementation of the Linearized Compromise Assignment Problem (LCAP). + +Based on: Zheng et al. (2014), multi-objective assignment problem scalarized +using the weighted Tchebychev (achievement) function. + +The LCAP minimizes the maximum weighted deviation from a reference point: + + min mu + s.t. mu >= lambda_k * (sum_{i,j} c^k_{ij} * x_{ij} - z_bar_k) for k = 1,...,p + sum_j x_{ij} = 1 for i = 1,...,n + sum_i x_{ij} = 1 for j = 1,...,n + x_{ij} in {0,1} + mu unrestricted +""" + +import argparse +import json + +import gurobipy as gp +from gurobipy import GRB +import os as _os, sys as _sys +# Walk up from this file's directory to find repo root (containing scripts/). +_repo = _os.path.dirname(_os.path.abspath(__file__)) +while _repo != _os.path.dirname(_repo) and not _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _repo = _os.path.dirname(_repo) +if _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _sys.path.insert(0, _repo) +try: + from scripts.utils.gurobi_log_helper import install_gurobi_logger +except ImportError: + def install_gurobi_logger(log_path): # no-op fallback when scripts/ unavailable + pass + + +def load_instance(path): + """Load an LCAP instance from JSON.""" + with open(path, "r") as f: + data = json.load(f) + return data + + +def solve_lcap(instance_path, solution_path, time_limit): + """Build and solve the LCAP model using Gurobi.""" + data = load_instance(instance_path) + + n = data["n"] + p = data["p"] + cost_matrices = data["cost_matrices"] # p matrices, each n x n + z_bar = data["reference_point"] # length p + lam = data["search_direction_lambda"] # length p + + print(f"LCAP Instance: n={n}, p={p}") + print(f"Reference point: {z_bar}") + print(f"Search direction (lambda): {lam}") + print(f"Time limit: {time_limit} seconds") + + # ---- Build model ---- + model = gp.Model("LCAP") + model.setParam("Threads", 1) + model.setParam("TimeLimit", time_limit) + model.setParam("OutputFlag", 1) + + # Decision variables + # x[i,j] binary assignment variables + x = {} + for i in range(n): + for j in range(n): + x[i, j] = model.addVar(vtype=GRB.BINARY, name=f"x_{i}_{j}") + + # mu: unrestricted continuous variable (the Tchebychev objective) + mu = model.addVar(lb=-GRB.INFINITY, vtype=GRB.CONTINUOUS, name="mu") + + model.update() + + # Objective: minimize mu + model.setObjective(mu, GRB.MINIMIZE) + + # Assignment constraints: each row assigned to exactly one column + for i in range(n): + model.addConstr( + gp.quicksum(x[i, j] for j in range(n)) == 1, + name=f"row_{i}" + ) + + # Assignment constraints: each column assigned to exactly one row + for j in range(n): + model.addConstr( + gp.quicksum(x[i, j] for i in range(n)) == 1, + name=f"col_{j}" + ) + + # Linearization constraints: + # mu >= lambda_k * (sum_{i,j} c^k_{ij} * x_{ij} - z_bar_k) for each k + for k in range(p): + c_k = cost_matrices[k] + model.addConstr( + mu >= lam[k] * ( + gp.quicksum(c_k[i][j] * x[i, j] for i in range(n) for j in range(n)) + - z_bar[k] + ), + name=f"tcheby_{k}" + ) + + model.update() + + # ---- Solve ---- + model.optimize() + + # ---- Extract solution ---- + result = { + "problem": "LCAP", + "instance": instance_path, + "instance_id": data.get("instance_id"), + } + + if model.SolCount > 0: + obj_val = model.ObjVal + result["objective_value"] = obj_val + result["status"] = "optimal" if model.Status == GRB.OPTIMAL else "feasible" + result["gap"] = model.MIPGap if hasattr(model, "MIPGap") else 0.0 + + # Extract the assignment matrix and permutation + assignment_matrix = [[0] * n for _ in range(n)] + assignment = [] # assignment[i] = j means row i assigned to column j + for i in range(n): + for j in range(n): + if x[i, j].X > 0.5: + assignment_matrix[i][j] = 1 + assignment.append(j) + + result["assignment"] = assignment + result["assignment_matrix"] = assignment_matrix + + # Compute objective value for each criterion + objective_values_per_criterion = [] + for k in range(p): + c_k = cost_matrices[k] + val = sum( + c_k[i][assignment[i]] for i in range(n) + ) + objective_values_per_criterion.append(val) + result["objective_values_per_criterion"] = objective_values_per_criterion + + # Compute weighted deviations for verification + weighted_deviations = [] + for k in range(p): + dev = lam[k] * (objective_values_per_criterion[k] - z_bar[k]) + weighted_deviations.append(dev) + result["weighted_deviations"] = weighted_deviations + else: + result["objective_value"] = None + result["status"] = "infeasible_or_no_solution" + + result["solve_time"] = model.Runtime + + # Write solution + with open(solution_path, "w") as f: + json.dump(result, f, indent=2) + + print(f"\nSolution written to {solution_path}") + if model.SolCount > 0: + print(f"Objective value (mu): {result['objective_value']}") + print(f"Status: {result['status']}") + print(f"Assignment: {result['assignment']}") + print(f"Objective values per criterion: {result['objective_values_per_criterion']}") + print(f"Weighted deviations: {result['weighted_deviations']}") + + return result + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description="Solve the Linearized Compromise Assignment Problem (LCAP) using Gurobi" + ) + parser.add_argument( + "--instance_path", type=str, required=True, + help="Path to the JSON instance file" + ) + parser.add_argument( + "--solution_path", type=str, default="gurobi_solution_1.json", + help="Path for the output solution JSON file" + ) + parser.add_argument( + "--time_limit", type=int, default=3600, + help="Maximum solver runtime in seconds" + ) + parser.add_argument("--log_path", type=str, default=None, help="Path to log incumbent solutions") + args = parser.parse_args() + install_gurobi_logger(args.log_path) + + solve_lcap(args.instance_path, args.solution_path, args.time_limit) diff --git a/belhoul2014/gurobi_feasi_result/large_feasi_result_1.json b/belhoul2014/gurobi_feasi_result/large_feasi_result_1.json new file mode 100644 index 0000000000000000000000000000000000000000..e305b82c3d6732211d5033415df1a105476e9cf1 --- /dev/null +++ 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sha256:c68e1d6ec3ec10879fa0e1a8f52822f66ceaa54ea9801616aa4db316915930e3 +size 30288196 diff --git a/belhoul2014/instance/tiny_instance.json b/belhoul2014/instance/tiny_instance.json new file mode 100644 index 0000000000000000000000000000000000000000..1da8ec89896a7cbd46fe714146c33a47b59dbeca --- /dev/null +++ b/belhoul2014/instance/tiny_instance.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c8e8cafce722cd91bf6af7cb23aa7c1557a84cd2e871ebdeaa3f2a05808c9aec +size 3147 diff --git a/belhoul2014/instance_schema.json b/belhoul2014/instance_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..d896d23b04184dca70e1ec5607ab599768bac43f --- /dev/null +++ b/belhoul2014/instance_schema.json @@ -0,0 +1,9 @@ +{ + "n": " Number of tasks (and agents) to be matched one-to-one.", + "p": " Number of distinct performance criteria evaluated for each assignment.", + "cost_matrices": " Cost of assigning task i to agent j under criterion k.", + "ideal_point": " Best achievable total cost for each criterion when that criterion is optimized independently.", + "nadir_approximation": " Worst total cost observed for each criterion across the set of individually optimal solutions.", + "reference_point": " Target total cost for each criterion that the assignment should aim toward.", + "search_direction_lambda": " Weight controlling how deviations from the target are scaled for each criterion." +} diff --git a/belhoul2014/mathematical_formulation.md b/belhoul2014/mathematical_formulation.md new file mode 100644 index 0000000000000000000000000000000000000000..e423eec8d799b1f4bbd0a9b8b8cc0703bb25a206 --- /dev/null +++ b/belhoul2014/mathematical_formulation.md @@ -0,0 +1,41 @@ +# Sets and Indices + +- $n$: number of tasks (equal to the number of agents); $i, j \in \{1,\ldots,n\}$. + +- $p$: number of objectives, indexed by $k = 1,\ldots,p$. + +# Parameters + +- $c^k_{ij} \in \mathbb{Z}_+$: cost of assigning task $i$ to agent $j$ with respect to objective $k$. + +- $\lambda = (\lambda_1,\ldots,\lambda_p) \in \mathbb{R}^p_{>0}$: strictly positive weighting vector representing the decision-maker’s search direction. + +- $\bar{z} = (\bar{z}_1,\ldots,\bar{z}_p) \in \mathbb{R}^p$: reference point (e.g. the ideal point $z^*$). + +# Decision Variables + +- $x_{ij} \in \{0,1\}$: equals $1$ if task $i$ is assigned to agent $j$, $0$ otherwise, for $i,j = 1,\ldots,n$. + +- $\mu$: unrestricted scalar variable (epigraph of the Tchebychev objective). + +# Compromise Assignment Problem (CAP) + +The Compromise Assignment Problem uses the weighted Tchebychev / achievement scalarizing function: $$\begin{align} + \min \quad & \max_{k=1,\ldots,p}\ \bigl\{\lambda_k\!\left(\sum_{i=1}^{n}\sum_{j=1}^{n} c^k_{ij}\, x_{ij} - \bar{z}_k\right)\bigr\} \tag{CAP} \\ + \text{s.t.}\quad + & \sum_{j=1}^{n} x_{ij} = 1, && i = 1,\ldots,n \notag \\ + & \sum_{i=1}^{n} x_{ij} = 1, && j = 1,\ldots,n \notag \\ + & x_{ij} \in \{0,1\}, && i,j = 1,\ldots,n \notag +\end{align}$$ + +# Linearized Compromise Assignment Problem (LCAP) + +The paper linearizes (CAP) by introducing a scalar variable $\mu$, yielding the mixed-integer linear program: $$\begin{align} + \min \quad & \mu \tag{LCAP-1} \\ + \text{s.t.}\quad + & \mu \;\ge\; \lambda_k\!\left(\sum_{i=1}^{n}\sum_{j=1}^{n} c^k_{ij}\, x_{ij} - \bar{z}_k\right), && k = 1,\ldots,p \tag{LCAP-2} \\ + & \sum_{j=1}^{n} x_{ij} = 1, && i = 1,\ldots,n \tag{LCAP-3} \\ + & \sum_{i=1}^{n} x_{ij} = 1, && j = 1,\ldots,n \tag{LCAP-4} \\ + & x_{ij} \in \{0,1\}, && i, j = 1,\ldots,n \notag \\ + & \mu \text{ unrestricted}. \notag +\end{align}$$ diff --git a/belhoul2014/problem_description.txt b/belhoul2014/problem_description.txt new file mode 100644 index 0000000000000000000000000000000000000000..6e43b00d87b1625a5096f995dc3f8d0d594e48f9 --- /dev/null +++ b/belhoul2014/problem_description.txt @@ -0,0 +1,7 @@ +# Problem Description + +A company must assign n tasks to n agents on a one-to-one basis, so that every task is performed by exactly one agent and every agent performs exactly one task. There are p distinct performance criteria, and for each criterion the cost of assigning any particular task to any particular agent is a given non-negative integer. The input therefore specifies the number of tasks and agents (n), the number of criteria (p), one n-by-n cost matrix per criterion, a reference point consisting of one real-valued target per criterion, and a search direction consisting of one strictly positive weight per criterion. + +For a given assignment, the total cost under each criterion is the sum of the individual assignment costs for that criterion across all task-agent pairs that are selected. The goal is to find the assignment that best compromises among the p criteria, where the quality of a compromise is measured as follows: for each criterion, compute the product of its search-direction weight and the difference between the assignment's total cost on that criterion and the corresponding reference-point value, then take the maximum of these p products. The assignment to be found is the one that minimizes this worst-case weighted deviation. + +To express this as a single problem, an auxiliary value representing the worst-case weighted deviation is introduced. This value is unrestricted in sign and must be at least as large as, for every criterion, the product of that criterion's search-direction weight and the difference between the assignment's total cost on that criterion and the reference-point value for that criterion. Each task must be assigned to exactly one agent, and each agent must be assigned exactly one task. The goal is to minimize the worst-case weighted deviation value. diff --git a/belhoul2014/solution_logger.py b/belhoul2014/solution_logger.py new file mode 100644 index 0000000000000000000000000000000000000000..5393fa935ce71df9da95cee04fe5199077cb8461 --- /dev/null +++ b/belhoul2014/solution_logger.py @@ -0,0 +1,61 @@ +""" +Convergence logger for optimization algorithms. + +Records incumbent solutions with timestamps to a JSONL file. +This module is provided to LLM-generated programs — they only need to call +`log(objective_value)` whenever a better feasible solution is found. + +Usage in generated code: + from solution_logger import SolutionLogger + logger = SolutionLogger(log_path, sense="minimize") # or "maximize" + # ... inside algorithm loop: + logger.log(objective_value) +""" + +import json +import time + + +class SolutionLogger: + def __init__(self, log_path, sense="minimize"): + """ + Args: + log_path: Path to the JSONL output file. + sense: "minimize" or "maximize". + """ + self.log_path = log_path + self.sense = sense + self.start_time = time.time() + self.best_obj = None + self.min_interval = 0.1 # seconds, avoid excessive writes + + self._last_log_time = 0.0 + # Clear the file + with open(self.log_path, "w") as f: + pass + + def log(self, objective_value): + """Record a new incumbent if it improves on the best known.""" + if objective_value is None: + return + + # Check if this is an improvement + if self.best_obj is not None: + if self.sense == "minimize" and objective_value >= self.best_obj: + return + if self.sense == "maximize" and objective_value <= self.best_obj: + return + + elapsed = time.time() - self.start_time + + # Throttle writes + if self.best_obj is not None and elapsed - self._last_log_time < self.min_interval: + self.best_obj = objective_value + return + + self.best_obj = objective_value + self._last_log_time = elapsed + + with open(self.log_path, "a") as f: + f.write(json.dumps({"time": round(elapsed, 3), + "objective_value": objective_value}) + "\n") diff --git a/belhoul2014/solution_schema.json b/belhoul2014/solution_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..9ff8c8b63231395109ce4ab2ef0b41fb54ac070b --- /dev/null +++ b/belhoul2014/solution_schema.json @@ -0,0 +1,5 @@ +{ + "objective_value": " Worst-case weighted deviation of the chosen assignment's total costs from the reference point across all criteria.", + "assignment": " Agent assigned to each task, where entry i gives the agent performing task i.", + "assignment_matrix": " One-to-one pairing between tasks and agents (1 if task i is assigned to agent j, 0 otherwise)." +} diff --git a/belvaux2000/feasibility_check.py b/belvaux2000/feasibility_check.py new file mode 100644 index 0000000000000000000000000000000000000000..766db3d2a6098255f16ce640913ab025c31c497d --- /dev/null +++ b/belvaux2000/feasibility_check.py @@ -0,0 +1,533 @@ +""" +Feasibility checker for the Con-NT Small Bucket Lot-Sizing Model. + +From: Belvaux & Wolsey (2000), "bc-prod: A Specialized Branch-and-Cut System +for Lot-Sizing Problems", Management Science 46(5):724-738. + +Checks all hard constraints from the mathematical formulation (math_model.txt), +numbered strictly top-to-bottom in the formulation section. + +Constraints in the formulation section (top to bottom): + Constraint 1: Flow balance for intermediate products (multilevel only) + Constraint 2: Flow conservation for final products (generic) + Constraint 3: Capacity / variable upper bound (generic) + Constraint 4: Minimum production (generic, only if L^{ik} > 0) + Constraint 5: Start-up / switch-off linking (generic) + Constraint 6: Variable domains (generic): x, s, r >= 0; y, z, w in {0,1} + Constraint 7: Single set-up per machine per period (additional linking) + Constraint 8: Machine capacity constraint (additional linking) + -- Example 1 (set1ch) constraints -- + Constraint 9: Flow balance for set1ch (eq 6) + Constraint 10: Capacity for set1ch (eq 7) + Constraint 11: Machine capacity for set1ch (eq 8) + Constraint 12: Variable domains for set1ch + -- Example 2 (Con-NT) constraints -- + Constraint 13: Flow balance (eq 9) + Constraint 14: Capacity with startup (eq 10) + Constraint 15: Startup definition (eq 11) + Constraint 16: One item per machine per period (eq 12) + Constraint 17: Startup bound z <= y (eq 13) + Constraint 18: Linking constraint (eq 14) + Constraint 19: Variable domains for Con-NT: s, r, x >= 0; y, z in {0,1}; s0=r0=0 + Constraint 20: Objective consistency (Tier C anti-exploit check) -- + reported objective_value must equal recomputed + sum_{i,k,t}(f*y + g*z) + sum_{i,t}(h_i*s + e_i*r). + +The instances are Con-NT (Example 2), so constraints 13-19 are the active ones. +Constraints 1-5 are generic templates; constraints 13-18 are the Con-NT specializations. +Constraint 6 variable domains are also checked via Constraint 19's specialization. +""" + +import json +import argparse +import math + +# FRONTIEROR_RELEASE_CONTRACT_V2 +_FRONTIEROR_REQUIRED_SOLUTION_FIELDS = ('backlog', 'objective_value', 'production', 'setup', 'startup', 'stock') +_FRONTIEROR_NONEMPTY_SOLUTION_FIELDS = () +_FRONTIEROR_DERIVED_FIELD_RULES = () +_FRONTIEROR_CONDITIONAL_FIELD_RULES = () + + +def _frontieror_validate_solution_contract(solution): + """Return a structured rejection when the public solution contract is absent.""" + violations = [] + if not isinstance(solution, dict): + violations.append("solution must be a JSON object") + else: + missing = [ + field + for field in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS + if field not in solution or solution[field] is None + ] + if missing: + violations.append( + "missing required solution field(s): " + ", ".join(missing) + ) + + for discriminator, expected_value, fields in _FRONTIEROR_CONDITIONAL_FIELD_RULES: + if solution.get(discriminator) != expected_value: + continue + conditional_missing = [ + field + for field in fields + if field not in solution or solution[field] is None + ] + if conditional_missing: + violations.append( + "missing conditionally required solution field(s): " + + ", ".join(conditional_missing) + ) + + empty = [ + field + for field in _FRONTIEROR_NONEMPTY_SOLUTION_FIELDS + if field in solution and solution[field] in (None, [], {}) + ] + if empty: + violations.append( + "empty required decision witness field(s): " + ", ".join(empty) + ) + + for target, operation, source, source_key in _FRONTIEROR_DERIVED_FIELD_RULES: + try: + reported = float(solution[target]) + if operation == "length": + expected = float(len(solution[source])) + elif operation == "mapping_value": + expected = float(solution[source][source_key]) + else: + raise ValueError("unknown derived-field operation") + except (KeyError, TypeError, ValueError, OverflowError): + violations.append( + f"cannot derive {target} from decision witness field {source}" + ) + continue + if ( + reported != reported + or expected != expected + or reported in (float("inf"), float("-inf")) + or expected in (float("inf"), float("-inf")) + or abs(reported - expected) > 1e-6 + ): + violations.append( + f"{target}={reported} does not match the decision-derived " + f"value {expected}" + ) + + if "objective_value" in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS: + objective = solution.get("objective_value") + if ( + isinstance(objective, bool) + or not isinstance(objective, (int, float)) + or objective != objective + or objective in (float("inf"), float("-inf")) + ): + violations.append("objective_value must be a finite JSON number") + + if not violations: + return None + return { + "feasible": False, + "violated_constraints": ["solution_schema"], + "violations": violations, + "violation_magnitudes": [], + } + + +def _frontieror_write_unexpected_rejection(error): + """Turn malformed candidate/checker exceptions into a normal rejection.""" + import json as _frontieror_json + import sys as _frontieror_sys + + result_path = None + for index, argument in enumerate(_frontieror_sys.argv): + if argument == "--result_path" and index + 1 < len(_frontieror_sys.argv): + result_path = _frontieror_sys.argv[index + 1] + break + if argument.startswith("--result_path="): + result_path = argument.split("=", 1)[1] + break + if not result_path: + return False + try: + with open(result_path, "w") as result_handle: + _frontieror_json.dump( + { + "feasible": False, + "violated_constraints": ["malformed_solution"], + "violations": [ + "checker rejected malformed candidate: " + + type(error).__name__ + + ": " + + str(error)[:500] + ], + "violation_magnitudes": [], + }, + result_handle, + indent=2, + ) + except Exception: + return False + return True + + +TOL = 1e-5 +EPS = 1e-5 + + +def load_json(path): + with open(path, "r") as f: + return json.load(f) + + +def get_val(dct, key, default=0.0): + """Get a value from a sparse dict (solution), defaulting to 0.""" + return dct.get(key, default) + + +def record_violation(violations_list, magnitudes_list, constraint_idx, lhs, rhs, message): + """Record a constraint violation with normalized magnitude.""" + raw_excess = abs(lhs - rhs) + normalizer = max(abs(rhs), EPS) + ratio = raw_excess / normalizer + violations_list.append(message) + magnitudes_list.append({ + "constraint": constraint_idx, + "lhs": float(lhs), + "rhs": float(rhs), + "raw_excess": float(raw_excess), + "normalizer": float(normalizer), + "ratio": float(ratio), + }) + + +def check_feasibility(instance, solution): + NI = instance["parameters"]["NI"] + NK = instance["parameters"]["NK"] + NT = instance["parameters"]["NT"] + + C = instance["machine_data"]["capacity_C"] + sigma = instance["machine_data"]["startup_time_sigma"] + demand = instance["demand"] + s0 = instance["initial_conditions"]["initial_stock_s0"] + r0 = instance["initial_conditions"]["initial_backlog_r0"] + + f_setup = instance["costs"]["setup_cost_f"] + g_startup = instance["costs"]["startup_cost_g"] + h_hold = instance["costs"]["holding_cost_h"] + e_back = instance["costs"]["backlogging_cost_e"] + + prod = solution.get("production", {}) + stock = solution.get("stock", {}) + backlog = solution.get("backlog", {}) + setup = solution.get("setup", {}) + startup = solution.get("startup", {}) + + # Helper to extract variable values from sparse solution dicts + def x(i, k, t): + return get_val(prod, f"x_{i}_{k}_{t}") + + def s(i, t): + return get_val(stock, f"s_{i}_{t}") + + def r(i, t): + return get_val(backlog, f"r_{i}_{t}") + + def y(i, k, t): + return get_val(setup, f"y_{i}_{k}_{t}") + + def z(i, k, t): + return get_val(startup, f"z_{i}_{k}_{t}") + + violations = [] + magnitudes = [] + violated_constraints = set() + + # ========================================================================= + # Constraint 13 (paper eq 9): Flow balance for final products (Con-NT) + # s_{i,t-1} - r_{i,t-1} + sum_k x_{ikt} = d_{it} + s_{it} - r_{it} + # for all i, t + # This is an equality constraint. + # ========================================================================= + for i in range(NI): + for t in range(NT): + s_prev = s0[i] if t == 0 else s(i, t - 1) + r_prev = r0[i] if t == 0 else r(i, t - 1) + + lhs = s_prev - r_prev + sum(x(i, k, t) for k in range(NK)) + rhs = demand[i][t] + s(i, t) - r(i, t) + + violation_amount = abs(lhs - rhs) + if violation_amount > TOL: + violated_constraints.add(13) + record_violation( + violations, magnitudes, 13, lhs, rhs, + f"Constraint 13 (flow balance eq 9) violated for item {i}, period {t}: " + f"LHS={lhs:.6f}, RHS={rhs:.6f}, diff={violation_amount:.6f}" + ) + + # ========================================================================= + # Constraint 14 (paper eq 10): Capacity with startup + # x_{ikt} + sigma_k * z_{ikt} <= C_k * y_{ikt} + # for all i, k, t + # This is a <= constraint. + # ========================================================================= + for i in range(NI): + for k in range(NK): + for t in range(NT): + lhs = x(i, k, t) + sigma[k] * z(i, k, t) + rhs = C[k] * y(i, k, t) + + violation_amount = lhs - rhs + if violation_amount > TOL: + violated_constraints.add(14) + record_violation( + violations, magnitudes, 14, lhs, rhs, + f"Constraint 14 (capacity eq 10) violated for item {i}, machine {k}, period {t}: " + f"x={x(i,k,t):.4f} + sigma*z={sigma[k]*z(i,k,t):.4f} = {lhs:.4f} > " + f"C*y={rhs:.4f}" + ) + + # ========================================================================= + # Constraint 15 (paper eq 11): Startup definition + # z_{ikt} >= y_{ikt} - y_{ik,t-1} + # for all i, k, t + # This is a >= constraint: z >= y_curr - y_prev + # Equivalently: violation if y_curr - y_prev - z > 0 + # ========================================================================= + for i in range(NI): + for k in range(NK): + for t in range(NT): + y_prev = 0 if t == 0 else y(i, k, t - 1) + lhs = z(i, k, t) + rhs = y(i, k, t) - y_prev + + violation_amount = rhs - lhs # >= constraint: RHS exceeds LHS + if violation_amount > TOL: + violated_constraints.add(15) + record_violation( + violations, magnitudes, 15, lhs, rhs, + f"Constraint 15 (startup def eq 11) violated for item {i}, machine {k}, period {t}: " + f"z={z(i,k,t):.4f} < y_curr - y_prev = {y(i,k,t):.4f} - {y_prev:.4f} = {rhs:.4f}" + ) + + # ========================================================================= + # Constraint 16 (paper eq 12): One item per machine per period + # sum_i y_{ikt} <= 1 for all k, t + # This is a <= constraint. + # ========================================================================= + for k in range(NK): + for t in range(NT): + lhs = sum(y(i, k, t) for i in range(NI)) + rhs = 1.0 + + violation_amount = lhs - rhs + if violation_amount > TOL: + violated_constraints.add(16) + record_violation( + violations, magnitudes, 16, lhs, rhs, + f"Constraint 16 (one setup eq 12) violated for machine {k}, period {t}: " + f"sum_i y_ikt = {lhs:.4f} > 1" + ) + + # ========================================================================= + # Constraint 17 (paper eq 13): Startup bound + # z_{ikt} <= y_{ikt} for all i, k, t + # This is a <= constraint. + # ========================================================================= + for i in range(NI): + for k in range(NK): + for t in range(NT): + lhs = z(i, k, t) + rhs = y(i, k, t) + + violation_amount = lhs - rhs + if violation_amount > TOL: + violated_constraints.add(17) + record_violation( + violations, magnitudes, 17, lhs, rhs, + f"Constraint 17 (z<=y eq 13) violated for item {i}, machine {k}, period {t}: " + f"z={z(i,k,t):.4f} > y={y(i,k,t):.4f}" + ) + + # ========================================================================= + # Constraint 18 (paper eq 14): Linking constraint + # y_{ik,t-1} + z_{ikt} + sum_{j!=i} (y_{jkt} - z_{jkt}) <= 1 + # for all i, k, t + # This is a <= constraint. + # ========================================================================= + for i in range(NI): + for k in range(NK): + for t in range(NT): + y_prev_i = 0 if t == 0 else y(i, k, t - 1) + lhs = y_prev_i + z(i, k, t) + for j in range(NI): + if j != i: + lhs += y(j, k, t) - z(j, k, t) + rhs = 1.0 + + violation_amount = lhs - rhs + if violation_amount > TOL: + violated_constraints.add(18) + record_violation( + violations, magnitudes, 18, lhs, rhs, + f"Constraint 18 (linking eq 14) violated for item {i}, machine {k}, period {t}: " + f"LHS={lhs:.4f} > 1" + ) + + # ========================================================================= + # Constraint 19: Variable domains for Con-NT + # s, r, x >= 0; y, z in {0, 1}; s^i_0 = r^i_0 = 0 + # ========================================================================= + + # Check non-negativity of continuous variables (x, s, r >= 0) + for i in range(NI): + for k in range(NK): + for t in range(NT): + val = x(i, k, t) + if val < -TOL: + violated_constraints.add(19) + record_violation( + violations, magnitudes, 19, val, 0.0, + f"Constraint 19 (domain) violated: x_{i}_{k}_{t} = {val:.6f} < 0" + ) + + for i in range(NI): + for t in range(NT): + val_s = s(i, t) + if val_s < -TOL: + violated_constraints.add(19) + record_violation( + violations, magnitudes, 19, val_s, 0.0, + f"Constraint 19 (domain) violated: s_{i}_{t} = {val_s:.6f} < 0" + ) + val_r = r(i, t) + if val_r < -TOL: + violated_constraints.add(19) + record_violation( + violations, magnitudes, 19, val_r, 0.0, + f"Constraint 19 (domain) violated: r_{i}_{t} = {val_r:.6f} < 0" + ) + + # Check binary variables y, z in {0, 1} + for i in range(NI): + for k in range(NK): + for t in range(NT): + val_y = y(i, k, t) + if abs(val_y - round(val_y)) > TOL or round(val_y) not in (0, 1): + violated_constraints.add(19) + record_violation( + violations, magnitudes, 19, val_y, + round(val_y) if round(val_y) in (0, 1) else 0.0, + f"Constraint 19 (domain) violated: y_{i}_{k}_{t} = {val_y:.6f} is not binary" + ) + val_z = z(i, k, t) + if abs(val_z - round(val_z)) > TOL or round(val_z) not in (0, 1): + violated_constraints.add(19) + record_violation( + violations, magnitudes, 19, val_z, + round(val_z) if round(val_z) in (0, 1) else 0.0, + f"Constraint 19 (domain) violated: z_{i}_{k}_{t} = {val_z:.6f} is not binary" + ) + + # ========================================================================= + # Constraint 20: Objective consistency (Tier C anti-exploit check) + # reported objective_value must equal recomputed + # sum_{i,k,t} (f * y_{ikt} + g * z_{ikt}) + # + sum_{i,t} (h_i * s_{it} + e_i * r_{it}) + # All variables (y, z, s, r) appearing in the obj formula are present in + # the solution, so a full recompute is exact (modulo float rounding from + # solution serialization). Tolerance: 0.1% relative with 1e-3 absolute + # floor, matching the recipe in docs/data_review_fix/checker_add_obj_recompute.md. + # ========================================================================= + reported_obj = solution.get("objective_value") + if reported_obj is not None: + try: + reported = float(reported_obj) + except (TypeError, ValueError): + reported = None + if reported is not None and math.isfinite(reported): + true_obj = 0.0 + for i in range(NI): + for k in range(NK): + for t in range(NT): + true_obj += f_setup * y(i, k, t) + g_startup * z(i, k, t) + for t in range(NT): + true_obj += h_hold[i] * s(i, t) + e_back[i] * r(i, t) + abs_diff = abs(reported - true_obj) + tol = max(1e-3, 1e-3 * abs(true_obj)) + if abs_diff > tol: + violated_constraints.add(20) + record_violation( + violations, magnitudes, 20, reported, true_obj, + f"Constraint 20 (objective consistency) violated: " + f"reported objective_value={reported} differs from recomputed " + f"sum_(i,k,t)(f*y+g*z) + sum_(i,t)(h_i*s+e_i*r)={true_obj} " + f"(|diff|={abs_diff:.3g}, tol={tol:.3g})" + ) + elif reported is not None: + # Non-finite reported value (e.g. inf, nan) -- always inconsistent. + violated_constraints.add(20) + record_violation( + violations, magnitudes, 20, reported, 0.0, + f"Constraint 20 (objective consistency) violated: " + f"reported objective_value={reported} is not a finite number" + ) + + feasible = len(violated_constraints) == 0 + + return { + "feasible": feasible, + "violated_constraints": sorted(violated_constraints), + "violations": violations, + "violation_magnitudes": magnitudes, + } + + +def main(): + parser = argparse.ArgumentParser( + description="Feasibility checker for the Con-NT lot-sizing model (Belvaux & Wolsey 2000)" + ) + parser.add_argument("--instance_path", type=str, required=True, + help="Path to the JSON instance file") + parser.add_argument("--solution_path", type=str, required=True, + help="Path to the JSON candidate solution file") + parser.add_argument("--result_path", type=str, required=True, + help="Path to write the JSON feasibility result") + args = parser.parse_args() + + instance = load_json(args.instance_path) + solution = load_json(args.solution_path) + _frontieror_contract_result = _frontieror_validate_solution_contract(solution) + if _frontieror_contract_result is not None: + with open(args.result_path, "w") as _frontieror_result_handle: + json.dump( + _frontieror_contract_result, + _frontieror_result_handle, + indent=2, + ) + return + + result = check_feasibility(instance, solution) + + with open(args.result_path, "w") as f: + json.dump(result, f, indent=2) + + status = "FEASIBLE" if result["feasible"] else "INFEASIBLE" + n_violations = len(result["violated_constraints"]) + print(f"{status} ({n_violations} violated constraint(s))") + if not result["feasible"]: + print(f"Violated constraints: {result['violated_constraints']}") + for v in result["violations"]: + print(f" - {v}") + + +if __name__ == "__main__": + try: + main() + except Exception as _frontieror_unexpected_error: + if not _frontieror_write_unexpected_rejection( + _frontieror_unexpected_error + ): + raise diff --git a/belvaux2000/gurobi_code.py b/belvaux2000/gurobi_code.py new file mode 100644 index 0000000000000000000000000000000000000000..d9c6bda5c0a8e01ead9917986d0ee12a0027eb09 --- /dev/null +++ b/belvaux2000/gurobi_code.py @@ -0,0 +1,286 @@ +""" +Gurobi implementation of the Con-NT Small Bucket Lot-Sizing Model (SB-1). + +From: Belvaux & Wolsey (2000), "bc-prod: A Specialized Branch-and-Cut System +for Lot-Sizing Problems", Management Science 46(5):724-738. + +This implements the Con-NT model (Example 2 in the paper, Equations 9-14): + min sum_{i,k,t} (f * y_{ikt} + g * z_{ikt}) + + sum_{i,t} (h_i * s_{it} + e_i * r_{it}) + s.t. + (9) s_{i,t-1} - r_{i,t-1} + sum_k x_{ikt} = d_{it} + s_{it} - r_{it} for all i, t + (10) x_{ikt} + sigma_k * z_{ikt} <= C_k * y_{ikt} for all i, k, t + (11) z_{ikt} >= y_{ikt} - y_{ik,t-1} for all i, k, t + (12) sum_i y_{ikt} <= 1 for all k, t + (13) z_{ikt} <= y_{ikt} for all i, k, t + (14) y_{ik,t-1} + z_{ikt} + sum_{j!=i} (y_{jkt} - z_{jkt}) <= 1 for all i, k, t + s, r, x >= 0; y, z in {0, 1} + s_0 = r_0 = 0 +""" + +import json +import argparse +import gurobipy as gp +from gurobipy import GRB +import os as _os, sys as _sys +# Walk up from this file's directory to find repo root (containing scripts/). +_repo = _os.path.dirname(_os.path.abspath(__file__)) +while _repo != _os.path.dirname(_repo) and not _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _repo = _os.path.dirname(_repo) +if _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _sys.path.insert(0, _repo) +try: + from scripts.utils.gurobi_log_helper import install_gurobi_logger +except ImportError: + def install_gurobi_logger(log_path): # no-op fallback when scripts/ unavailable + pass + + +def load_instance(path): + with open(path, 'r') as f: + return json.load(f) + + +def solve(instance_path, solution_path, time_limit): + data = load_instance(instance_path) + + NI = data["parameters"]["NI"] + NK = data["parameters"]["NK"] + NT = data["parameters"]["NT"] + + f = data["costs"]["setup_cost_f"] # scalar setup cost + g = data["costs"]["startup_cost_g"] # scalar startup cost + h = data["costs"]["holding_cost_h"] # list length NI + e = data["costs"]["backlogging_cost_e"] # list length NI + + C = data["machine_data"]["capacity_C"] # list length NK + sigma = data["machine_data"]["startup_time_sigma"] # list length NK + + # demand[i][t] -- NI x NT + demand = data["demand"] + + s0 = data["initial_conditions"]["initial_stock_s0"] # list length NI + r0 = data["initial_conditions"]["initial_backlog_r0"] # list length NI + + # Index ranges (0-based internally) + items = range(NI) + machines = range(NK) + periods = range(NT) # 0..NT-1 represents periods 1..NT + + # Create model + model = gp.Model("ConNT_LotSizing") + model.setParam("Threads", 1) + model.setParam("TimeLimit", time_limit) + model.setParam("OutputFlag", 1) + + # Decision variables + # x[i,k,t]: production of item i on machine k in period t + x = {} + for i in items: + for k in machines: + for t in periods: + x[i, k, t] = model.addVar(lb=0.0, vtype=GRB.CONTINUOUS, + name=f"x_{i}_{k}_{t}") + + # s[i,t]: stock of item i at end of period t + s = {} + for i in items: + for t in periods: + s[i, t] = model.addVar(lb=0.0, vtype=GRB.CONTINUOUS, + name=f"s_{i}_{t}") + + # r[i,t]: backlog of item i at end of period t + r = {} + for i in items: + for t in periods: + r[i, t] = model.addVar(lb=0.0, vtype=GRB.CONTINUOUS, + name=f"r_{i}_{t}") + + # y[i,k,t]: setup variable (binary) + y = {} + for i in items: + for k in machines: + for t in periods: + y[i, k, t] = model.addVar(vtype=GRB.BINARY, + name=f"y_{i}_{k}_{t}") + + # z[i,k,t]: startup variable (binary) + z = {} + for i in items: + for k in machines: + for t in periods: + z[i, k, t] = model.addVar(vtype=GRB.BINARY, + name=f"z_{i}_{k}_{t}") + + model.update() + + # Objective: min sum_{i,k,t} (f * y + g * z) + sum_{i,t} (h_i * s + e_i * r) + obj = gp.LinExpr() + for i in items: + for k in machines: + for t in periods: + obj += f * y[i, k, t] + g * z[i, k, t] + for t in periods: + obj += h[i] * s[i, t] + e[i] * r[i, t] + model.setObjective(obj, GRB.MINIMIZE) + + # Constraint (9): Flow balance + # s_{i,t-1} - r_{i,t-1} + sum_k x_{ikt} = d_{it} + s_{it} - r_{it} + for i in items: + for t in periods: + lhs = gp.LinExpr() + # s_{i,t-1}: if t=0, use s0[i] + if t == 0: + lhs += s0[i] + else: + lhs += s[i, t - 1] + # -r_{i,t-1}: if t=0, use r0[i] + if t == 0: + lhs += -r0[i] + else: + lhs += -r[i, t - 1] + # + sum_k x_{ikt} + for k in machines: + lhs += x[i, k, t] + + rhs = demand[i][t] + s[i, t] - r[i, t] + model.addConstr(lhs == rhs, name=f"flow_{i}_{t}") + + # Constraint (10): Capacity with startup time + # x_{ikt} + sigma_k * z_{ikt} <= C_k * y_{ikt} + for i in items: + for k in machines: + for t in periods: + model.addConstr( + x[i, k, t] + sigma[k] * z[i, k, t] <= C[k] * y[i, k, t], + name=f"cap_{i}_{k}_{t}" + ) + + # Constraint (11): Startup definition + # z_{ikt} >= y_{ikt} - y_{ik,t-1} + # For t=0: y_{ik,-1} is not defined. + # ASSUMPTION (inferred): y_{ik,0} (period before the horizon) = 0, + # meaning no machine is set up before the planning horizon starts. + # This is consistent with the initial conditions s_0 = r_0 = 0. + for i in items: + for k in machines: + for t in periods: + if t == 0: + y_prev = 0 + else: + y_prev = y[i, k, t - 1] + model.addConstr( + z[i, k, t] >= y[i, k, t] - y_prev, + name=f"startup_{i}_{k}_{t}" + ) + + # Constraint (12): At most one item per machine per period + # sum_i y_{ikt} <= 1 + for k in machines: + for t in periods: + model.addConstr( + gp.quicksum(y[i, k, t] for i in items) <= 1, + name=f"onesetup_{k}_{t}" + ) + + # Constraint (13): z_{ikt} <= y_{ikt} + for i in items: + for k in machines: + for t in periods: + model.addConstr( + z[i, k, t] <= y[i, k, t], + name=f"zbound_{i}_{k}_{t}" + ) + + # Constraint (14): Linking constraint + # y_{ik,t-1} + z_{ikt} + sum_{j!=i} (y_{jkt} - z_{jkt}) <= 1 + # For t=0: y_{ik,-1} = 0 (same assumption as constraint 11) + for i in items: + for k in machines: + for t in periods: + if t == 0: + y_prev_i = 0 + else: + y_prev_i = y[i, k, t - 1] + lhs = y_prev_i + z[i, k, t] + for j in items: + if j != i: + lhs += y[j, k, t] - z[j, k, t] + model.addConstr(lhs <= 1, name=f"link_{i}_{k}_{t}") + + # Solve + model.optimize() + + # Extract solution + result = { + "instance_id": data.get("instance_id", 1), + "solver": "gurobi", + "status": model.Status, + "objective_value": None, + "production": {}, + "stock": {}, + "backlog": {}, + "setup": {}, + "startup": {} + } + + if model.SolCount > 0: + result["objective_value"] = model.ObjVal + + for i in items: + for k in machines: + for t in periods: + val = x[i, k, t].X + if val > 1e-6: + result["production"][f"x_{i}_{k}_{t}"] = round(val, 4) + for t in periods: + s_val = s[i, t].X + r_val = r[i, t].X + if s_val > 1e-6: + result["stock"][f"s_{i}_{t}"] = round(s_val, 4) + if r_val > 1e-6: + result["backlog"][f"r_{i}_{t}"] = round(r_val, 4) + for k in machines: + for t in periods: + if y[i, k, t].X > 0.5: + result["setup"][f"y_{i}_{k}_{t}"] = 1 + if z[i, k, t].X > 0.5: + result["startup"][f"z_{i}_{k}_{t}"] = 1 + + if model.Status == GRB.OPTIMAL: + result["status_text"] = "OPTIMAL" + elif model.Status == GRB.TIME_LIMIT: + result["status_text"] = "TIME_LIMIT" + result["best_bound"] = model.ObjBound + result["gap"] = model.MIPGap + else: + result["status_text"] = f"STATUS_{model.Status}" + else: + result["status_text"] = "NO_SOLUTION_FOUND" + result["objective_value"] = None + + with open(solution_path, 'w') as f: + json.dump(result, f, indent=2) + + print(f"Solution written to {solution_path}") + if result["objective_value"] is not None: + print(f"Objective value: {result['objective_value']}") + print(f"Status: {result['status_text']}") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description="Gurobi solver for the Con-NT Small Bucket Lot-Sizing Problem" + ) + parser.add_argument("--instance_path", type=str, required=True, + help="Path to the JSON instance file") + parser.add_argument("--solution_path", type=str, + default="gurobi_solution_1.json", + help="Path to write the solution JSON file") + parser.add_argument("--time_limit", type=int, default=3600, + help="Maximum solver runtime in seconds") + parser.add_argument("--log_path", type=str, default=None, help="Path to log incumbent solutions") + args = parser.parse_args() + install_gurobi_logger(args.log_path) + + solve(args.instance_path, args.solution_path, args.time_limit) diff --git a/belvaux2000/gurobi_feasi_result/large_feasi_result_1.json b/belvaux2000/gurobi_feasi_result/large_feasi_result_1.json new file mode 100644 index 0000000000000000000000000000000000000000..e305b82c3d6732211d5033415df1a105476e9cf1 --- /dev/null +++ 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a/belvaux2000/instance/tiny_instance.json b/belvaux2000/instance/tiny_instance.json new file mode 100644 index 0000000000000000000000000000000000000000..289566da9ef94a86bac56263782c45081a08bd26 --- /dev/null +++ b/belvaux2000/instance/tiny_instance.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:520d862ba7f2334db869cca15742f86de5be9d0b736f636e6295c66df9c99ec8 +size 2251 diff --git a/belvaux2000/instance_schema.json b/belvaux2000/instance_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..99b2f2388127594df1fd4073d047bd9b4e463294 --- /dev/null +++ b/belvaux2000/instance_schema.json @@ -0,0 +1,22 @@ +{ + "parameters": { + "NI": " Number of distinct items to be produced.", + "NK": " Number of machines available for production.", + "NT": " Number of discrete time periods in the planning horizon." + }, + "costs": { + "setup_cost_f": " Fixed cost incurred each period a machine is set up for any item, uniform across all items and machines.", + "startup_cost_g": " Fixed cost incurred each time a machine starts up for an item it was not set up for in the previous period, uniform across all items and machines.", + "holding_cost_h": " Per-unit cost of holding one unit of each item in inventory at the end of a period.", + "backlogging_cost_e": " Per-unit penalty cost for each unit of unmet demand (backlog) for each item at the end of a period." + }, + "machine_data": { + "capacity_C": " Maximum production quantity each machine can produce in any single period.", + "startup_time_sigma": " Amount of production capacity consumed on each machine when a startup occurs in a period." + }, + "demand": " Quantity of each item required by customers in each period.", + "initial_conditions": { + "initial_stock_s0": " Inventory on hand for each item at the start of the planning horizon.", + "initial_backlog_r0": " Unfulfilled demand for each item carried into the start of the planning horizon." + } +} diff --git a/belvaux2000/mathematical_formulation.md b/belvaux2000/mathematical_formulation.md new file mode 100644 index 0000000000000000000000000000000000000000..b6e9f760fbd23c7bef4a200d78df8335a9f511fe --- /dev/null +++ b/belvaux2000/mathematical_formulation.md @@ -0,0 +1,65 @@ +# Original Formulation: Two-Machine Small-Bucket Lot-Sizing with Backlogging and Start-Ups (Con-NT) + +*Source: bc–prod: A Specialized Branch-and-Cut System for Lot-Sizing Problems, Gaetan Belvaux and Laurence A. Wolsey, 2000 (Management Science 46(5):724–738).* + +The paper describes the prototype system *bc–prod* via a generic lot-sizing template (Constraints (1)–(5)) and two concrete problem instances. The instance studied here is **Example 2 (Con-NT)**, the two-machine small-bucket model with backlogging and start-up costs/times stated in Eqs. (9)–(14); its variable symbols and reported objective are the ones carried into the computational experiments. The generic template and the big-bucket instance (set1ch) are recorded under **Variants**. + +## Sets and Indices + +- $i = 1, \dots, NI$ : items (products). + +- $k = 1, \dots, NK$ : machines (Con-NT uses two machines). + +- $t = 1, \dots, NT$ : time periods ($NT = 12/24$ in the instance). + +## Parameters + +- $f$ : set-up cost (uniform over all items and machines). + +- $g$ : start-up cost (uniform over all items and machines). + +- $h^i$ : per-unit inventory holding cost for item $i$. + +- $e^i$ : per-unit backlogging penalty cost for item $i$. + +- $d^i_t$ : prespecified demand for item $i$ in period $t$. + +- $C^k$ : production capacity of machine $k$ (constant over $t$). + +- $\sigma^k$ : start-up time – loss of production capacity on machine $k$ when a start-up occurs. + +## Decision Variables + +- $x^{ik}_t \geq 0$ : production of item $i$ on machine $k$ in period $t$ (continuous). + +- $s^i_t \geq 0$ : stock of item $i$ at the end of period $t$ (continuous). + +- $r^i_t \geq 0$ : backlog of item $i$ at the end of period $t$ (continuous). + +- $y^{ik}_t \in \{0,1\}$ : set-up variable; $1$ if machine $k$ is set up to produce item $i$ in period $t$. + +- $z^{ik}_t \in \{0,1\}$ : start-up variable; $1$ if machine $k$ starts a sequence of set-ups of item $i$ in period $t$. + +## Objective + +$$\begin{align} +\min \quad \sum_{i,k,t} \bigl( f\,y^{ik}_t + g\,z^{ik}_t \bigr) + + \sum_{i,t} \bigl( h^i\,s^i_t + e^i\,r^i_t \bigr) \notag +\end{align}$$ + +## Constraints + +$$\begin{align} +s^i_{t-1} - r^i_{t-1} + \sum_{k} x^{ik}_t + &= d^i_t + s^i_t - r^i_t && \forall\, i,\,t \tag{9}\\[2pt] +x^{ik}_t + \sigma^k z^{ik}_t + &\leq C^k y^{ik}_t && \forall\, i,\,k,\,t \tag{10}\\[2pt] +z^{ik}_t &\geq y^{ik}_t - y^{ik}_{t-1} && \forall\, i,\,k,\,t \tag{11}\\[2pt] +\sum_{i} y^{ik}_t &\leq 1 && \forall\, k,\,t \tag{12}\\[2pt] +z^{ik}_t &\leq y^{ik}_t && \forall\, i,\,k,\,t \tag{13}\\[2pt] +y^{ik}_{t-1} + z^{ik}_t + \sum_{j:\, j \neq i} \bigl( y^{jk}_t - z^{jk}_t \bigr) + &\leq 1 && \forall\, i,\,k,\,t \tag{14} +\end{align}$$ with domains and initial conditions $$\begin{align} +s,\, r,\, x &\geq 0, \qquad y,\, z \in \{0,1\}, \notag\\ +s^i_0 &= r^i_0 = 0. \notag +\end{align}$$ diff --git a/belvaux2000/problem_description.txt b/belvaux2000/problem_description.txt new file mode 100644 index 0000000000000000000000000000000000000000..ccd70b56101bfc3d297a4a8fe365af5a5c46063b --- /dev/null +++ b/belvaux2000/problem_description.txt @@ -0,0 +1,7 @@ +# Problem Description + +A manufacturing facility produces NI distinct items using NK machines over a planning horizon of NT discrete periods. The instance data specifies the demand for each item in each period (demand, indexed by item and period), each machine's per-period production capacity (capacity_C, indexed by machine) and startup capacity loss (startup_time_sigma, indexed by machine), a uniform setup cost (setup_cost_f) and a uniform startup cost (startup_cost_g) applying to all items and machines, item-specific per-unit inventory holding costs (holding_cost_h, indexed by item) and backlogging penalty costs (backlogging_cost_e, indexed by item), and initial inventory (initial_stock_s0) and backlog levels (initial_backlog_r0) for each item. + +In each period, for each item, the prior period's closing inventory plus total production of that item across all machines minus the prior period's closing backlog must equal the current demand plus this period's closing inventory minus this period's closing backlog. Each machine may produce at most one item per period and can only produce an item it is set up for. Each machine has a fixed per-period production capacity that does not vary over time. A startup is incurred on a machine precisely when it becomes set up for an item it was not set up for in the immediately preceding period; no machine is considered set up for any item before the first period. Each startup consumes a machine-specific amount of that period's production capacity, so the quantity produced on a machine plus any startup capacity loss cannot exceed the machine's capacity. Furthermore, if a machine was set up for a certain item in the previous period, that same item cannot incur a startup on that machine in the current period, and every different item assigned to that machine in the current period must incur a startup. + +The facility pays the setup cost each period a machine is set up for any item, the startup cost for each startup event, the item-specific holding cost for each unit of inventory at each period's end, and the item-specific backlogging penalty for each unit of backlog at each period's end. The goal is to choose production quantities, inventory and backlog levels, and setup and startup schedules for every item, machine, and period so as to minimize the total of all setup, startup, holding, and backlogging costs over the entire horizon. diff --git a/belvaux2000/solution_logger.py b/belvaux2000/solution_logger.py new file mode 100644 index 0000000000000000000000000000000000000000..5393fa935ce71df9da95cee04fe5199077cb8461 --- /dev/null +++ b/belvaux2000/solution_logger.py @@ -0,0 +1,61 @@ +""" +Convergence logger for optimization algorithms. + +Records incumbent solutions with timestamps to a JSONL file. +This module is provided to LLM-generated programs — they only need to call +`log(objective_value)` whenever a better feasible solution is found. + +Usage in generated code: + from solution_logger import SolutionLogger + logger = SolutionLogger(log_path, sense="minimize") # or "maximize" + # ... inside algorithm loop: + logger.log(objective_value) +""" + +import json +import time + + +class SolutionLogger: + def __init__(self, log_path, sense="minimize"): + """ + Args: + log_path: Path to the JSONL output file. + sense: "minimize" or "maximize". + """ + self.log_path = log_path + self.sense = sense + self.start_time = time.time() + self.best_obj = None + self.min_interval = 0.1 # seconds, avoid excessive writes + + self._last_log_time = 0.0 + # Clear the file + with open(self.log_path, "w") as f: + pass + + def log(self, objective_value): + """Record a new incumbent if it improves on the best known.""" + if objective_value is None: + return + + # Check if this is an improvement + if self.best_obj is not None: + if self.sense == "minimize" and objective_value >= self.best_obj: + return + if self.sense == "maximize" and objective_value <= self.best_obj: + return + + elapsed = time.time() - self.start_time + + # Throttle writes + if self.best_obj is not None and elapsed - self._last_log_time < self.min_interval: + self.best_obj = objective_value + return + + self.best_obj = objective_value + self._last_log_time = elapsed + + with open(self.log_path, "a") as f: + f.write(json.dumps({"time": round(elapsed, 3), + "objective_value": objective_value}) + "\n") diff --git a/belvaux2000/solution_schema.json b/belvaux2000/solution_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..5f87c325315651eb570c0e7f71478b44d3a4d67d --- /dev/null +++ b/belvaux2000/solution_schema.json @@ -0,0 +1,8 @@ +{ + "objective_value": " Total cost of the production plan, combining setup, startup, inventory holding, and backlogging costs over the planning horizon.", + "production": " Quantity of an item produced on a specific machine in a specific period.", + "stock": " Inventory of an item held on hand at the end of a specific period.", + "backlog": " Unfulfilled demand for an item carried as backlog at the end of a specific period.", + "setup": " Whether a machine is set up to produce a given item in a given period (1 = set up).", + "startup": " Whether a machine begins a new setup for a given item in a given period, having not been set up for that item in the previous period (1 = startup occurs)." +} diff --git a/bentert2022/feasibility_check.py b/bentert2022/feasibility_check.py new file mode 100644 index 0000000000000000000000000000000000000000..30242c2b684b9a1b6c0300148759eb61f3eeaa69 --- /dev/null +++ b/bentert2022/feasibility_check.py @@ -0,0 +1,425 @@ +#!/usr/bin/env python3 +""" +Feasibility checker for Min-Power Symmetric Connectivity (MinPSC). + +Checks candidate solutions against the hard constraints from the mathematical +formulation in Bentert et al. (2020): + + Constraint 1 (C1): Spanning subgraph — F ⊆ E, and all edges in F must use + valid vertices from V. + Constraint 2 (C2): Connectivity — T = (V, F) must be a connected subgraph. + Constraint 3 (C3): Cost definition — For each v ∈ V, + p_v = max_{{u,v} ∈ F} w({u,v}). +""" + +import argparse +import json +from collections import defaultdict, deque + +# FRONTIEROR_RELEASE_CONTRACT_V2 +_FRONTIEROR_REQUIRED_SOLUTION_FIELDS = ('objective_value', 'selected_edges', 'vertex_costs') +_FRONTIEROR_NONEMPTY_SOLUTION_FIELDS = () +_FRONTIEROR_DERIVED_FIELD_RULES = () +_FRONTIEROR_CONDITIONAL_FIELD_RULES = () + + +def _frontieror_validate_solution_contract(solution): + """Return a structured rejection when the public solution contract is absent.""" + violations = [] + if not isinstance(solution, dict): + violations.append("solution must be a JSON object") + else: + missing = [ + field + for field in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS + if field not in solution or solution[field] is None + ] + if missing: + violations.append( + "missing required solution field(s): " + ", ".join(missing) + ) + + for discriminator, expected_value, fields in _FRONTIEROR_CONDITIONAL_FIELD_RULES: + if solution.get(discriminator) != expected_value: + continue + conditional_missing = [ + field + for field in fields + if field not in solution or solution[field] is None + ] + if conditional_missing: + violations.append( + "missing conditionally required solution field(s): " + + ", ".join(conditional_missing) + ) + + empty = [ + field + for field in _FRONTIEROR_NONEMPTY_SOLUTION_FIELDS + if field in solution and solution[field] in (None, [], {}) + ] + if empty: + violations.append( + "empty required decision witness field(s): " + ", ".join(empty) + ) + + for target, operation, source, source_key in _FRONTIEROR_DERIVED_FIELD_RULES: + try: + reported = float(solution[target]) + if operation == "length": + expected = float(len(solution[source])) + elif operation == "mapping_value": + expected = float(solution[source][source_key]) + else: + raise ValueError("unknown derived-field operation") + except (KeyError, TypeError, ValueError, OverflowError): + violations.append( + f"cannot derive {target} from decision witness field {source}" + ) + continue + if ( + reported != reported + or expected != expected + or reported in (float("inf"), float("-inf")) + or expected in (float("inf"), float("-inf")) + or abs(reported - expected) > 1e-6 + ): + violations.append( + f"{target}={reported} does not match the decision-derived " + f"value {expected}" + ) + + if "objective_value" in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS: + objective = solution.get("objective_value") + if ( + isinstance(objective, bool) + or not isinstance(objective, (int, float)) + or objective != objective + or objective in (float("inf"), float("-inf")) + ): + violations.append("objective_value must be a finite JSON number") + + if not violations: + return None + return { + "feasible": False, + "violated_constraints": ["solution_schema"], + "violations": violations, + "violation_magnitudes": [], + } + + +def _frontieror_write_unexpected_rejection(error): + """Turn malformed candidate/checker exceptions into a normal rejection.""" + import json as _frontieror_json + import sys as _frontieror_sys + + result_path = None + for index, argument in enumerate(_frontieror_sys.argv): + if argument == "--result_path" and index + 1 < len(_frontieror_sys.argv): + result_path = _frontieror_sys.argv[index + 1] + break + if argument.startswith("--result_path="): + result_path = argument.split("=", 1)[1] + break + if not result_path: + return False + try: + with open(result_path, "w") as result_handle: + _frontieror_json.dump( + { + "feasible": False, + "violated_constraints": ["malformed_solution"], + "violations": [ + "checker rejected malformed candidate: " + + type(error).__name__ + + ": " + + str(error)[:500] + ], + "violation_magnitudes": [], + }, + result_handle, + indent=2, + ) + except Exception: + return False + return True + + + +def load_instance(instance_path): + with open(instance_path, 'r') as f: + data = json.load(f) + + vertices = data["vertices"] + V = [] + coords = {} + for v_info in vertices: + vid = v_info["id"] + V.append(vid) + coords[vid] = (v_info["x"], v_info["y"]) + + # Build edge weight lookup (squared Euclidean distance, complete graph) + edge_weights = {} + for i in range(len(V)): + for j in range(i + 1, len(V)): + u, v = V[i], V[j] + dx = coords[u][0] - coords[v][0] + dy = coords[u][1] - coords[v][1] + w = dx * dx + dy * dy + edge_weights[(u, v)] = w + edge_weights[(v, u)] = w + + return V, coords, edge_weights + + +def check_connectivity(V, selected_edges): + """Check if (V, selected_edges) is connected. Returns number of components.""" + adj = defaultdict(set) + for u, v in selected_edges: + adj[u].add(v) + adj[v].add(u) + + if not V: + return 0 + + visited = set() + queue = deque([V[0]]) + visited.add(V[0]) + while queue: + node = queue.popleft() + for nb in adj[node]: + if nb not in visited: + visited.add(nb) + queue.append(nb) + + V_set = set(V) + num_visited = len(visited & V_set) + if num_visited == len(V_set): + return 1 + + # Count actual components + components = 0 + visited = set() + for start in V: + if start in visited: + continue + components += 1 + queue = deque([start]) + visited.add(start) + while queue: + node = queue.popleft() + for nb in adj[node]: + if nb in V_set and nb not in visited: + visited.add(nb) + queue.append(nb) + return components + + +def main(): + parser = argparse.ArgumentParser( + description="Feasibility checker for MinPSC solutions" + ) + parser.add_argument("--instance_path", type=str, required=True, + help="Path to the JSON instance file") + parser.add_argument("--solution_path", type=str, required=True, + help="Path to the JSON solution file") + parser.add_argument("--result_path", type=str, required=True, + help="Path to write the JSON feasibility result") + args = parser.parse_args() + + tol = 1e-5 + eps = 1e-5 + + V, coords, edge_weights = load_instance(args.instance_path) + V_set = set(V) + + with open(args.solution_path, 'r') as f: + solution = json.load(f) + _frontieror_contract_result = _frontieror_validate_solution_contract(solution) + if _frontieror_contract_result is not None: + with open(args.result_path, "w") as _frontieror_result_handle: + json.dump( + _frontieror_contract_result, + _frontieror_result_handle, + indent=2, + ) + return + + selected_edges = [tuple(e) for e in solution.get("selected_edges", [])] + objective_value = solution.get("objective_value", None) + vertex_costs = solution.get("vertex_costs", {}) + # Keys in vertex_costs may be strings + vertex_costs = {int(k): float(v) for k, v in vertex_costs.items()} + + violated_constraints = set() + violations = [] + violation_magnitudes = [] + + # ========================================================================= + # Constraint 1 (C1): Spanning subgraph — F ⊆ E + # All edges in F must connect valid vertices from V, and each edge must + # exist in E (the complete graph over V, so just check vertex validity). + # ========================================================================= + invalid_edges = [] + for u, v in selected_edges: + if u not in V_set or v not in V_set: + invalid_edges.append((u, v)) + elif u == v: + invalid_edges.append((u, v)) + + if invalid_edges: + violated_constraints.add(1) + violations.append( + f"C1: {len(invalid_edges)} edge(s) in F use invalid or duplicate " + f"vertex IDs not in V: {invalid_edges[:5]}" + ) + # LHS = number of invalid edges, RHS = 0 (equality: should be 0) + lhs = float(len(invalid_edges)) + rhs = 0.0 + violation_amount = abs(lhs - rhs) + normalizer = max(abs(rhs), eps) + violation_magnitudes.append({ + "constraint": 1, + "lhs": lhs, + "rhs": rhs, + "raw_excess": violation_amount, + "normalizer": normalizer, + "ratio": violation_amount / normalizer, + }) + + # ========================================================================= + # Constraint 2 (C2): Connectivity — T = (V, F) must be connected + # ========================================================================= + num_components = check_connectivity(V, selected_edges) + if num_components != 1: + violated_constraints.add(2) + violations.append( + f"C2: Subgraph (V, F) has {num_components} connected components " + f"(must be 1)" + ) + # LHS = num_components, RHS = 1 (equality: should be exactly 1) + lhs = float(num_components) + rhs = 1.0 + violation_amount = abs(lhs - rhs) + normalizer = max(abs(rhs), eps) + violation_magnitudes.append({ + "constraint": 2, + "lhs": lhs, + "rhs": rhs, + "raw_excess": violation_amount, + "normalizer": normalizer, + "ratio": violation_amount / normalizer, + }) + + # ========================================================================= + # Constraint 3 (C3): Cost definition — p_v = max_{{u,v} ∈ F} w({u,v}) + # For each vertex v, the reported cost must equal the maximum weight of + # any selected edge incident to v. Vertices with no incident selected + # edges have an implied cost of 0. + # ========================================================================= + # Compute actual max incident edge weight for each vertex + actual_costs = {v: 0.0 for v in V} + for u, v in selected_edges: + if u in V_set and v in V_set and u != v: + w = edge_weights[(u, v)] + if w > actual_costs[u]: + actual_costs[u] = w + if w > actual_costs[v]: + actual_costs[v] = w + + c3_violations = [] + for v in V: + reported = vertex_costs.get(v, 0.0) + actual = actual_costs[v] + diff = abs(reported - actual) + if diff > tol: + c3_violations.append((v, reported, actual, diff)) + + if c3_violations: + violated_constraints.add(3) + # Summarize + example_strs = [] + for v, reported, actual, diff in c3_violations[:5]: + example_strs.append( + f"v={v}: reported={reported:.6f}, actual={actual:.6f}, " + f"diff={diff:.6f}" + ) + violations.append( + f"C3: {len(c3_violations)} vertex cost(s) do not match " + f"max incident edge weight. Examples: {'; '.join(example_strs)}" + ) + # Record one violation magnitude entry per violated vertex + for v, reported, actual, diff in c3_violations: + lhs = reported + rhs = actual + violation_amount = diff + normalizer = max(abs(rhs), eps) + violation_magnitudes.append({ + "constraint": 3, + "lhs": lhs, + "rhs": rhs, + "raw_excess": violation_amount, + "normalizer": normalizer, + "ratio": violation_amount / normalizer, + }) + + # ========================================================================= + # Constraint 4 (C4): Objective value — objective_value == sum of p_v + # The reported objective_value must equal the sum of all vertex costs. + # From math_model: min sum_{v in V} p_v + # ========================================================================= + if objective_value is not None: + computed_obj = sum(vertex_costs.get(v, 0.0) for v in V) + obj_diff = abs(objective_value - computed_obj) + if obj_diff > tol: + violated_constraints.add(4) + violations.append( + f"C4: Reported objective_value={objective_value:.6f} does not " + f"match sum of vertex_costs={computed_obj:.6f}, " + f"diff={obj_diff:.6f}" + ) + lhs = float(objective_value) + rhs = computed_obj + violation_amount = obj_diff + normalizer = max(abs(rhs), eps) + violation_magnitudes.append({ + "constraint": 4, + "lhs": lhs, + "rhs": rhs, + "raw_excess": violation_amount, + "normalizer": normalizer, + "ratio": violation_amount / normalizer, + }) + + # ========================================================================= + # Build output + # ========================================================================= + feasible = len(violated_constraints) == 0 + result = { + "feasible": feasible, + "violated_constraints": sorted(violated_constraints), + "violations": violations, + "violation_magnitudes": violation_magnitudes, + } + + with open(args.result_path, 'w') as f: + json.dump(result, f, indent=2) + + if feasible: + print("Solution is FEASIBLE.") + else: + print(f"Solution is INFEASIBLE. Violated constraints: " + f"{sorted(violated_constraints)}") + for msg in violations: + print(f" - {msg}") + + +if __name__ == "__main__": + try: + main() + except Exception as _frontieror_unexpected_error: + if not _frontieror_write_unexpected_rejection( + _frontieror_unexpected_error + ): + raise diff --git a/bentert2022/gurobi_code.py b/bentert2022/gurobi_code.py new file mode 100644 index 0000000000000000000000000000000000000000..fcbeae951bd5474a72eb55aebecfe64dc2ae25bd --- /dev/null +++ b/bentert2022/gurobi_code.py @@ -0,0 +1,244 @@ +#!/usr/bin/env python3 +""" +Gurobi ILP implementation of Min-Power Symmetric Connectivity (MinPSC). + +Paper: Bentert et al., "Parameterized Algorithms for Power-Efficiently + Connecting Wireless Sensor Networks: Theory and Experiments" (2020) + +Problem 1.1 (MinPSC): + Given a connected undirected graph G=(V,E) with edge weights w: E -> N, + find a connected spanning subgraph T=(V,F), F subset of E, that minimizes + sum_{v in V} max_{{u,v} in F} w({u,v}) + +The paper does NOT provide its own ILP formulation. It references ILP models +EX1 and EX2 from Montemanni and Gambardella (2005), which are +**NOT EXPLICITLY SPECIFIED IN THIS PAPER**. + +INFERRED ASSUMPTION: We implement a standard ILP for MinPSC using: + - Binary edge selection variables x_e + - Continuous variables p_v for vertex power cost + - Linearization of max via p_v >= w(e) * x_e + - Single-commodity flow for connectivity enforcement + (pick an arbitrary root, send n-1 units of flow, each vertex absorbs 1) +""" + +import argparse +import json +import math +import sys +from itertools import combinations + +import gurobipy as gp +from gurobipy import GRB +import os as _os, sys as _sys +# Walk up from this file's directory to find repo root (containing scripts/). +_repo = _os.path.dirname(_os.path.abspath(__file__)) +while _repo != _os.path.dirname(_repo) and not _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _repo = _os.path.dirname(_repo) +if _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _sys.path.insert(0, _repo) +try: + from scripts.utils.gurobi_log_helper import install_gurobi_logger +except ImportError: + def install_gurobi_logger(log_path): # no-op fallback when scripts/ unavailable + pass +def load_instance(instance_path): + """Load a MinPSC instance from JSON.""" + with open(instance_path, 'r') as f: + data = json.load(f) + + vertices = data["vertices"] + n = data["n"] + m = data["m"] + + # Build vertex list + V = [] + coords = {} + lower_bounds = {} + for v_info in vertices: + vid = v_info["id"] + V.append(vid) + coords[vid] = (v_info["x"], v_info["y"]) + lower_bounds[vid] = v_info["lower_bound"] + + # Build complete graph edges with squared Euclidean distance weights + # The instance specifies "complete" graph with "squared_euclidean_distance" + edges = [] + edge_weights = {} + for i in range(len(V)): + for j in range(i + 1, len(V)): + u, v = V[i], V[j] + dx = coords[u][0] - coords[v][0] + dy = coords[u][1] - coords[v][1] + w = dx * dx + dy * dy + edges.append((u, v)) + edge_weights[(u, v)] = w + edge_weights[(v, u)] = w + + return V, edges, edge_weights, coords, lower_bounds, data + + +def solve_minpsc_gurobi(V, edges, edge_weights, time_limit): + """ + Solve MinPSC using Gurobi ILP. + + Formulation: + min sum_{v in V} p_v + s.t. + p_v >= w({u,v}) * x_{u,v} for all edges {u,v} and both endpoints + T = (V, F) is connected (enforced via single-commodity flow) + x_e in {0,1}, p_v >= 0 + + Connectivity via single-commodity flow: + - Pick root r (vertex 0). Root sends n-1 units of flow. + - Each non-root vertex absorbs exactly 1 unit. + - Flow on directed arc (u,v) is bounded by (n-1)*x_{u,v}. + + **NOT EXPLICITLY SPECIFIED IN PAPER**: The exact connectivity formulation. + INFERRED ASSUMPTION: We use single-commodity flow which is a standard + approach for enforcing connectivity in ILP formulations. + """ + n = len(V) + if n == 0: + return 0.0, [] + if n == 1: + return 0.0, [] + + model = gp.Model("MinPSC") + model.setParam("Threads", 1) + model.setParam("TimeLimit", time_limit) + model.setParam("OutputFlag", 1) + + # Binary edge selection variables + x = {} + for (u, v) in edges: + x[(u, v)] = model.addVar(vtype=GRB.BINARY, name=f"x_{u}_{v}") + + # Vertex power cost variables + p = {} + for v in V: + p[v] = model.addVar(vtype=GRB.CONTINUOUS, lb=0.0, name=f"p_{v}") + + # Flow variables: directed arcs for each undirected edge + # Pick root as V[0] + root = V[0] + f_var = {} + for (u, v) in edges: + f_var[(u, v)] = model.addVar(vtype=GRB.CONTINUOUS, lb=0.0, + ub=n - 1, name=f"f_{u}_{v}") + f_var[(v, u)] = model.addVar(vtype=GRB.CONTINUOUS, lb=0.0, + ub=n - 1, name=f"f_{v}_{u}") + + model.update() + + # Objective: minimize sum of vertex power costs + model.setObjective(gp.quicksum(p[v] for v in V), GRB.MINIMIZE) + + # Constraint: p_v >= w(e) * x_e for each edge e incident to v + for (u, v) in edges: + w = edge_weights[(u, v)] + model.addConstr(p[u] >= w * x[(u, v)], name=f"pwr_{u}_{u}_{v}") + model.addConstr(p[v] >= w * x[(u, v)], name=f"pwr_{v}_{u}_{v}") + + # Flow conservation: each non-root absorbs 1 unit + # Build adjacency for flow + # For root: outflow - inflow = n - 1 + # For others: outflow - inflow = -1 + for v in V: + outflow = gp.LinExpr() + inflow = gp.LinExpr() + for (u, w_v) in edges: + if u == v: + outflow.add(f_var[(u, w_v)]) + inflow.add(f_var[(w_v, u)]) + elif w_v == v: + outflow.add(f_var[(w_v, u)]) + inflow.add(f_var[(u, w_v)]) + + if v == root: + model.addConstr(outflow - inflow == n - 1, + name=f"flow_root_{v}") + else: + model.addConstr(outflow - inflow == -1, + name=f"flow_{v}") + + # Flow capacity: flow on arc (u,v) <= (n-1) * x_{u,v} + for (u, v) in edges: + model.addConstr(f_var[(u, v)] <= (n - 1) * x[(u, v)], + name=f"cap_{u}_{v}") + model.addConstr(f_var[(v, u)] <= (n - 1) * x[(u, v)], + name=f"cap_{v}_{u}") + + # Solve + model.optimize() + + # Extract solution + obj_val = None + selected_edges = [] + + if model.SolCount > 0: + obj_val = model.ObjVal + for (u, v) in edges: + if x[(u, v)].X > 0.5: + selected_edges.append((u, v)) + else: + obj_val = None + + return obj_val, selected_edges + + +def main(): + parser = argparse.ArgumentParser( + description="Gurobi ILP solver for MinPSC (Min-Power Symmetric Connectivity)" + ) + parser.add_argument("--instance_path", type=str, required=True, + help="Path to the JSON instance file") + parser.add_argument("--solution_path", type=str, required=True, + help="Path to write the solution JSON file") + parser.add_argument("--time_limit", type=int, required=True, + help="Maximum solver runtime in seconds") + parser.add_argument("--log_path", type=str, default=None, help="Path to log incumbent solutions") + args = parser.parse_args() + install_gurobi_logger(args.log_path) + + # Load instance + V, edges, edge_weights, coords, lower_bounds, instance_data = \ + load_instance(args.instance_path) + + # Solve + obj_val, selected_edges = solve_minpsc_gurobi( + V, edges, edge_weights, args.time_limit + ) + + # Build solution output + solution = { + "problem": "MinPSC", + "instance_path": args.instance_path, + "solver": "Gurobi", + "objective_value": obj_val if obj_val is not None else None, + "selected_edges": [list(e) for e in selected_edges], + "num_selected_edges": len(selected_edges), + } + + # Compute per-vertex costs if solution found + if obj_val is not None: + vertex_costs = {v: 0.0 for v in V} + for (u, v) in selected_edges: + w = edge_weights[(u, v)] + if w > vertex_costs[u]: + vertex_costs[u] = w + if w > vertex_costs[v]: + vertex_costs[v] = w + solution["vertex_costs"] = {str(v): c for v, c in vertex_costs.items()} + + with open(args.solution_path, 'w') as f: + json.dump(solution, f, indent=2) + + if obj_val is not None: + print(f"Optimal objective value: {obj_val}") + else: + print("No feasible solution found within time limit.") + + +if __name__ == "__main__": + main() diff --git a/bentert2022/gurobi_feasi_result/large_feasi_result_1.json b/bentert2022/gurobi_feasi_result/large_feasi_result_1.json new file mode 100644 index 0000000000000000000000000000000000000000..e305b82c3d6732211d5033415df1a105476e9cf1 --- /dev/null +++ b/bentert2022/gurobi_feasi_result/large_feasi_result_1.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3269392edcbb7720882e77caadb88b4a9cf2fda04cf482cebd23baace4a531fd +size 102 diff --git a/bentert2022/gurobi_feasi_result/large_feasi_result_2.json b/bentert2022/gurobi_feasi_result/large_feasi_result_2.json 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/dev/null +++ b/bentert2022/instance/tiny_instance.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9e2a38a8af25933b2569391fd89eecbf7c6a3f0473bb45e1c8efab2ddd337f0c +size 7558 diff --git a/bentert2022/instance_schema.json b/bentert2022/instance_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..4d019b772a8e45e06f51b5f7094fd6b76017c540 --- /dev/null +++ b/bentert2022/instance_schema.json @@ -0,0 +1,15 @@ +{ + "n": " Number of sensors in the network.", + "m": " Number of communication links between sensors.", + "graph_type": " Structure of the graph connecting sensors.", + "edge_weight_rule": " Rule used to compute the power cost of each communication link from sensor positions.", + "vertices": [ + { + "id": " Unique identifier of the sensor.", + "x": " Horizontal coordinate of the sensor.", + "y": " Vertical coordinate of the sensor.", + "lower_bound": " Minimum link weight among all links incident to this sensor." + } + ], + "c_obligatory_components": " Number of connected components in the obligatory subgraph formed by mutually nearest-neighbor sensor pairs." +} diff --git a/bentert2022/mathematical_formulation.md b/bentert2022/mathematical_formulation.md new file mode 100644 index 0000000000000000000000000000000000000000..58ef59ae572125dd327b8b48db1da0a62b732aa2 --- /dev/null +++ b/bentert2022/mathematical_formulation.md @@ -0,0 +1,35 @@ +# Original Formulation: Min-Power Symmetric Connectivity (MinPSC) + +*Source: Parameterized Algorithms for Power-Efficiently Connecting Wireless Sensor Networks: Theory and Experiments, Matthias Bentert, René van Bevern, André Nichterlein, Rolf Niedermeier, Pavel V. Smirnov, 2020 (arXiv:1706.03177v3).* + +## Sets and Parameters + +- $G = (V, E)$: a connected, undirected, finite, simple graph. + +- $V$: set of vertices (wireless sensors), $|V| = n$. + +- $E \subseteq \big\{\,\{v, w\} \mid v \neq w \text{ and } v, w \in V\,\big\}$: set of undirected edges, $|E| = m$. + +- $w : E \to \mathbb{N}$: edge-weight function; weights are natural numbers (the paper takes $\mathbb{N}$ to include $0$). + +- Convention (Section 2.1): $\max \emptyset = -\infty$ and $\min \emptyset = \infty$. + +## Decision Variables + +- $F \subseteq E$: the set of edges selected for the spanning subgraph $T = (V, F)$. (The vertex set of $T$ is fixed to $V$; $T$ *spans* $G$.) + +## Objective + +Find a connected spanning subgraph $T = (V, F)$, $F \subseteq E$, of $G$ that minimizes $$\begin{equation*} + \sum_{v \in V} \ \max_{\{u, v\} \in F} w(\{u, v\}). + \tag{Problem 1.1} +\end{equation*}$$ That is, each vertex $v$ pays the most expensive edge incident to it in the solution, and the cost of $T$ is the sum of these payments. The minimum cost of a solution to an instance $I = (G, w)$ is denoted $\mathrm{Opt}(I)$. + +## Constraints + +$$\begin{align*} + & T = (V, F), \quad F \subseteq E + && \text{(spanning subgraph: vertex set is all of $V$)} \tag{C1} \\ + & T = (V, F) \text{ is connected} + && \text{(every pair of vertices joined by a path in $F$)} \tag{C2} +\end{align*}$$ diff --git a/bentert2022/problem_description.txt b/bentert2022/problem_description.txt new file mode 100644 index 0000000000000000000000000000000000000000..7d49fdd11d87a42a4a712d96992b15741f2a96ad --- /dev/null +++ b/bentert2022/problem_description.txt @@ -0,0 +1,7 @@ +# Problem Description + +A wireless sensor network consists of a set of sensors (nodes) and a set of possible communication links (edges) between pairs of distinct sensors, forming a connected undirected simple graph. Each link has a non-negative integer weight representing the power cost required to maintain that link, where in typical instances weights equal the squared Euclidean distance between the two sensors' positions. + +The network operator must select a subset of communication links such that the resulting subgraph spans all sensors (every sensor is included) and is connected (every sensor can reach every other sensor through the chosen links). The power cost paid by each sensor equals the maximum weight among all chosen links incident to that sensor. The goal is to minimize the total power cost across all sensors, defined as the sum over all sensors of the maximum-weight chosen link incident to each sensor. + +The input specifies the number of sensors, the number of links, the two-dimensional coordinates of each sensor, and the weight of each link. The graph over which the optimization takes place is complete, meaning every pair of sensors is connected by a link whose weight is the squared Euclidean distance between them. diff --git a/bentert2022/solution_logger.py b/bentert2022/solution_logger.py new file mode 100644 index 0000000000000000000000000000000000000000..5393fa935ce71df9da95cee04fe5199077cb8461 --- /dev/null +++ b/bentert2022/solution_logger.py @@ -0,0 +1,61 @@ +""" +Convergence logger for optimization algorithms. + +Records incumbent solutions with timestamps to a JSONL file. +This module is provided to LLM-generated programs — they only need to call +`log(objective_value)` whenever a better feasible solution is found. + +Usage in generated code: + from solution_logger import SolutionLogger + logger = SolutionLogger(log_path, sense="minimize") # or "maximize" + # ... inside algorithm loop: + logger.log(objective_value) +""" + +import json +import time + + +class SolutionLogger: + def __init__(self, log_path, sense="minimize"): + """ + Args: + log_path: Path to the JSONL output file. + sense: "minimize" or "maximize". + """ + self.log_path = log_path + self.sense = sense + self.start_time = time.time() + self.best_obj = None + self.min_interval = 0.1 # seconds, avoid excessive writes + + self._last_log_time = 0.0 + # Clear the file + with open(self.log_path, "w") as f: + pass + + def log(self, objective_value): + """Record a new incumbent if it improves on the best known.""" + if objective_value is None: + return + + # Check if this is an improvement + if self.best_obj is not None: + if self.sense == "minimize" and objective_value >= self.best_obj: + return + if self.sense == "maximize" and objective_value <= self.best_obj: + return + + elapsed = time.time() - self.start_time + + # Throttle writes + if self.best_obj is not None and elapsed - self._last_log_time < self.min_interval: + self.best_obj = objective_value + return + + self.best_obj = objective_value + self._last_log_time = elapsed + + with open(self.log_path, "a") as f: + f.write(json.dumps({"time": round(elapsed, 3), + "objective_value": objective_value}) + "\n") diff --git a/bentert2022/solution_schema.json b/bentert2022/solution_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..e8f817bf4bc42c647504b9b5ca3aff77f54619c6 --- /dev/null +++ b/bentert2022/solution_schema.json @@ -0,0 +1,5 @@ +{ + "objective_value": " Total power cost across all sensors, where each sensor's cost is the maximum weight among its chosen links.", + "selected_edges": " Pairs of sensor identifiers for each communication link chosen in the spanning subgraph.", + "vertex_costs": " Power cost paid by each sensor, equal to the maximum weight among chosen links incident to that sensor." +} diff --git a/bergman2019/feasibility_check.py b/bergman2019/feasibility_check.py new file mode 100644 index 0000000000000000000000000000000000000000..a954ca03e85630d27056a19621aa2365efed2a96 --- /dev/null +++ b/bergman2019/feasibility_check.py @@ -0,0 +1,438 @@ +""" +Feasibility checker for the Quadratic Multiknapsack Problem (QMKP-QP). +Source: Bergman (2019), INFORMS Journal on Computing. + +Constraints (numbered top-to-bottom from the QMKP-QP formulation): + 1. Capacity: sum_i w_i * x_{i,k} <= C_k, for all k in [m] + 2. Assignment: sum_k x_{i,k} <= 1, for all i in [n] + 3. Binary: x_{i,k} in {0,1}, for all i in [n], k in [m] + 6. Objective consistency: reported objective_value must equal the + recomputed QMKP-QP objective + sum_{i,k} p_i * x_{i,k} + + sum_{i 1e-6 + ): + violations.append( + f"{target}={reported} does not match the decision-derived " + f"value {expected}" + ) + + if "objective_value" in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS: + objective = solution.get("objective_value") + if ( + isinstance(objective, bool) + or not isinstance(objective, (int, float)) + or objective != objective + or objective in (float("inf"), float("-inf")) + ): + violations.append("objective_value must be a finite JSON number") + + if not violations: + return None + return { + "feasible": False, + "violated_constraints": ["solution_schema"], + "violations": violations, + "violation_magnitudes": [], + } + + +def _frontieror_write_unexpected_rejection(error): + """Turn malformed candidate/checker exceptions into a normal rejection.""" + import json as _frontieror_json + import sys as _frontieror_sys + + result_path = None + for index, argument in enumerate(_frontieror_sys.argv): + if argument == "--result_path" and index + 1 < len(_frontieror_sys.argv): + result_path = _frontieror_sys.argv[index + 1] + break + if argument.startswith("--result_path="): + result_path = argument.split("=", 1)[1] + break + if not result_path: + return False + try: + with open(result_path, "w") as result_handle: + _frontieror_json.dump( + { + "feasible": False, + "violated_constraints": ["malformed_solution"], + "violations": [ + "checker rejected malformed candidate: " + + type(error).__name__ + + ": " + + str(error)[:500] + ], + "violation_magnitudes": [], + }, + result_handle, + indent=2, + ) + except Exception: + return False + return True + + + +def check_feasibility(instance, solution): + tol = 1e-5 + eps = 1e-5 + + n = instance["n"] + m = instance["m"] + weights = instance["weights"] + capacities = instance["capacities"] + + assignment = solution.get("assignment", []) + + violations = [] + violation_magnitudes = [] + violated_set = set() + + # ── Constraint 3 (index validity): validate BEFORE downstream lookups. + # Fix_7 reviewer item 2: weights[i] was indexed before the bounds + # check ran, so i >= n would raise IndexError and i < 0 would silently + # wrap via Python's negative indexing. + # Fix_7 reviewer item 1: assignment is now a list of [item, knapsack] + # pairs (schema reshape) so multi-assignment is expressible and + # constraint 2 below is no longer a tautology. + valid_pairs = [] + for entry in assignment: + item = int(entry[0]) + kk = int(entry[1]) + item_ok = 0 <= item < n + k_ok = 0 <= kk < m + if not item_ok: + violated_set.add(3) + violations.append( + f"Item index {item} out of range [0, {n-1}]" + ) + violation_magnitudes.append({ + "constraint": 3, + "lhs": float(item), + "rhs": float(n - 1), + "raw_excess": float(abs(item - (n - 1)) if item >= n else abs(item)), + "normalizer": max(float(n - 1), eps), + "ratio": float(abs(item - (n - 1)) if item >= n else abs(item)) + / max(float(n - 1), eps), + }) + if not k_ok: + violated_set.add(3) + violations.append( + f"Knapsack index {kk} for item {item} out of range [0, {m-1}]" + ) + violation_magnitudes.append({ + "constraint": 3, + "lhs": float(kk), + "rhs": float(m - 1), + "raw_excess": float(abs(kk - (m - 1)) if kk >= m else abs(kk)), + "normalizer": max(float(m - 1), eps), + "ratio": float(abs(kk - (m - 1)) if kk >= m else abs(kk)) + / max(float(m - 1), eps), + }) + if item_ok and k_ok: + valid_pairs.append((item, kk)) + + # ── Constraint 1: Capacity ── + # sum_i w_i * x_{i,k} <= C_k, for each k in [m] + for k in range(m): + lhs = sum(weights[i] for i, kk in valid_pairs if kk == k) + rhs = capacities[k] + violation_amount = lhs - rhs # for <= constraint + if violation_amount > tol: + violated_set.add(1) + normalizer = max(abs(rhs), eps) + ratio = violation_amount / normalizer + violations.append( + f"Capacity exceeded on knapsack {k}: " + f"total weight {lhs} > capacity {rhs}" + ) + violation_magnitudes.append({ + "constraint": 1, + "lhs": float(lhs), + "rhs": float(rhs), + "raw_excess": float(violation_amount), + "normalizer": float(normalizer), + "ratio": float(ratio), + }) + + # ── Constraint 2: Assignment ── + # sum_k x_{i,k} <= 1, for each i in [n]. + # The list-of-pairs schema lets the same item index appear more than + # once; the prior dict-keyed schema structurally collapsed duplicates, + # making this branch unreachable. + counts = {} + for i, kk in valid_pairs: + counts[i] = counts.get(i, 0) + 1 + for i, count in counts.items(): + violation_amount = count - 1 + if violation_amount > tol: + violated_set.add(2) + normalizer = max(abs(1), eps) + ratio = violation_amount / normalizer + violations.append( + f"Item {i} assigned to {count} knapsacks (max 1 allowed)" + ) + violation_magnitudes.append({ + "constraint": 2, + "lhs": float(count), + "rhs": float(1), + "raw_excess": float(violation_amount), + "normalizer": float(normalizer), + "ratio": float(ratio), + }) + + _domain_check_vars_binary = [] + _domain_check_vars_integer = [("assignment", assignment)] + + # ===================================================================== + # Variable Domain Checks (auto-generated by add_domain_checks.py) + # ===================================================================== + # Constraint 4: Binary domain — variables must be 0 or 1 + for var_name, var_dict in _domain_check_vars_binary: + if isinstance(var_dict, dict): + for key, val in var_dict.items(): + try: + v = float(val) + except (TypeError, ValueError): + continue + if abs(v - round(v)) > tol or round(v) not in (0, 1): + viol = min(abs(v - 0), abs(v - 1)) + if viol > tol: + violated_constraints.add(4) + violations.append( + f"Constraint 4 (binary domain): {var_name}[{key}] = {v} not in {0, 1}") + violation_magnitudes.append({ + "constraint": 4, + "lhs": v, + "rhs": 1.0, + "raw_excess": float(viol), + "normalizer": 1.0, + "ratio": float(viol), + }) + + # Constraint 5: Integer domain — variables must be integral + for var_name, var_dict in _domain_check_vars_integer: + if isinstance(var_dict, dict): + for key, val in var_dict.items(): + try: + v = float(val) + except (TypeError, ValueError): + continue + frac = abs(v - round(v)) + if frac > tol: + violated_constraints.add(5) + violations.append( + f"Constraint 5 (integer domain): {var_name}[{key}] = {v} is not integer") + violation_magnitudes.append({ + "constraint": 5, + "lhs": v, + "rhs": round(v), + "raw_excess": float(frac), + "normalizer": max(abs(round(v)), eps), + "ratio": float(frac / max(abs(round(v)), eps)), + }) + + # ── Constraint 6: Objective consistency (Tier C) ── + # Recompute the QMKP-QP objective from the solution's assignment and + # compare against the reported objective_value. The solution contains + # every variable the objective depends on (x_{i,k}), so a full + # recompute applies. Profits and pairwise_profits are integers per + # math_model.txt, so the true objective is integral; a tolerance of + # max(0.5, 1e-6 * |true_obj|) flags any integer-level mismatch while + # absorbing float-formatting noise (e.g. trailing ".0" round-trips). + profits = instance.get("profits") + pairwise_profits = instance.get("pairwise_profits") + reported_obj = solution.get("objective_value") + if profits is not None and pairwise_profits is not None and reported_obj is not None: + try: + reported = float(reported_obj) + except (TypeError, ValueError): + reported = None + if reported is not None: + # Dedup per knapsack: x_{i,k} is binary, so an item appearing + # twice in the same knapsack contributes only once to the obj + # (the duplicate is already flagged separately by constraint 2). + items_per_knap = {} + for i, kk in valid_pairs: + items_per_knap.setdefault(kk, set()).add(i) + linear_part = 0.0 + for kk, items in items_per_knap.items(): + for i in items: + linear_part += float(profits[i]) + quad_part = 0.0 + for kk, items in items_per_knap.items(): + items_sorted = sorted(items) + for a in range(len(items_sorted)): + i = items_sorted[a] + row_i = pairwise_profits[i] + for b in range(a + 1, len(items_sorted)): + j = items_sorted[b] + quad_part += float(row_i[j]) + true_obj = linear_part + quad_part + abs_diff = abs(reported - true_obj) + tol_obj = max(0.5, 1e-6 * abs(true_obj)) + if abs_diff > tol_obj: + violated_set.add(6) + normalizer = max(abs(true_obj), eps) + violations.append( + f"Objective consistency violated: reported objective_value=" + f"{reported} differs from recomputed QMKP-QP objective=" + f"{true_obj} (|diff|={abs_diff:.6g}, tol={tol_obj:.6g})" + ) + violation_magnitudes.append({ + "constraint": 6, + "lhs": float(reported), + "rhs": float(true_obj), + "raw_excess": float(abs_diff), + "normalizer": float(normalizer), + "ratio": float(abs_diff / normalizer), + }) + + feasible = len(violated_set) == 0 + violated_constraints = sorted(violated_set) + + return { + "feasible": feasible, + "violated_constraints": violated_constraints, + "violations": violations, + "violation_magnitudes": violation_magnitudes, + } + + +def main(): + parser = argparse.ArgumentParser( + description="Feasibility checker for QMKP-QP (Bergman 2019)" + ) + parser.add_argument( + "--instance_path", type=str, required=True, + help="Path to the JSON file containing the data instance." + ) + parser.add_argument( + "--solution_path", type=str, required=True, + help="Path to the JSON file containing the candidate solution." + ) + parser.add_argument( + "--result_path", type=str, required=True, + help="Path to write the JSON file containing the feasibility result." + ) + args = parser.parse_args() + + with open(args.instance_path, "r") as f: + instance = json.load(f) + with open(args.solution_path, "r") as f: + solution = json.load(f) + _frontieror_contract_result = _frontieror_validate_solution_contract(solution) + if _frontieror_contract_result is not None: + with open(args.result_path, "w") as _frontieror_result_handle: + json.dump( + _frontieror_contract_result, + _frontieror_result_handle, + indent=2, + ) + return + + result = check_feasibility(instance, solution) + + with open(args.result_path, "w") as f: + json.dump(result, f, indent=2) + + print(f"Feasible: {result['feasible']}") + if result['violated_constraints']: + print(f"Violated constraints: {result['violated_constraints']}") + for v in result['violations']: + print(f" - {v}") + + +if __name__ == "__main__": + try: + main() + except Exception as _frontieror_unexpected_error: + if not _frontieror_write_unexpected_rejection( + _frontieror_unexpected_error + ): + raise diff --git a/bergman2019/gurobi_code.py b/bergman2019/gurobi_code.py new file mode 100644 index 0000000000000000000000000000000000000000..4d1a596d851d398279365b9c4aa8a224f57fd5f3 --- /dev/null +++ b/bergman2019/gurobi_code.py @@ -0,0 +1,158 @@ +""" +Gurobi implementation of the Quadratic Multiknapsack Problem (QMKP-QP). +Source: Bergman (2019), "An Exact Algorithm for the Quadratic Multiknapsack Problem + with an Application to Event Seating", INFORMS Journal on Computing. + +Model: QMKP-QP (Section 3.1) + maximize sum_{i,k} p_i * x_{i,k} + + sum_{i 0): + obj_val = model.ObjVal + # Fix_7: emit list-of-pairs so the feasibility checker can detect + # multi-assignment violations; the prior dict keying collapsed + # duplicate item keys, making constraint 2 unreachable. + assignment = [] + for i in range(n): + for k in range(m): + if x[i, k].X > 0.5: + assignment.append([i, k]) + + result["objective_value"] = obj_val + result["assignment"] = assignment + result["status"] = "optimal" if status == GRB.OPTIMAL else "time_limit_feasible" + result["gap"] = model.MIPGap if model.SolCount > 0 else None + elif status == GRB.INFEASIBLE: + result["objective_value"] = None + result["status"] = "infeasible" + else: + result["objective_value"] = None + result["status"] = "no_solution_found" + + return result + + +def main(): + parser = argparse.ArgumentParser( + description="Gurobi solver for the Quadratic Multiknapsack Problem (QMKP-QP)" + ) + parser.add_argument("--instance_path", type=str, required=True, + help="Path to the JSON file containing the problem instance.") + parser.add_argument("--solution_path", type=str, required=True, + help="Path where the final solution JSON file must be written.") + parser.add_argument("--time_limit", type=int, required=True, + help="Maximum solver runtime in seconds.") + parser.add_argument("--log_path", type=str, default=None, help="Path to log incumbent solutions") + args = parser.parse_args() + install_gurobi_logger(args.log_path) + + instance = load_instance(args.instance_path) + result = solve_qmkp(instance, args.time_limit) + + with open(args.solution_path, "w") as f: + json.dump(result, f, indent=2) + + print(f"Objective value: {result['objective_value']}") + print(f"Status: {result['status']}") + print(f"Solution written to: {args.solution_path}") + + +if __name__ == "__main__": + main() diff --git a/bergman2019/gurobi_feasi_result/large_feasi_result_1.json b/bergman2019/gurobi_feasi_result/large_feasi_result_1.json new file mode 100644 index 0000000000000000000000000000000000000000..e305b82c3d6732211d5033415df1a105476e9cf1 --- /dev/null +++ b/bergman2019/gurobi_feasi_result/large_feasi_result_1.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3269392edcbb7720882e77caadb88b4a9cf2fda04cf482cebd23baace4a531fd +size 102 diff --git a/bergman2019/gurobi_feasi_result/large_feasi_result_2.json b/bergman2019/gurobi_feasi_result/large_feasi_result_2.json new file mode 100644 index 0000000000000000000000000000000000000000..e305b82c3d6732211d5033415df1a105476e9cf1 --- /dev/null +++ b/bergman2019/gurobi_feasi_result/large_feasi_result_2.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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a/bergman2019/instance_schema.json b/bergman2019/instance_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..fe8fc39e3334de7826469c52dbab0b0b5ab56fd2 --- /dev/null +++ b/bergman2019/instance_schema.json @@ -0,0 +1,8 @@ +{ + "n": " Number of items available for assignment to knapsacks.", + "m": " Number of knapsacks available to hold items.", + "profits": " Individual profit earned when each item is placed in any knapsack.", + "pairwise_profits": " Symmetric matrix of bonus profit earned when two items are placed together in the same knapsack, with zero diagonal.", + "weights": " Weight consumed by each item when placed in a knapsack.", + "capacities": " Maximum total weight each knapsack can hold." +} diff --git a/bergman2019/mathematical_formulation.md b/bergman2019/mathematical_formulation.md new file mode 100644 index 0000000000000000000000000000000000000000..65f0b88bb8a07cbba01710bbd5d07bf6792d6ad0 --- /dev/null +++ b/bergman2019/mathematical_formulation.md @@ -0,0 +1,49 @@ +# Original Formulation: Quadratic Multiknapsack Problem (QMKP) + +*Source: An Exact Algorithm for the Quadratic Multiknapsack Problem with an Application to Event Seating, David Bergman, 2019 (INFORMS Journal on Computing).* + +## Sets and Indices + +- $n \in \mathbb{Z}^+$: number of items; $[n] := \{1,\dots,n\}$. + +- $m \in \mathbb{Z}^+$: number of knapsacks; $[m] := \{1,\dots,m\}$. + +- $i,j \in [n]$: item indices. + +- $k \in [m]$: knapsack index. + +## Parameters + +- $p_i \in \mathbb{Z}$: individual profit of item $i \in [n]$ (not required to be nonnegative). + +- $p_{i,j} \in \mathbb{Z}$: pairwise profit of distinct items $i,j \in [n]$, with $p_{i,j} = p_{j,i}$ (not required to be nonnegative). + +- $w_i \geq 0$: weight of item $i \in [n]$. + +- $C_k \in \mathbb{Z}^+$: capacity of knapsack $k \in [m]$. + +## Decision Variables + +- $x_{i,k} \in \{0,1\}$ for $i \in [n],\, k \in [m]$: equals $1$ iff item $i$ is placed in knapsack $k$. + +## Objective + +$$\begin{align} +\text{maximize} \quad + & \sum_{i=1}^{n}\sum_{k=1}^{m} p_i\, x_{i,k} + + \sum_{i=1}^{n-1}\sum_{j=i+1}^{n}\sum_{k=1}^{m} x_{i,k}\, x_{j,k}\, p_{i,j} + \notag +\end{align}$$ + +## Constraints + +$$\begin{align} +\text{subject to} \quad + & \sum_{i=1}^{n} w_i\, x_{i,k} \leq C_k, + && k \in [m], \\ + & \sum_{k=1}^{m} x_{i,k} \leq 1, + && i \in [n], \\ + & x_{i,k} \in \{0,1\}, + && i \in [n],\; k \in [m]. + \tag{QMKP-QP} +\end{align}$$ diff --git a/bergman2019/problem_description.txt b/bergman2019/problem_description.txt new file mode 100644 index 0000000000000000000000000000000000000000..c0cde24b9afd971332ff8281ea036d9bff66995a --- /dev/null +++ b/bergman2019/problem_description.txt @@ -0,0 +1,5 @@ +# Problem Description + +A collection of items must be allocated among a set of knapsacks so as to maximize total profit. Each item has an individual profit (which may be negative) and a weight (which may be zero). Each distinct pair of items has a pairwise profit (which may also be negative), and the pairwise profit matrix is symmetric. Each knapsack has a positive integer capacity. The planner must decide, for each item, whether to place it in one of the knapsacks or to leave it unassigned. Each item may be placed in at most one knapsack. The total weight of items assigned to any single knapsack must not exceed that knapsack's capacity. The total profit to be maximized is the sum of two components: first, the individual profits of all items that are placed in some knapsack, and second, for every pair of distinct items that are placed together in the same knapsack, the pairwise profit associated with that pair. + +The input data specifies the number of items, the number of knapsacks, a vector of individual profits (one integer per item, not necessarily nonnegative), a symmetric matrix of pairwise profits (one integer per unordered pair of distinct items, not necessarily nonnegative), a vector of item weights (one nonnegative value per item), and a vector of knapsack capacities (one positive integer per knapsack). The goal is to determine an assignment of items to knapsacks that maximizes the sum of the individual profits of all assigned items plus the sum of the pairwise profits of every co-assigned pair, while respecting the capacity of every knapsack and placing each item in at most one knapsack. diff --git a/bergman2019/solution_logger.py b/bergman2019/solution_logger.py new file mode 100644 index 0000000000000000000000000000000000000000..5393fa935ce71df9da95cee04fe5199077cb8461 --- /dev/null +++ b/bergman2019/solution_logger.py @@ -0,0 +1,61 @@ +""" +Convergence logger for optimization algorithms. + +Records incumbent solutions with timestamps to a JSONL file. +This module is provided to LLM-generated programs — they only need to call +`log(objective_value)` whenever a better feasible solution is found. + +Usage in generated code: + from solution_logger import SolutionLogger + logger = SolutionLogger(log_path, sense="minimize") # or "maximize" + # ... inside algorithm loop: + logger.log(objective_value) +""" + +import json +import time + + +class SolutionLogger: + def __init__(self, log_path, sense="minimize"): + """ + Args: + log_path: Path to the JSONL output file. + sense: "minimize" or "maximize". + """ + self.log_path = log_path + self.sense = sense + self.start_time = time.time() + self.best_obj = None + self.min_interval = 0.1 # seconds, avoid excessive writes + + self._last_log_time = 0.0 + # Clear the file + with open(self.log_path, "w") as f: + pass + + def log(self, objective_value): + """Record a new incumbent if it improves on the best known.""" + if objective_value is None: + return + + # Check if this is an improvement + if self.best_obj is not None: + if self.sense == "minimize" and objective_value >= self.best_obj: + return + if self.sense == "maximize" and objective_value <= self.best_obj: + return + + elapsed = time.time() - self.start_time + + # Throttle writes + if self.best_obj is not None and elapsed - self._last_log_time < self.min_interval: + self.best_obj = objective_value + return + + self.best_obj = objective_value + self._last_log_time = elapsed + + with open(self.log_path, "a") as f: + f.write(json.dumps({"time": round(elapsed, 3), + "objective_value": objective_value}) + "\n") diff --git a/bergman2019/solution_schema.json b/bergman2019/solution_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..552389cafe7b51e4722bfa851329ef53b73f9633 --- /dev/null +++ b/bergman2019/solution_schema.json @@ -0,0 +1,4 @@ +{ + "objective_value": " Total profit from individual item profits plus pairwise profits of items sharing a knapsack.", + "assignment": " List of [item_index, knapsack_index] pairs; item indices 0-indexed in [0, n-1] and knapsack indices 0-indexed in [0, m-1]. Unassigned items are omitted. An item index appearing more than once indicates a multi-assignment infeasibility (math_model label {assign})." +} diff --git a/bertsimas2022/feasibility_check.py b/bertsimas2022/feasibility_check.py new file mode 100644 index 0000000000000000000000000000000000000000..b7654733c868180f644295d77406e2512df4578f --- /dev/null +++ b/bertsimas2022/feasibility_check.py @@ -0,0 +1,403 @@ +""" +Feasibility checker for sparse portfolio selection solutions. + +Checks candidate solutions against the hard constraints from: + Bertsimas and Cory-Wright (2022), + "A Scalable Algorithm for Sparse Portfolio Selection" + +Constraint numbering (first unique appearance, top to bottom in formulation section): + C1: e^T x = 1 (budget constraint, Problem 1) + C2: l <= A x <= u (linear constraints, Problem 2) + - C2: mu^T x >= r_bar (min return, when applicable) + - C2: x_i >= l_i * z_i (min investment, when applicable) + - C2: x_i <= u_i * z_i (max investment, when applicable) + C3: ||x||_0 <= k (cardinality on x, Problem 2) + C4: x_i = 0 if z_i = 0 forall i (linking, Problem 3) + C5: x_i^2 <= z_i * theta_i forall i (perspective, Problem 5; gurobi only) + C6: x_i >= 0 forall i (non-negativity of x, domain) + C7: z_i in {0,1} forall i (binary z, domain) + C8: e^T z <= k (cardinality on z, domain) + C9: theta_i >= 0 forall i (non-negativity of theta, domain; gurobi only) + C10: objective_value matches the recomputed objective (Tier C obj-recompute) +""" + +import argparse +import json +import numpy as np + +# FRONTIEROR_RELEASE_CONTRACT_V2 +_FRONTIEROR_REQUIRED_SOLUTION_FIELDS = ('objective_value', 'x') +_FRONTIEROR_NONEMPTY_SOLUTION_FIELDS = () +_FRONTIEROR_DERIVED_FIELD_RULES = () +_FRONTIEROR_CONDITIONAL_FIELD_RULES = () + + +def _frontieror_validate_solution_contract(solution): + """Return a structured rejection when the public solution contract is absent.""" + violations = [] + if not isinstance(solution, dict): + violations.append("solution must be a JSON object") + else: + missing = [ + field + for field in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS + if field not in solution or solution[field] is None + ] + if missing: + violations.append( + "missing required solution field(s): " + ", ".join(missing) + ) + + for discriminator, expected_value, fields in _FRONTIEROR_CONDITIONAL_FIELD_RULES: + if solution.get(discriminator) != expected_value: + continue + conditional_missing = [ + field + for field in fields + if field not in solution or solution[field] is None + ] + if conditional_missing: + violations.append( + "missing conditionally required solution field(s): " + + ", ".join(conditional_missing) + ) + + empty = [ + field + for field in _FRONTIEROR_NONEMPTY_SOLUTION_FIELDS + if field in solution and solution[field] in (None, [], {}) + ] + if empty: + violations.append( + "empty required decision witness field(s): " + ", ".join(empty) + ) + + for target, operation, source, source_key in _FRONTIEROR_DERIVED_FIELD_RULES: + try: + reported = float(solution[target]) + if operation == "length": + expected = float(len(solution[source])) + elif operation == "mapping_value": + expected = float(solution[source][source_key]) + else: + raise ValueError("unknown derived-field operation") + except (KeyError, TypeError, ValueError, OverflowError): + violations.append( + f"cannot derive {target} from decision witness field {source}" + ) + continue + if ( + reported != reported + or expected != expected + or reported in (float("inf"), float("-inf")) + or expected in (float("inf"), float("-inf")) + or abs(reported - expected) > 1e-6 + ): + violations.append( + f"{target}={reported} does not match the decision-derived " + f"value {expected}" + ) + + if "objective_value" in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS: + objective = solution.get("objective_value") + if ( + isinstance(objective, bool) + or not isinstance(objective, (int, float)) + or objective != objective + or objective in (float("inf"), float("-inf")) + ): + violations.append("objective_value must be a finite JSON number") + + if not violations: + return None + return { + "feasible": False, + "violated_constraints": ["solution_schema"], + "violations": violations, + "violation_magnitudes": [], + } + + +def _frontieror_write_unexpected_rejection(error): + """Turn malformed candidate/checker exceptions into a normal rejection.""" + import json as _frontieror_json + import sys as _frontieror_sys + + result_path = None + for index, argument in enumerate(_frontieror_sys.argv): + if argument == "--result_path" and index + 1 < len(_frontieror_sys.argv): + result_path = _frontieror_sys.argv[index + 1] + break + if argument.startswith("--result_path="): + result_path = argument.split("=", 1)[1] + break + if not result_path: + return False + try: + with open(result_path, "w") as result_handle: + _frontieror_json.dump( + { + "feasible": False, + "violated_constraints": ["malformed_solution"], + "violations": [ + "checker rejected malformed candidate: " + + type(error).__name__ + + ": " + + str(error)[:500] + ], + "violation_magnitudes": [], + }, + result_handle, + indent=2, + ) + except Exception: + return False + return True + + + +def check_feasibility(instance, solution): + tol = 1e-5 + eps = 1e-5 + + n = instance["n"] + k = instance["k"] + mu = np.array(instance["mu"], dtype=float) + + x = np.array(solution["x"], dtype=float) + # z and theta are reformulation auxiliaries (perspective MISOCO); the + # original Problem (4) only has x. We tolerate their absence. + z = (np.array(solution["z"], dtype=float) + if "z" in solution and solution["z"] is not None else None) + has_theta = "theta" in solution and solution["theta"] is not None + theta = np.array(solution["theta"], dtype=float) if has_theta else None + + constr = instance.get("constraints", {}) + has_min_return = constr.get("has_min_return_constraint", False) + r_bar = constr.get("r_bar", None) + has_min_inv = constr.get("has_min_investment_constraint", False) + l_min = constr.get("l_min_investment", None) + u_max = constr.get("u_max_investment", None) + + violations = [] + violation_magnitudes = [] + violated_set = set() + + def record(constraint_idx, msg, lhs, rhs, raw_excess): + violated_set.add(constraint_idx) + violations.append(msg) + normalizer = max(abs(rhs), eps) + ratio = raw_excess / normalizer + violation_magnitudes.append({ + "constraint": constraint_idx, + "lhs": float(lhs), + "rhs": float(rhs), + "raw_excess": float(raw_excess), + "normalizer": float(normalizer), + "ratio": float(ratio), + }) + + # ------------------------------------------------------------------ + # Constraint 1: e^T x = 1 (budget) + # ------------------------------------------------------------------ + lhs_c1 = float(np.sum(x)) + rhs_c1 = 1.0 + viol_c1 = abs(lhs_c1 - rhs_c1) + if viol_c1 > tol: + record(1, f"Budget constraint violated: sum(x)={lhs_c1:.8f}, expected 1.0", + lhs_c1, rhs_c1, viol_c1) + + # ------------------------------------------------------------------ + # Constraint 2: l <= Ax <= u (linear constraints, when applicable) + # ------------------------------------------------------------------ + # 2a: min return constraint: mu^T x >= r_bar + if has_min_return and r_bar is not None: + lhs_ret = float(mu @ x) + rhs_ret = float(r_bar) + viol_ret = rhs_ret - lhs_ret # >= constraint: violation if RHS > LHS + if viol_ret > tol: + record(2, f"Min return violated: mu^T x={lhs_ret:.8f} < r_bar={rhs_ret:.8f}", + lhs_ret, rhs_ret, viol_ret) + + # 2b: min investment threshold (semi-continuous on x): if x_i > 0 then x_i >= l_i. + if has_min_inv and l_min is not None: + l_arr = np.array(l_min, dtype=float) + for i in range(n): + if x[i] > tol: + lhs_mi = float(x[i]) + rhs_mi = float(l_arr[i]) + viol_mi = rhs_mi - lhs_mi # >= constraint + if viol_mi > tol: + record(2, f"Min investment violated for asset {i}: x[{i}]={lhs_mi:.8f} < l_min={rhs_mi:.8f}", + lhs_mi, rhs_mi, viol_mi) + + # 2c: max investment cap on x: x_i <= u_i. + if u_max is not None: + u_arr = np.array(u_max, dtype=float) + for i in range(n): + if x[i] > tol: + lhs_ui = float(x[i]) + rhs_ui = float(u_arr[i]) + viol_ui = lhs_ui - rhs_ui # <= constraint + if viol_ui > tol: + record(2, f"Max investment violated for asset {i}: x[{i}]={lhs_ui:.8f} > u_max={rhs_ui:.8f}", + lhs_ui, rhs_ui, viol_ui) + + # 2d: general linear constraints: l_lin <= A x <= u_lin + A_lin = constr.get("A_lin", None) + l_lin = constr.get("l_lin", None) + u_lin = constr.get("u_lin", None) + if A_lin is not None: + A_mat = np.array(A_lin, dtype=float) + Ax = A_mat @ x + m_lin = Ax.shape[0] + if l_lin is not None: + l_arr_lin = np.array(l_lin, dtype=float) + for j in range(m_lin): + viol_lj = float(l_arr_lin[j] - Ax[j]) + if viol_lj > tol: + record(2, f"General linear lower bound violated at row {j}: (Ax)[{j}]={float(Ax[j]):.8f} < l[{j}]={float(l_arr_lin[j]):.8f}", + float(Ax[j]), float(l_arr_lin[j]), viol_lj) + if u_lin is not None: + u_arr_lin = np.array(u_lin, dtype=float) + for j in range(m_lin): + viol_uj = float(Ax[j] - u_arr_lin[j]) + if viol_uj > tol: + record(2, f"General linear upper bound violated at row {j}: (Ax)[{j}]={float(Ax[j]):.8f} > u[{j}]={float(u_arr_lin[j]):.8f}", + float(Ax[j]), float(u_arr_lin[j]), viol_uj) + + # ------------------------------------------------------------------ + # Constraint 3: ||x||_0 <= k (cardinality) + # ------------------------------------------------------------------ + nnz_x = int(np.sum(np.abs(x) > tol)) + lhs_c3 = float(nnz_x) + rhs_c3 = float(k) + viol_c3 = lhs_c3 - rhs_c3 # <= constraint + if viol_c3 > tol: + record(3, f"Cardinality violated: ||x||_0={nnz_x} > k={k}", + lhs_c3, rhs_c3, viol_c3) + + # ------------------------------------------------------------------ + # NOTE: Constraints 4 (x_i=0 if z_i=0), 5 (perspective x_i^2 <= z_i*theta_i), + # 7 (z binary), 8 (e^T z <= k via z), and 9 (theta >= 0) all enforce the + # auxiliary variables z and theta of the perspective MISOCO + # reformulation (Problem 35). They are NOT constraints of the original + # Problem (4), whose only decision variable is x. Per project rule + # they are intentionally NOT enforced so that any solver that does + # not introduce z / theta is not falsely flagged as infeasible. + # Cardinality is verified above (C3) directly from x. + # ------------------------------------------------------------------ + + # ------------------------------------------------------------------ + # Constraint 6: x_i >= 0 (non-negativity) + # ------------------------------------------------------------------ + for i in range(n): + if x[i] < -tol: + lhs_c6 = float(x[i]) + rhs_c6 = 0.0 + viol_c6 = -x[i] # >= constraint: violation = RHS - LHS = -x[i] + record(6, f"Non-negativity violated: x[{i}]={x[i]:.8f} < 0", + lhs_c6, rhs_c6, viol_c6) + + # ------------------------------------------------------------------ + # Constraint 10: objective consistency (Tier C obj-recompute) + # The objective (Problem 34 / 4 equivalent) is fully determined by x: + # obj = (1/2) x^T Sigma x + (1/(2*gamma)) ||x||_2^2 - kappa * mu^T x + # where Sigma = F F^T + diag(idiosyncratic_variance). + # ------------------------------------------------------------------ + reported_obj = solution.get("objective_value") + if reported_obj is not None: + try: + reported = float(reported_obj) + except (TypeError, ValueError): + reported = None + if reported is not None: + try: + F = np.array(instance["factor_loadings"], dtype=float) + idio = np.array(instance["idiosyncratic_variance"], dtype=float) + gamma = float(instance["gamma"]) + kappa = float(instance["kappa"]) + except (KeyError, TypeError, ValueError): + F = idio = None + gamma = kappa = None + if F is not None and idio is not None and gamma is not None and gamma > 0: + Fx = F.T @ x + xSx = float(Fx @ Fx + np.sum(idio * x * x)) + ridge = float(x @ x) / (2.0 * gamma) + ret = float(mu @ x) + true_obj = 0.5 * xSx + ridge - kappa * ret + abs_diff = abs(reported - true_obj) + # 0.1% relative tolerance with 1e-6 absolute floor + obj_tol = max(1e-6, 1e-3 * abs(true_obj)) + if abs_diff > obj_tol: + record( + 10, + f"Objective consistency violated: reported objective_value=" + f"{reported} differs from recomputed " + f"(1/2)*x^T Sigma x + (1/(2*gamma))*||x||^2 - kappa*mu^T x=" + f"{true_obj} (|diff|={abs_diff:.3g}, tol={obj_tol:.3g})", + reported, true_obj, abs_diff, + ) + + # ------------------------------------------------------------------ + # Build result + # ------------------------------------------------------------------ + feasible = len(violated_set) == 0 + violated_constraints = sorted(violated_set) + + return { + "feasible": feasible, + "violated_constraints": violated_constraints, + "violations": violations, + "violation_magnitudes": violation_magnitudes, + } + + +def main(): + parser = argparse.ArgumentParser( + description="Feasibility checker for sparse portfolio selection (Bertsimas 2022)" + ) + parser.add_argument("--instance_path", type=str, required=True, + help="Path to the JSON instance file") + parser.add_argument("--solution_path", type=str, required=True, + help="Path to the JSON solution file") + parser.add_argument("--result_path", type=str, required=True, + help="Path to write the JSON feasibility result") + args = parser.parse_args() + + with open(args.instance_path, "r") as f: + instance = json.load(f) + with open(args.solution_path, "r") as f: + solution = json.load(f) + _frontieror_contract_result = _frontieror_validate_solution_contract(solution) + if _frontieror_contract_result is not None: + with open(args.result_path, "w") as _frontieror_result_handle: + json.dump( + _frontieror_contract_result, + _frontieror_result_handle, + indent=2, + ) + return + + result = check_feasibility(instance, solution) + + with open(args.result_path, "w") as f: + json.dump(result, f, indent=2) + + status = "FEASIBLE" if result["feasible"] else "INFEASIBLE" + print(f"Result: {status}") + if not result["feasible"]: + print(f"Violated constraints: {result['violated_constraints']}") + for v in result["violations"]: + print(f" - {v}") + print(f"Written to {args.result_path}") + + +if __name__ == "__main__": + try: + main() + except Exception as _frontieror_unexpected_error: + if not _frontieror_write_unexpected_rejection( + _frontieror_unexpected_error + ): + raise diff --git a/bertsimas2022/gurobi_code.py b/bertsimas2022/gurobi_code.py new file mode 100644 index 0000000000000000000000000000000000000000..7282f7418c91c5e6d6cc460d6c5f1d6f364afc83 --- /dev/null +++ b/bertsimas2022/gurobi_code.py @@ -0,0 +1,268 @@ +""" +Gurobi implementation of the MISOCO formulation (Problem 35) from: + Bertsimas and Cory-Wright (2022), + "A Scalable Algorithm for Sparse Portfolio Selection" + +Problem (35): + min_{z in Z_k^n, x in R^n_+, theta in R^n_+} + (1/2) x^T Sigma x + (1/(2*gamma)) * e^T theta - kappa * mu^T x + s.t. + e^T z <= k, + e^T x = 1, + l <= A x <= u (if any linear constraints), + x_i^2 <= z_i * theta_i for all i in [n] (perspective constraints) + +The covariance matrix is reconstructed from the factor model: + Sigma = F @ F^T + diag(idiosyncratic_variance) +where F = factor_loadings (n x r). + +Usage: + python gurobi_code.py --instance_path instance_1.json \ + --solution_path gurobi_solution_1.json \ + --time_limit 300 +""" + +import argparse +import json +import numpy as np +import gurobipy as gp +from gurobipy import GRB +import os as _os, sys as _sys +# Walk up from this file's directory to find repo root (containing scripts/). +_repo = _os.path.dirname(_os.path.abspath(__file__)) +while _repo != _os.path.dirname(_repo) and not _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _repo = _os.path.dirname(_repo) +if _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _sys.path.insert(0, _repo) +try: + from scripts.utils.gurobi_log_helper import install_gurobi_logger +except ImportError: + def install_gurobi_logger(log_path): # no-op fallback when scripts/ unavailable + pass + + +def project_x_to_support(x_raw, z_raw, k, n): + """Hard-project x onto the cardinality-feasible set with budget sum=1. + + The big-M perspective formulation can leak small nonzero x_i (~1e-4) at + time-out when the binary z_i has not been fully driven to 0. The original + Problem (4) requires ||x||_0 <= k, which the leaked x violates. We project + by: + 1. Pick the support: indices with z_i >= 0.5 (or top-k by |x_i| if z is + degenerate or violates the cardinality budget itself). + 2. Zero x outside the support; clamp to non-negativity. + 3. Renormalize so sum(x) = 1. + Returns (x_proj, sorted_support_indices). + """ + if z_raw is not None: + support = sorted(int(i) for i in np.where(z_raw >= 0.5)[0]) + else: + support = [] + if len(support) == 0 or len(support) > k: + # Fall back to magnitude-based top-k + order = np.argsort(-np.abs(x_raw)) + support = sorted(int(i) for i in order[:k]) + + x_proj = np.zeros(n, dtype=float) + for i in support: + x_proj[i] = max(float(x_raw[i]), 0.0) + s = x_proj.sum() + if s > 0: + x_proj /= s + return x_proj, support + + +def evaluate_objective(x, Sigma, gamma, kappa, mu): + """Objective with theta_i = x_i^2 (optimal given z_i=1 in the support).""" + quad = 0.5 * float(x @ Sigma @ x) + persp = (1.0 / (2.0 * gamma)) * float((x ** 2).sum()) + ret = kappa * float(mu @ x) + return quad + persp - ret + + +def main(): + parser = argparse.ArgumentParser( + description="Solve sparse portfolio selection via MISOCO (Gurobi)" + ) + parser.add_argument("--instance_path", type=str, required=True, + help="Path to the JSON instance file") + parser.add_argument("--solution_path", type=str, required=True, + help="Path to write the JSON solution file") + parser.add_argument("--time_limit", type=int, required=True, + help="Maximum solver runtime in seconds") + parser.add_argument("--log_path", type=str, default=None, help="Path to log incumbent solutions") + args = parser.parse_args() + install_gurobi_logger(args.log_path) + + # ------------------------------------------------------------------ # + # Load instance + # ------------------------------------------------------------------ # + with open(args.instance_path, "r") as fh: + inst = json.load(fh) + + n = inst["n"] # number of assets + k = inst["k"] # cardinality budget + gamma = inst["gamma"] # ridge regularizer + kappa = inst["kappa"] # return weight (0 or 1) + mu = np.array(inst["mu"], dtype=float) # expected returns (n,) + F = np.array(inst["factor_loadings"], dtype=float) # factor loadings (n, r) + eps_var = np.array(inst["idiosyncratic_variance"], dtype=float) # (n,) + + # ------------------------------------------------------------------ # + # Build covariance matrix: Sigma = F @ F^T + diag(eps_var) + # ------------------------------------------------------------------ # + Sigma = F @ F.T + np.diag(eps_var) # (n, n) positive definite + + # ------------------------------------------------------------------ # + # Read optional constraints + # ------------------------------------------------------------------ # + constr = inst.get("constraints", {}) + has_min_return = constr.get("has_min_return_constraint", False) + r_bar = constr.get("r_bar", None) + has_min_inv = constr.get("has_min_investment_constraint", False) + l_min = constr.get("l_min_investment", None) # per-asset lower bounds + u_max = constr.get("u_max_investment", None) # per-asset upper bounds + A_lin = inst.get("A", None) # (m, n) linear constraint matrix + l_lin = inst.get("l", None) # (m,) linear lower bounds + u_lin = inst.get("u", None) # (m,) linear upper bounds + + # ------------------------------------------------------------------ # + # Build Gurobi model + # ------------------------------------------------------------------ # + model = gp.Model("SparsePortfolio_MISOCO") + model.setParam("TimeLimit", args.time_limit) + model.setParam("Threads", 1) + model.setParam("OutputFlag", 1) + + # Decision variables + x = model.addVars(n, lb=0.0, name="x") # portfolio weights + z = model.addVars(n, vtype=GRB.BINARY, name="z") # support indicators + theta = model.addVars(n, lb=0.0, name="theta") # auxiliary perspective vars + + # ------------------------------------------------------------------ # + # Objective: (1/2) x^T Sigma x + (1/(2*gamma)) sum(theta) - kappa * mu^T x + # ------------------------------------------------------------------ # + # Build quadratic objective using the factor structure for efficiency: + # x^T Sigma x = x^T (F F^T + diag(eps)) x + # = ||F^T x||_2^2 + eps^T (x o x) + # We use auxiliary variables v = F^T x (r-dimensional) to keep the + # objective quadratic rather than constructing the dense n x n Sigma. + # For correctness on any instance size we use the dense Sigma here. + obj = gp.QuadExpr() + # Quadratic term: (1/2) x^T Sigma x + for i in range(n): + for j in range(n): + coeff = Sigma[i, j] + if abs(coeff) > 1e-16: + obj += 0.5 * coeff * x[i] * x[j] + # Regularization / perspective term: (1/(2*gamma)) sum theta_i + obj += (1.0 / (2.0 * gamma)) * gp.quicksum(theta[i] for i in range(n)) + # Return term: -kappa * mu^T x + obj -= kappa * gp.quicksum(mu[i] * x[i] for i in range(n)) + model.setObjective(obj, GRB.MINIMIZE) + + # ------------------------------------------------------------------ # + # Constraints + # ------------------------------------------------------------------ # + # Cardinality: sum(z) <= k + model.addConstr( + gp.quicksum(z[i] for i in range(n)) <= k, + name="cardinality" + ) + # Budget: sum(x) = 1 + model.addConstr( + gp.quicksum(x[i] for i in range(n)) == 1.0, + name="budget" + ) + # Perspective constraints: x_i^2 <= z_i * theta_i (rotated SOC) + for i in range(n): + model.addQConstr( + x[i] * x[i] <= z[i] * theta[i], + name=f"persp_{i}" + ) + + # Optional: minimum return constraint mu^T x >= r_bar + if has_min_return and r_bar is not None: + model.addConstr( + gp.quicksum(mu[i] * x[i] for i in range(n)) >= r_bar, + name="min_return" + ) + + # Optional: minimum investment x_i >= l_i * z_i (semi-continuous) + if has_min_inv and l_min is not None: + l_arr = np.array(l_min, dtype=float) + for i in range(n): + model.addConstr(x[i] >= l_arr[i] * z[i], name=f"min_inv_{i}") + + # Optional: maximum investment x_i <= u_i * z_i + if u_max is not None: + u_arr = np.array(u_max, dtype=float) + for i in range(n): + model.addConstr(x[i] <= u_arr[i] * z[i], name=f"max_inv_{i}") + + # General linear constraints: l <= A x <= u (math_model.txt eq (35)) + if A_lin is not None: + A_arr = np.array(A_lin, dtype=float) + l_arr_lin = np.array(l_lin, dtype=float) if l_lin is not None else None + u_arr_lin = np.array(u_lin, dtype=float) if u_lin is not None else None + for j in range(A_arr.shape[0]): + lhs = gp.quicksum(A_arr[j, i] * x[i] for i in range(n)) + if l_arr_lin is not None: + model.addConstr(lhs >= l_arr_lin[j], name=f"lin_lb_{j}") + if u_arr_lin is not None: + model.addConstr(lhs <= u_arr_lin[j], name=f"lin_ub_{j}") + + # ------------------------------------------------------------------ # + # Solve + # ------------------------------------------------------------------ # + model.optimize() + + # ------------------------------------------------------------------ # + # Extract and save solution + # ------------------------------------------------------------------ # + if model.SolCount > 0: + x_raw = np.array([x[i].X for i in range(n)]) + z_raw = np.array([z[i].X for i in range(n)]) + solver_obj_val = float(model.ObjVal) + + # Hard projection onto cardinality-feasible set: needed because at + # time-out the big-M perspective lets x_i leak ~1e-4 even when z_i is + # supposed to be 0, which would violate ||x||_0 <= k in the original + # problem. The projected x exactly satisfies cardinality and budget. + x_sol, support = project_x_to_support(x_raw, z_raw, k, n) + obj_val = evaluate_objective(x_sol, Sigma, gamma, kappa, mu) + else: + x_sol = None + support = None + solver_obj_val = None + obj_val = None + + solution = { + "instance_id": inst.get("instance_id", ""), + "objective_value": obj_val, # objective evaluated on projected x + # The original Problem (4) decision variable is x. z and theta are + # auxiliary variables of the perspective MISOCO reformulation + # (Problem 35) and are NOT emitted — feasibility_check verifies only + # the original problem against x. + "x": x_sol.tolist() if x_sol is not None else None, + "support": support, # indices with x_i > 0, len <= k + "solver_obj_val": solver_obj_val, # Gurobi's ObjVal on the un-projected x + "solver_status": model.Status, + "solver_status_str": { + 1: "LOADED", 2: "OPTIMAL", 3: "INFEASIBLE", + 4: "INF_OR_UNBD", 5: "UNBOUNDED", 9: "TIME_LIMIT", + }.get(model.Status, f"STATUS_{model.Status}"), + "mip_gap": model.MIPGap if model.SolCount > 0 else None, + "runtime_s": model.Runtime, + } + + with open(args.solution_path, "w") as fh: + json.dump(solution, fh, indent=2) + + print(f"Status : {solution['solver_status_str']}") + print(f"Obj val : {obj_val}") + print(f"Runtime : {model.Runtime:.2f}s") + + +if __name__ == "__main__": + main() diff --git a/bertsimas2022/gurobi_feasi_result/large_feasi_result_1.json b/bertsimas2022/gurobi_feasi_result/large_feasi_result_1.json new file mode 100644 index 0000000000000000000000000000000000000000..e305b82c3d6732211d5033415df1a105476e9cf1 --- /dev/null +++ b/bertsimas2022/gurobi_feasi_result/large_feasi_result_1.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3269392edcbb7720882e77caadb88b4a9cf2fda04cf482cebd23baace4a531fd +size 102 diff --git a/bertsimas2022/gurobi_feasi_result/large_feasi_result_2.json b/bertsimas2022/gurobi_feasi_result/large_feasi_result_2.json new file mode 100644 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b/bertsimas2022/instance_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..6707e63d11e225cb85ff6db7d7842dbfa8d608c8 --- /dev/null +++ b/bertsimas2022/instance_schema.json @@ -0,0 +1,18 @@ +{ + "n": " Number of securities in the investment universe.", + "k": " Maximum number of securities that may receive a positive allocation in the portfolio.", + "r": " Number of factors used in the factor model for the covariance structure.", + "gamma": " Ridge regularization parameter that penalizes concentrated portfolio positions.", + "gamma_formula": " Formula used to compute gamma from n.", + "kappa": " Multiplier controlling whether expected returns appear in the objective (1) or are handled via a separate minimum-return constraint (0).", + "mu": " Expected monthly return for each security.", + "factor_loadings": " Factor loading matrix whose outer product contributes the systematic component of the covariance matrix.", + "idiosyncratic_variance": " Per-security idiosyncratic variance forming the diagonal component of the covariance matrix.", + "constraints": { + "has_min_return_constraint": " Whether the portfolio must achieve a minimum expected return.", + "r_bar": " Minimum required expected portfolio return, or null if no such constraint applies.", + "has_min_investment_constraint": " Whether each selected security must receive at least a minimum allocation.", + "l_min_investment": " Minimum allocation required for each security if it is included in the portfolio, or null if no minimum investment constraint applies.", + "u_max_investment": " Maximum allocation allowed for each security if it is included in the portfolio, or null if no maximum investment constraint applies." + } +} diff --git a/bertsimas2022/mathematical_formulation.md b/bertsimas2022/mathematical_formulation.md new file mode 100644 index 0000000000000000000000000000000000000000..f397c0dfb94de24878f88343c0a10b9d65cae56b --- /dev/null +++ b/bertsimas2022/mathematical_formulation.md @@ -0,0 +1,50 @@ +# Original Formulation: Ridge-Regularized Sparse Portfolio Selection (SPS) + +*Source: A Scalable Algorithm for Sparse Portfolio Selection, Dimitris Bertsimas and Ryan Cory-Wright, 2022.* + +The problem the paper actually studies and solves to certifiable optimality is the ridge-regularized sparse portfolio selection model, Problem (4), introduced in Section 1.1 (“Problem Formulation and Main Contributions”) as the paper’s first main contribution. It augments the cardinality-constrained Markowitz model, Problem (2), with a ridge term $\tfrac{1}{2\gamma}\|\boldsymbol{x}\|_2^2$. Its notation ($\boldsymbol{x},\boldsymbol{\Sigma},\boldsymbol{\mu},\boldsymbol{A},\boldsymbol{l},\boldsymbol{u},k,\gamma$) is carried into every subsequent (algorithmic and experimental) section. The unregularized baseline (2), the convex mixed-integer quadratic reformulation (3), and the MISOCO reformulation (5) are listed under *Variants*. + +## Sets and Parameters + +- $n$ — number of securities in the universe; $[n] := \{1,\dots,n\}$. + +- $m$ — number of generic linear inequality rows. + +- $k \in \mathbb{Z}_{>0}$ — cardinality budget; maximum number of non-zero positions, with $k \ll n$. + +- $\boldsymbol{\mu} \in \mathbb{R}^n$ — vector of expected marginal returns. + +- $\boldsymbol{\Sigma} \in \mathbb{S}^n_+$ — positive semidefinite variance-covariance matrix of returns. + +- $\sigma \geq 0$ — scalar trade-off parameter controlling the portfolio’s risk-return trade-off. + +- $\gamma > 0$ — fixed ridge (concentration) regularization parameter. + +- $\boldsymbol{A} \in \mathbb{R}^{m \times n}$ — linear constraint matrix; $\boldsymbol{l} \in \mathbb{R}^m,\ \boldsymbol{u} \in \mathbb{R}^m$ — lower and upper bounds for the generic linear inequalities. + +- $\boldsymbol{e} \in \mathbb{R}^n$ — vector of all ones. + +## Decision Variables + +- $\boldsymbol{x} \in \mathbb{R}^n_+$ — portfolio allocation vector (fraction of capital in each security; continuous, non-negative, i.e. no short selling). + +The cardinality requirement is expressed directly on $\boldsymbol{x}$ via the $\ell_0$ “norm” $\|\boldsymbol{x}\|_0 = |\{ i : x_i \neq 0 \}|$. Binary indicators $\boldsymbol{z} \in \{0,1\}^n$ ($z_i = 1$ iff $x_i \neq 0$) are introduced only in the reformulations under *Variants*. + +## Objective + +$$\begin{equation} + \min_{\boldsymbol{x} \in \mathbb{R}^n_+}\; + \frac{\sigma}{2}\, \boldsymbol{x}^\top \boldsymbol{\Sigma} \boldsymbol{x} + + \frac{1}{2\gamma}\, \|\boldsymbol{x}\|_2^2 + - \boldsymbol{\mu}^\top \boldsymbol{x} + \tag{4-obj} +\end{equation}$$ + +## Constraints + +$$\begin{align} + \boldsymbol{l} \leq \boldsymbol{A}\boldsymbol{x} \leq \boldsymbol{u}, \qquad + \boldsymbol{e}^\top \boldsymbol{x} = 1, \qquad + \|\boldsymbol{x}\|_0 \leq k. + \tag{4} +\end{align}$$ (The objective and constraints together constitute Problem (4) of the paper, tagged (4) in the source.) diff --git a/bertsimas2022/problem_description.txt b/bertsimas2022/problem_description.txt new file mode 100644 index 0000000000000000000000000000000000000000..431b36bb984f0a5ccdc55b286e88e264a75ec5b3 --- /dev/null +++ b/bertsimas2022/problem_description.txt @@ -0,0 +1,7 @@ +# Problem Description + +An investor allocates a fixed pool of capital across a universe of securities. Each security has a known expected return per unit invested, and the pairwise risk of holding any two securities together is described by a symmetric covariance matrix whose eigenvalues are all at least zero. The covariance matrix may be supplied either directly or in factor-model form: a factor-loadings matrix (whose rows are factors and whose columns are securities) together with a per-security idiosyncratic-variance vector, in which case the full covariance equals the factor-loadings matrix multiplied by its transpose plus the diagonal matrix of idiosyncratic variances. Two trade-off parameters are also given: a return-weight that controls how strongly expected return is rewarded relative to risk, and a concentration parameter that controls how strongly the strategy is penalized for piling capital into too few securities. + +The investor must choose what fraction of the capital each security receives. The fractions are non-negative (no short selling is allowed) and sum to one (the entire pool is invested). At most a given maximum number of securities may receive a positive fraction; this maximum is much smaller than the total number of securities. The problem may also include a list of linear rules on the chosen fractions, each supplied as a row of coefficients together with a lower bound and an upper bound on the corresponding weighted sum; such rules can encode sector-exposure limits, position-size caps, or a minimum required overall expected return. When per-security minimum and maximum thresholds are provided, each security either receives no investment at all or receives a fraction lying between its given minimum and its given maximum. + +The goal is to minimize a total made up of three components. The first component is one half of the portfolio's variance, namely the symmetric weighted sum across every pair of securities of the corresponding covariance entry multiplied by the two chosen fractions. The second component is a concentration penalty equal to the sum of the squared chosen fractions divided by twice the concentration parameter. The third component, subtracted from the first two, is the return-weight multiplied by the portfolio's expected return, where the portfolio's expected return is the sum across securities of each security's expected return times its chosen fraction. diff --git a/bertsimas2022/solution_logger.py b/bertsimas2022/solution_logger.py new file mode 100644 index 0000000000000000000000000000000000000000..5393fa935ce71df9da95cee04fe5199077cb8461 --- /dev/null +++ b/bertsimas2022/solution_logger.py @@ -0,0 +1,61 @@ +""" +Convergence logger for optimization algorithms. + +Records incumbent solutions with timestamps to a JSONL file. +This module is provided to LLM-generated programs — they only need to call +`log(objective_value)` whenever a better feasible solution is found. + +Usage in generated code: + from solution_logger import SolutionLogger + logger = SolutionLogger(log_path, sense="minimize") # or "maximize" + # ... inside algorithm loop: + logger.log(objective_value) +""" + +import json +import time + + +class SolutionLogger: + def __init__(self, log_path, sense="minimize"): + """ + Args: + log_path: Path to the JSONL output file. + sense: "minimize" or "maximize". + """ + self.log_path = log_path + self.sense = sense + self.start_time = time.time() + self.best_obj = None + self.min_interval = 0.1 # seconds, avoid excessive writes + + self._last_log_time = 0.0 + # Clear the file + with open(self.log_path, "w") as f: + pass + + def log(self, objective_value): + """Record a new incumbent if it improves on the best known.""" + if objective_value is None: + return + + # Check if this is an improvement + if self.best_obj is not None: + if self.sense == "minimize" and objective_value >= self.best_obj: + return + if self.sense == "maximize" and objective_value <= self.best_obj: + return + + elapsed = time.time() - self.start_time + + # Throttle writes + if self.best_obj is not None and elapsed - self._last_log_time < self.min_interval: + self.best_obj = objective_value + return + + self.best_obj = objective_value + self._last_log_time = elapsed + + with open(self.log_path, "a") as f: + f.write(json.dumps({"time": round(elapsed, 3), + "objective_value": objective_value}) + "\n") diff --git a/bertsimas2022/solution_schema.json b/bertsimas2022/solution_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..a12dec3be21938e8da71bbedc76e929db9dd9563 --- /dev/null +++ b/bertsimas2022/solution_schema.json @@ -0,0 +1,4 @@ +{ + "objective_value": " Total cost combining portfolio variance, ridge regularization penalty, and weighted expected return.", + "x": " Fractional allocation of capital to each security in the portfolio." +} diff --git a/bertsimas2024/feasibility_check.py b/bertsimas2024/feasibility_check.py new file mode 100644 index 0000000000000000000000000000000000000000..d6328a0ffcde50a8935434d4b80d579948db20da --- /dev/null +++ b/bertsimas2024/feasibility_check.py @@ -0,0 +1,106 @@ +#!/usr/bin/env python3 +"""Solver-free checker for Bertsimas et al. (2024) MCFND.""" + +import argparse +import json +import math +import sys +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parent.parent)) +from solverless_witness import require_linked_variables, validate_complete_witness + + +def rejection(kind, messages): + return {"feasible": False, "violated_constraints": [kind], + "violations": messages, "violation_magnitudes": []} + + +def check_feasibility(instance, solution, instance_path): + errors = [] + if not isinstance(solution, dict): + return rejection("solution_schema", ["solution must be a JSON object"]) + for field in ("objective_value", "z", "model_variables"): + if field not in solution or solution[field] is None: + errors.append(f"missing required solution field: {field}") + if errors: + return rejection("solution_schema", errors) + + edge_count = int(instance["num_edges"]) + z_raw = solution["z"] + if not isinstance(z_raw, dict) or set(z_raw) != {str(edge) for edge in range(edge_count)}: + errors.append("z must contain every edge index exactly once") + z_raw = z_raw if isinstance(z_raw, dict) else {} + z = [] + for edge in range(edge_count): + raw = z_raw.get(str(edge), 0.0) + if isinstance(raw, bool) or not isinstance(raw, (int, float)): + errors.append(f"z[{edge}] must be binary") + value = 0.0 + else: + value = float(raw) + if not math.isfinite(value) or abs(value - round(value)) > 1e-5 or round(value) not in (0, 1): + errors.append(f"z[{edge}] must be binary") + z.append(value) + if errors: + return rejection("solution_schema", errors[:20]) + + violations = [] + if sum(z) > float(instance["c_0"]) + 1e-5: + violations.append(f"cardinality violated: sum(z)={sum(z)} > c_0={instance['c_0']}") + objective, witness_errors, diagnostics = validate_complete_witness( + instance_path, solution, Path(__file__).resolve().parent + ) + witness_errors.extend(require_linked_variables( + solution, {f"z[{edge}]": z[edge] for edge in range(edge_count)} + )) + violations.extend(witness_errors) + + reported = solution["objective_value"] + if isinstance(reported, bool) or not isinstance(reported, (int, float)) or not math.isfinite(float(reported)): + violations.append("objective_value must be a finite number") + elif objective is not None: + difference = abs(float(reported) - objective) + tolerance = max(1e-3, 1e-7 * max(abs(float(reported)), abs(objective))) + if difference > tolerance: + violations.append( + f"objective_value={reported} differs from complete-witness objective=" + f"{objective:.12g} by {difference:.6g} (tol={tolerance:.6g})" + ) + return { + "feasible": not violations, + "violated_constraints": [] if not violations else [1, 2, 3, 4, 5], + "violations": violations, + "violation_magnitudes": [] if not violations else [diagnostics], + } + + +def main(): + parser = argparse.ArgumentParser(description="Solver-free MCFND witness checker") + parser.add_argument("--instance_path", required=True) + parser.add_argument("--solution_path", required=True) + parser.add_argument("--result_path", required=True) + args = parser.parse_args() + with open(args.instance_path) as handle: + instance = json.load(handle) + with open(args.solution_path) as handle: + solution = json.load(handle) + result = check_feasibility(instance, solution, args.instance_path) + with open(args.result_path, "w") as handle: + json.dump(result, handle, indent=2) + + +if __name__ == "__main__": + try: + main() + except Exception as error: + result_path = None + for index, argument in enumerate(sys.argv): + if argument == "--result_path" and index + 1 < len(sys.argv): + result_path = sys.argv[index + 1] + elif argument.startswith("--result_path="): + result_path = argument.split("=", 1)[1] + if result_path is None: + raise + with open(result_path, "w") as handle: + json.dump(rejection("malformed_solution", [f"{type(error).__name__}: {str(error)[:500]}"]), handle, indent=2) diff --git a/bertsimas2024/gurobi_code.py b/bertsimas2024/gurobi_code.py new file mode 100644 index 0000000000000000000000000000000000000000..13811dd698f949c00670f0da008626a1b6c6669c --- /dev/null +++ b/bertsimas2024/gurobi_code.py @@ -0,0 +1,235 @@ +""" +Gurobi implementation of the Stochastic Multi-commodity Capacitated +Fixed-charge Network Design (MCFND) problem. + +From: Bertsimas et al. (2024), "A Stochastic Benders Decomposition Scheme + for Large-Scale Stochastic Network Design" + +Implements Problem (1) from page 4 of the paper directly as a monolithic +mixed-integer quadratic program solved by Gurobi. +""" + +import argparse +import json +import numpy as np +import gurobipy as gp +from gurobipy import GRB +import os as _os, sys as _sys +# Walk up from this file's directory to find repo root (containing scripts/). +_repo = _os.path.dirname(_os.path.abspath(__file__)) +while _repo != _os.path.dirname(_repo) and not _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _repo = _os.path.dirname(_repo) +if _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _sys.path.insert(0, _repo) +try: + from scripts.utils.gurobi_log_helper import install_gurobi_logger +except ImportError: + def install_gurobi_logger(log_path): # no-op fallback when scripts/ unavailable + pass +def load_instance(instance_path): + """Load a problem instance from a JSON file.""" + with open(instance_path, "r") as f: + data = json.load(f) + return data + + +def build_and_solve(data, time_limit): + """ + Build and solve the MCFND problem (1) from the paper. + + min sum_{(i,j) in E} c_{i,j} z_{i,j} + + (1/|R|) sum_{r in R} sum_{(i,j) in E} + [ sum_k f^k_{i,j} x^{k,r}_{i,j} + + (1/(2*gamma)) * (sum_k x^{k,r}_{i,j})^2 ] + + s.t. A x^{k,r} = d^{k,r} for all k, r + sum_k x^{k,r}_{i,j} <= u_{i,j} for all (i,j), r + x^{k,r}_{i,j} <= u_{i,j} * z_{i,j} (big-M linking) + sum_{(i,j)} z_{i,j} <= c_0 + z_{i,j} in {0,1}, x >= 0 + """ + num_nodes = data["num_nodes"] + num_commodities = data["num_commodities"] + num_scenarios = data["num_scenarios"] + num_edges = data["num_edges"] + gamma = data["gamma"] + c_0 = data["c_0"] + edges = data["edges"] # list of [i, j] + construction_costs = data["construction_costs"] # length num_edges + flow_costs = data["flow_costs"] # length num_edges (per-commodity costs) + capacities = data["capacities"] # length num_edges + commodity_destinations = data["commodity_destinations"] # length num_commodities + demands = data["demands"] # shape: [num_scenarios][num_commodities][num_nodes] + + # The flow_costs array has length num_edges. + # From the instance data, flow_costs = edge_lengths * 10 and is the same + # for all commodities (f^k_{i,j} = flow_costs[e] for all k). + # **INFERRED ASSUMPTION**: The paper states f^k_{i,j} is proportional to edge + # length (factor of 10). The instance provides a single flow_costs array of + # length num_edges. We assume f^k_{i,j} = flow_costs[e] for all k. + # This is consistent with the instance generation description. + + model = gp.Model("MCFND") + model.setParam("Threads", 1) + model.setParam("TimeLimit", time_limit) + model.setParam("OutputFlag", 1) + # NOTE: DualReductions=0 is applied conditionally after the first solve. + # Default presolve sometimes misclassifies the (feasible) MIQP as + # INF_OR_UNBD on large instances (~10s); we only fall back to + # DualReductions=0 when that happens, to avoid penalising small + # instances that converge fine under default presolve. + + # Decision variables + # z[e] binary design variables + z = model.addVars(num_edges, vtype=GRB.BINARY, name="z") + + # x[k, r, e] continuous flow variables + x = model.addVars( + num_commodities, num_scenarios, num_edges, + lb=0.0, vtype=GRB.CONTINUOUS, name="x" + ) + + # Auxiliary variables for the quadratic term: total_flow[r, e] = sum_k x[k,r,e] + total_flow = model.addVars( + num_scenarios, num_edges, + lb=0.0, vtype=GRB.CONTINUOUS, name="tf" + ) + + model.update() + + # Objective function + # Part 1: construction costs + obj = gp.quicksum(construction_costs[e] * z[e] for e in range(num_edges)) + + # Part 2: expected linear transportation and quadratic congestion costs. + # Keeping the quadratic term is essential: objective_value is part of the + # public contract and must describe the same problem as the formulation. + inv_R = 1.0 / num_scenarios + for r in range(num_scenarios): + for e in range(num_edges): + obj += inv_R * gp.quicksum( + flow_costs[e] * x[k, r, e] for k in range(num_commodities) + ) + obj += ( + inv_R + * (1.0 / (2.0 * gamma)) + * total_flow[r, e] + * total_flow[r, e] + ) + + model.setObjective(obj, GRB.MINIMIZE) + + # Constraints + + # Link total_flow to x + for r in range(num_scenarios): + for e in range(num_edges): + model.addConstr( + total_flow[r, e] == gp.quicksum(x[k, r, e] for k in range(num_commodities)), + name=f"tf_link_{r}_{e}" + ) + + # Constraint 1: Flow conservation A x^{k,r} = d^{k,r} for all k, r + # A is the node-arc incidence matrix: for edge e=(i,j), + # A[i,e] = +1 (outgoing), A[j,e] = -1 (incoming) + for k in range(num_commodities): + for r in range(num_scenarios): + for n in range(num_nodes): + # Net outflow at node n for commodity k, scenario r + outflow = gp.LinExpr() + for e in range(num_edges): + i_e, j_e = edges[e] + if i_e == n: + outflow += x[k, r, e] + if j_e == n: + outflow -= x[k, r, e] + model.addConstr( + outflow == demands[r][k][n], + name=f"flow_{k}_{r}_{n}" + ) + + # Constraint 2: Hard capacity sum_k x^{k,r}_{i,j} <= u_{i,j} + for r in range(num_scenarios): + for e in range(num_edges): + model.addConstr( + total_flow[r, e] <= capacities[e], + name=f"cap_{r}_{e}" + ) + + # Constraint 3: Logical linking - big-M formulation + # x_{i,j}^{k,r} = 0 if z_{i,j} = 0 + # We use a big-M equal to the maximum total supply across all scenarios + # (an upper bound on the flow through any single edge). + big_M = max( + sum(max(0.0, demands[r][k][n]) for k in range(num_commodities) for n in range(num_nodes)) + for r in range(num_scenarios) + ) + for r in range(num_scenarios): + for e in range(num_edges): + model.addConstr( + total_flow[r, e] <= big_M * z[e], + name=f"link_{r}_{e}" + ) + + # Constraint 4: Cardinality constraint + model.addConstr( + gp.quicksum(z[e] for e in range(num_edges)) <= c_0, + name="cardinality" + ) + + # Solve + model.optimize() + + # If presolve declared INF_OR_UNBD on a model that is actually feasible + # (false alarm from default dual reductions), retry with DualReductions=0. + if model.Status == GRB.INF_OR_UNBD: + model.setParam("DualReductions", 0) + model.reset() + model.optimize() + + # Extract solution + result = {} + if model.SolCount > 0: + result["objective_value"] = model.ObjVal + result["z"] = {str(e): z[e].X for e in range(num_edges)} + result["status"] = model.Status + result["mip_gap"] = model.MIPGap if hasattr(model, "MIPGap") else None + result["runtime"] = model.Runtime + else: + result["objective_value"] = None + result["status"] = model.Status + result["runtime"] = model.Runtime + result["z"] = {} + + return result + + +def main(): + parser = argparse.ArgumentParser( + description="Solve MCFND Problem (1) using Gurobi directly." + ) + parser.add_argument("--instance_path", type=str, required=True, + help="Path to the JSON instance file.") + parser.add_argument("--solution_path", type=str, required=True, + help="Path to write the solution JSON file.") + parser.add_argument("--time_limit", type=int, required=True, + help="Maximum solver runtime in seconds.") + parser.add_argument("--log_path", type=str, default=None, help="Path to log incumbent solutions") + args = parser.parse_args() + install_gurobi_logger(args.log_path) + + data = load_instance(args.instance_path) + result = build_and_solve(data, args.time_limit) + + with open(args.solution_path, "w") as f: + json.dump(result, f, indent=2) + + print(f"Solution written to 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0000000000000000000000000000000000000000..cc4a7389ad9a904dec996fdc3235c71108b9dd88 --- /dev/null +++ b/bertsimas2024/instance/tiny_instance.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1849aa0e5d7bf1273e9ac1d1340b3f84e8d7150e3b3c170e17f0a84dab485d48 +size 22776 diff --git a/bertsimas2024/instance_schema.json b/bertsimas2024/instance_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..18cd1dba08cfb927ece76324435d75a1a19d1ff1 --- /dev/null +++ b/bertsimas2024/instance_schema.json @@ -0,0 +1,16 @@ +{ + "num_nodes": " Total number of nodes in the directed network.", + "num_commodities": " Total number of distinct commodities to be shipped through the network.", + "num_scenarios": " Total number of equally likely demand scenarios.", + "num_edges": " Total number of directed edges (arcs) in the network.", + "gamma": " Regularization parameter controlling the strength of the quadratic congestion penalty on edge flows.", + "c_0": " Maximum number of edges that may be activated (constructed) across the entire network.", + "node_positions": " Two-dimensional coordinates of each node in the unit square.", + "edges": " Ordered pair of origin and destination node indices for each directed edge.", + "construction_costs": " Fixed cost incurred for activating (constructing) each edge.", + "flow_costs": " Per-unit transportation cost for shipping one unit of any commodity along each edge.", + "edge_lengths": " Euclidean distance between the endpoints of each edge.", + "capacities": " Maximum total flow of all commodities combined that each edge can carry in any scenario.", + "commodity_destinations": " Index of the single destination (sink) node for each commodity.", + "demands": " Net supply (positive) or demand (negative) at each node for each commodity in each scenario, where each commodity's values sum to zero across all nodes." +} diff --git a/bertsimas2024/mathematical_formulation.md b/bertsimas2024/mathematical_formulation.md new file mode 100644 index 0000000000000000000000000000000000000000..50446a1f8ecb830a99a07283e8137fa6230e2b71 --- /dev/null +++ b/bertsimas2024/mathematical_formulation.md @@ -0,0 +1,57 @@ +# Original Formulation: Stochastic Multi-commodity Capacitated Fixed-charge Network Design (MCFND) + +*Source: A Stochastic Benders Decomposition Scheme for Large-Scale Stochastic Network Design, Dimitris Bertsimas, Ryan Cory-Wright, Jean Pauphilet, Periklis Petridis, 2024.* + +## Sets and Indices + +- $\mathcal{N}$: set of nodes of the capacitated directed network; index $n \in \mathcal{N}$. + +- $\mathcal{E}$: set of edges (arcs) of the directed network $(\mathcal{N},\mathcal{E})$; index $(i,j) \in \mathcal{E}$. + +- $\mathcal{K}$: index set of commodities to be shipped; index $k \in \mathcal{K}$. + +- $\mathcal{R}$: set of historical demand observations (scenarios); index $r \in \mathcal{R}$. + +## Parameters + +- $\boldsymbol{A}$: flow conservation matrix (node–arc incidence matrix) of the network $(\mathcal{N},\mathcal{E})$. + +- $u_{i,j}$: capacity of arc $(i,j) \in \mathcal{E}$ (total flow of all commodities combined). + +- $d_n^{k,r}$: amount of commodity $k$ supplied (positive) or demanded (negative) at node $n$ in scenario $r$; $\boldsymbol{d}^{k,r}$ is the corresponding vector over nodes. + +- $c_{i,j}$: fixed cost of activating (constructing) edge $(i,j) \in \mathcal{E}$. + +- $f_{i,j}^{k}$: marginal transportation cost, i.e. per-unit cost of transporting commodity $k$ through edge $(i,j)$. + +- $c_0$: fixed upper limit on the number of edges that may be activated. + +- $\gamma > 0$: regularization parameter controlling the strongly quadratic penalty term in the objective. + +## Decision Variables + +- $z_{i,j} \in \{0,1\}$: binary design variable; $1$ if edge $(i,j)$ is activated, $0$ otherwise, for all $(i,j) \in \mathcal{E}$. + +- $x_{i,j}^{k,r} \ge 0$: continuous flow variable; quantity of commodity $k$ routed on edge $(i,j)$ in scenario $r$, for all $(i,j) \in \mathcal{E},\, k \in \mathcal{K},\, r \in \mathcal{R}$; $\boldsymbol{x}^{k,r}$ is the corresponding vector over edges. + +## Objective and Constraints + +The complete optimization formulation for MCFND is Problem (1), page 4: $$\begin{align} +\min \quad + & \sum_{(i,j) \in \mathcal{E}} c_{i,j}\, z_{i,j} + + \frac{1}{|\mathcal{R}|} \sum_{r \in \mathcal{R}} \sum_{(i,j) \in \mathcal{E}} + \left( + \sum_{k \in \mathcal{K}} f_{i,j}^{k}\, x_{i,j}^{k,r} + + \frac{1}{2\gamma} \Big( \sum_{k \in \mathcal{K}} x_{i,j}^{k,r} \Big)^{2} + \right) \notag \\ +\text{s.t.} \quad + & \boldsymbol{A}\,\boldsymbol{x}^{k,r} = \boldsymbol{d}^{k,r}, + && \forall k \in \mathcal{K},\, r \in \mathcal{R}, \notag \\ + & \sum_{k \in \mathcal{K}} x_{i,j}^{k,r} \le u_{i,j}, + && \forall (i,j) \in \mathcal{E},\, r \in \mathcal{R}, \notag \\ + & \boldsymbol{x}^{k,r} \ge 0,\ \ x_{i,j}^{k,r} = 0 \text{ if } z_{i,j} = 0, + && \forall (i,j) \in \mathcal{E}, \notag \\ + & \sum_{(i,j) \in \mathcal{E}} z_{i,j} \le c_0,\ \ z_{i,j} \in \{0,1\} + && \forall (i,j) \in \mathcal{E}. +\tag{1} +\end{align}$$ diff --git a/bertsimas2024/problem_description.txt b/bertsimas2024/problem_description.txt new file mode 100644 index 0000000000000000000000000000000000000000..5511e748dcbd8ffe7ae42b2e088686f78c0240eb --- /dev/null +++ b/bertsimas2024/problem_description.txt @@ -0,0 +1,9 @@ +# Problem Description + +A logistics planner manages a capacitated directed network consisting of a set of nodes and a set of directed edges (arcs) connecting pairs of nodes. Multiple distinct commodities must be shipped through this network. Demand for these commodities is uncertain and is represented by a collection of historical demand scenarios, each considered equally likely. For every commodity and every scenario, each node in the network has a known net supply or demand value: a positive value indicates that the node supplies that commodity, and a negative value indicates that the node consumes it. + +The planner must make two kinds of decisions. First, the planner selects which edges in the network to activate (construct). Second, for each demand scenario, the planner determines the quantity of each commodity to route along each activated edge. The input data includes, for every edge, a fixed construction cost incurred if that edge is activated, a per-unit transportation cost for shipping each commodity along that edge, and a capacity limiting the total flow of all commodities combined that the edge can carry. A regularization parameter gamma (strictly positive) is also given, along with an upper limit on the total number of edges that may be activated. + +Flow must be conserved at every node: for each commodity in each scenario, the net flow into and out of every node must equal the node's net supply or demand value for that commodity in that scenario. The total flow of all commodities on any edge in any scenario must not exceed that edge's capacity. No commodity may be routed along an edge that has not been activated; an edge carries zero flow of every commodity in every scenario unless it has been selected for construction. The total number of activated edges across the entire network must not exceed the given upper limit. + +The goal is to minimize total cost, which consists of three components summed together. The first component is the total construction cost, equal to the sum of fixed construction costs over all activated edges. The second component is the expected transportation cost, computed as the average across all scenarios of the sum over all edges of the per-unit transportation costs multiplied by the corresponding commodity flows. The third component is an expected quadratic congestion penalty, computed as the average across all scenarios of the sum over all edges of one over twice gamma multiplied by the square of the total commodity flow on that edge. This quadratic term penalizes heavy utilization of individual edges and acts as a soft reinforcement of the capacity limits. diff --git a/bertsimas2024/solution_logger.py b/bertsimas2024/solution_logger.py new file mode 100644 index 0000000000000000000000000000000000000000..5393fa935ce71df9da95cee04fe5199077cb8461 --- /dev/null +++ b/bertsimas2024/solution_logger.py @@ -0,0 +1,61 @@ +""" +Convergence logger for optimization algorithms. + +Records incumbent solutions with timestamps to a JSONL file. +This module is provided to LLM-generated programs — they only need to call +`log(objective_value)` whenever a better feasible solution is found. + +Usage in generated code: + from solution_logger import SolutionLogger + logger = SolutionLogger(log_path, sense="minimize") # or "maximize" + # ... inside algorithm loop: + logger.log(objective_value) +""" + +import json +import time + + +class SolutionLogger: + def __init__(self, log_path, sense="minimize"): + """ + Args: + log_path: Path to the JSONL output file. + sense: "minimize" or "maximize". + """ + self.log_path = log_path + self.sense = sense + self.start_time = time.time() + self.best_obj = None + self.min_interval = 0.1 # seconds, avoid excessive writes + + self._last_log_time = 0.0 + # Clear the file + with open(self.log_path, "w") as f: + pass + + def log(self, objective_value): + """Record a new incumbent if it improves on the best known.""" + if objective_value is None: + return + + # Check if this is an improvement + if self.best_obj is not None: + if self.sense == "minimize" and objective_value >= self.best_obj: + return + if self.sense == "maximize" and objective_value <= self.best_obj: + return + + elapsed = time.time() - self.start_time + + # Throttle writes + if self.best_obj is not None and elapsed - self._last_log_time < self.min_interval: + self.best_obj = objective_value + return + + self.best_obj = objective_value + self._last_log_time = elapsed + + with open(self.log_path, "a") as f: + f.write(json.dumps({"time": round(elapsed, 3), + "objective_value": objective_value}) + "\n") diff --git a/bertsimas2024/solution_schema.json b/bertsimas2024/solution_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..1ec8990bcb63b0fc879bdfe3f334b41266dd40f5 --- /dev/null +++ b/bertsimas2024/solution_schema.json @@ -0,0 +1,5 @@ +{ + "objective_value": " Total construction, transportation, and quadratic congestion cost represented by the complete witness.", + "model_variables": " Sparse complete design-and-flow solution. Omitted model variables are exactly zero.", + "z": " Whether each edge is activated for use in the network (1 = constructed, 0 = not constructed)." +} diff --git a/bertsimas2024/solverless_models/1849aa0e5d7bf1273e9ac1d1340b3f84e8d7150e3b3c170e17f0a84dab485d48.npz b/bertsimas2024/solverless_models/1849aa0e5d7bf1273e9ac1d1340b3f84e8d7150e3b3c170e17f0a84dab485d48.npz new file mode 100644 index 0000000000000000000000000000000000000000..8a7c4f3b00c5e6719b10964ac745e16ca02228d1 --- /dev/null +++ b/bertsimas2024/solverless_models/1849aa0e5d7bf1273e9ac1d1340b3f84e8d7150e3b3c170e17f0a84dab485d48.npz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6e72da9ab523092d5f7751d21e7f489331e27fad16bbf93238ff3c7f4f53ac28 +size 55590 diff --git a/bertsimas2024/solverless_models/21e77176aebf708762067d60bd67f53520191f641a6b8b51917bfef8186b1f14.npz b/bertsimas2024/solverless_models/21e77176aebf708762067d60bd67f53520191f641a6b8b51917bfef8186b1f14.npz new file mode 100644 index 0000000000000000000000000000000000000000..7c3707954f5261ec682c4960659ff36c15ee6468 --- /dev/null +++ b/bertsimas2024/solverless_models/21e77176aebf708762067d60bd67f53520191f641a6b8b51917bfef8186b1f14.npz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4065c28247bcc929c798c589f7f06ef2b9c75f5dc61e6a1bbf2206ee423260d9 +size 13825407 diff --git a/bertsimas2024/solverless_models/4edcb535a57aa6f6f4b2ee08df322533ece0bcf4673bd25682deb9121e703f3d.npz b/bertsimas2024/solverless_models/4edcb535a57aa6f6f4b2ee08df322533ece0bcf4673bd25682deb9121e703f3d.npz new file mode 100644 index 0000000000000000000000000000000000000000..3d5aaab1e0648a16c0be46ec3f5de9b6c5ee5091 --- /dev/null +++ b/bertsimas2024/solverless_models/4edcb535a57aa6f6f4b2ee08df322533ece0bcf4673bd25682deb9121e703f3d.npz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:288372892112a97e53322c79b24d5a7b18788438922ccee9ece585a14f3bfce4 +size 13875341 diff --git a/bertsimas2024/solverless_models/744f9702080744a97d9a26d95163e8cfdeb81d37692b8dcc2e64d3a765ae3f37.npz b/bertsimas2024/solverless_models/744f9702080744a97d9a26d95163e8cfdeb81d37692b8dcc2e64d3a765ae3f37.npz new file mode 100644 index 0000000000000000000000000000000000000000..6244103e9cb40407ab7b8bac7238dca8ef3112b0 --- /dev/null +++ b/bertsimas2024/solverless_models/744f9702080744a97d9a26d95163e8cfdeb81d37692b8dcc2e64d3a765ae3f37.npz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4ddf9aa1edc1af1f8123d6c9cb1057ab02a1c7e41fab06b56d24bba435ee61e4 +size 13936889 diff --git a/bertsimas2024/solverless_models/9f45249e9e8286dc71e7351780f00e94cfd40515b5709c8d68e3f760032047dd.npz b/bertsimas2024/solverless_models/9f45249e9e8286dc71e7351780f00e94cfd40515b5709c8d68e3f760032047dd.npz new file mode 100644 index 0000000000000000000000000000000000000000..057bbb70e319069c16e6aca31abfa7c15c6b24ad --- /dev/null +++ b/bertsimas2024/solverless_models/9f45249e9e8286dc71e7351780f00e94cfd40515b5709c8d68e3f760032047dd.npz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ce2e7fd95f4a213919d8430ba1efa5ed7a9eb45901719907ffe7b83cb1da69dc +size 13903871 diff --git a/bertsimas2024/solverless_models/a3ab080893563771dd17ea27ef7b33e658eb719572ef6e22235028abc8c58ac7.npz b/bertsimas2024/solverless_models/a3ab080893563771dd17ea27ef7b33e658eb719572ef6e22235028abc8c58ac7.npz new file mode 100644 index 0000000000000000000000000000000000000000..5fb74c007a653e220d4b7e87f18e194d23e91102 --- /dev/null +++ b/bertsimas2024/solverless_models/a3ab080893563771dd17ea27ef7b33e658eb719572ef6e22235028abc8c58ac7.npz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:236e63a34ff365cbd03b38ac9b26feee9fc55bd557ea771bc48f5b6cc0234eb8 +size 2681334 diff --git a/bertsimas2024/solverless_witness.py b/bertsimas2024/solverless_witness.py new file mode 100644 index 0000000000000000000000000000000000000000..6e8d1c0a2a83a5aa314a7405d6b50f6d5dacaf8c --- /dev/null +++ b/bertsimas2024/solverless_witness.py @@ -0,0 +1,155 @@ +#!/usr/bin/env python3 +"""Solver-free validation of complete sparse optimization witnesses.""" + +from __future__ import annotations + +import hashlib +from pathlib import Path +from typing import Any + +import numpy as np + +WITNESS_FIELD = "model_variables" + + +def _digest(path: str | Path) -> str: + value = hashlib.sha256() + with open(path, "rb") as handle: + for block in iter(lambda: handle.read(1024 * 1024), b""): + value.update(block) + return value.hexdigest() + + +def _strings(values: np.ndarray) -> list[str]: + return [str(value) for value in values.tolist()] + + +def validate_complete_witness( + instance_path: str | Path, + solution: dict[str, Any], + case_directory: str | Path, + *, + absolute_tolerance: float = 1e-5, + relative_tolerance: float = 1e-7, +) -> tuple[float | None, list[str], dict[str, float]]: + """Return recomputed objective, validation errors, and diagnostics.""" + errors: list[str] = [] + diagnostics: dict[str, float] = {} + witness = solution.get(WITNESS_FIELD) + if not isinstance(witness, dict): + return None, [f"{WITNESS_FIELD} must be an object"], diagnostics + + digest = _digest(instance_path) + snapshot = Path(case_directory) / "solverless_models" / f"{digest}.npz" + if not snapshot.is_file(): + return None, [f"no trusted solverless model for instance sha256={digest}"], diagnostics + + with np.load(snapshot, allow_pickle=False) as model: + if str(model["instance_sha256"].item()) != digest: + return None, ["trusted model instance digest mismatch"], diagnostics + names = _strings(model["var_names"]) + name_to_index = {name: index for index, name in enumerate(names)} + values = np.zeros(len(names), dtype=np.float64) + unknown: list[str] = [] + for name, raw in witness.items(): + index = name_to_index.get(str(name)) + if index is None: + if len(unknown) < 5: + unknown.append(str(name)) + continue + if isinstance(raw, bool) or not isinstance(raw, (int, float)): + errors.append(f"model variable {name!r} must be a finite number") + continue + value = float(raw) + if not np.isfinite(value): + errors.append(f"model variable {name!r} must be finite") + continue + values[index] = value + if unknown: + errors.append("unknown model variable(s): " + ", ".join(unknown)) + + lower = model["lower_bounds"] + upper = model["upper_bounds"] + scale = np.maximum(1.0, np.maximum(np.abs(lower), np.abs(upper))) + scale[~np.isfinite(scale)] = 1.0 + bound_tol = absolute_tolerance + relative_tolerance * scale + low_bad = np.flatnonzero(values < lower - bound_tol) + high_bad = np.flatnonzero(values > upper + bound_tol) + if low_bad.size: + diagnostics["max_lower_bound_violation"] = float(np.max(lower[low_bad] - values[low_bad])) + for index in low_bad[:5]: + errors.append(f"lower bound violated: {names[index]}={values[index]:.12g} < {lower[index]:.12g}") + if high_bad.size: + diagnostics["max_upper_bound_violation"] = float(np.max(values[high_bad] - upper[high_bad])) + for index in high_bad[:5]: + errors.append(f"upper bound violated: {names[index]}={values[index]:.12g} > {upper[index]:.12g}") + + integer_mask = model["integer_mask"].astype(bool) + integer_indices = np.flatnonzero(integer_mask) + fractionality = np.abs(values[integer_mask] - np.rint(values[integer_mask])) + bad_local = np.flatnonzero(fractionality > absolute_tolerance) + if bad_local.size: + diagnostics["max_integrality_violation"] = float(np.max(fractionality[bad_local])) + for local_index in bad_local[:5]: + index = integer_indices[local_index] + errors.append(f"integrality violated: {names[index]}={values[index]:.12g}") + + row_count = int(model["constraint_count"].item()) + rows = model["row_indices"] + columns = model["column_indices"] + coefficients = model["coefficients"] + activity = np.bincount(rows, weights=coefficients * values[columns], minlength=row_count) + absolute_activity = np.bincount(rows, weights=np.abs(coefficients * values[columns]), minlength=row_count) + rhs = model["rhs"] + senses = model["senses"] + row_tol = absolute_tolerance + relative_tolerance * np.maximum.reduce( + [np.ones(row_count), np.abs(rhs), absolute_activity] + ) + violation = np.zeros(row_count, dtype=np.float64) + violation[senses == -1] = np.maximum(0.0, activity[senses == -1] - rhs[senses == -1]) + violation[senses == 0] = np.abs(activity[senses == 0] - rhs[senses == 0]) + violation[senses == 1] = np.maximum(0.0, rhs[senses == 1] - activity[senses == 1]) + bad_rows = np.flatnonzero(violation > row_tol) + if bad_rows.size: + diagnostics["max_constraint_violation"] = float(np.max(violation[bad_rows])) + constraint_names = _strings(model["constraint_names"]) + sense_text = {-1: "<=", 0: "==", 1: ">="} + for row in bad_rows[:10]: + errors.append( + f"constraint violated: {constraint_names[row]}: {activity[row]:.12g} " + f"{sense_text[int(senses[row])]} {rhs[row]:.12g} (excess {violation[row]:.6g})" + ) + + objective = float(model["objective_constant"].item()) + objective += float(np.dot(model["linear_objective"], values)) + q_coefficients = model["quadratic_coefficients"] + if q_coefficients.size: + objective += float(np.sum( + q_coefficients * values[model["quadratic_rows"]] * values[model["quadratic_columns"]] + )) + diagnostics["recomputed_objective"] = objective + return objective, errors, diagnostics + + +def require_linked_variables( + solution: dict[str, Any], expected: dict[str, float], *, tolerance: float = 1e-6 +) -> list[str]: + """Ensure public fields and the complete vector describe one plan.""" + witness = solution.get(WITNESS_FIELD) + if not isinstance(witness, dict): + return [f"{WITNESS_FIELD} must be an object"] + errors: list[str] = [] + for name, expected_value in expected.items(): + raw = witness.get(name, 0.0) + try: + actual = float(raw) + except (TypeError, ValueError): + errors.append(f"linked model variable {name!r} is not numeric") + continue + if not np.isfinite(actual) or abs(actual - float(expected_value)) > tolerance: + errors.append( + f"public decision and model witness disagree for {name}: {expected_value} versus {actual}" + ) + if len(errors) >= 10: + break + return errors diff --git a/bettinelli2017/feasibility_check.py b/bettinelli2017/feasibility_check.py new file mode 100644 index 0000000000000000000000000000000000000000..c49f65352cdf86730e3c2809e7737c5d5efbee78 --- /dev/null +++ b/bettinelli2017/feasibility_check.py @@ -0,0 +1,405 @@ +#!/usr/bin/env python3 +""" +Feasibility checker for the Knapsack Problem with Conflict Graph (KPCG). +Based on Bettinelli, Cacchiani, and Malaguti (2017). + +Constraints checked (original edge-based formulation, eqs. 1b-1d): + 1: (1b) Capacity: sum w_i x_i <= c + 2: (1c) Edge conflict: x_i + x_j <= 1 for (i,j) in E + 3: (1d) Binary: x_i in {0,1} + 4: (1a) Objective consistency: reported objective_value must equal + sum_{i in selected} p_i (Tier C anti-exploit check). +""" + +import argparse +import json +from collections import defaultdict + +# FRONTIEROR_RELEASE_CONTRACT_V2 +_FRONTIEROR_REQUIRED_SOLUTION_FIELDS = ('objective_value', 'selected_items') +_FRONTIEROR_NONEMPTY_SOLUTION_FIELDS = () +_FRONTIEROR_DERIVED_FIELD_RULES = () +_FRONTIEROR_CONDITIONAL_FIELD_RULES = () + + +def _frontieror_validate_solution_contract(solution): + """Return a structured rejection when the public solution contract is absent.""" + violations = [] + if not isinstance(solution, dict): + violations.append("solution must be a JSON object") + else: + missing = [ + field + for field in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS + if field not in solution or solution[field] is None + ] + if missing: + violations.append( + "missing required solution field(s): " + ", ".join(missing) + ) + + for discriminator, expected_value, fields in _FRONTIEROR_CONDITIONAL_FIELD_RULES: + if solution.get(discriminator) != expected_value: + continue + conditional_missing = [ + field + for field in fields + if field not in solution or solution[field] is None + ] + if conditional_missing: + violations.append( + "missing conditionally required solution field(s): " + + ", ".join(conditional_missing) + ) + + empty = [ + field + for field in _FRONTIEROR_NONEMPTY_SOLUTION_FIELDS + if field in solution and solution[field] in (None, [], {}) + ] + if empty: + violations.append( + "empty required decision witness field(s): " + ", ".join(empty) + ) + + for target, operation, source, source_key in _FRONTIEROR_DERIVED_FIELD_RULES: + try: + if operation == "assignment_key_set": + reported_set = set(solution[target]) + expected_set = {entry[source_key] for entry in solution[source]} + if reported_set != expected_set: + violations.append( + f"{target}={sorted(reported_set)} does not match the " + f"decision-derived value {sorted(expected_set)}" + ) + continue + + reported = float(solution[target]) + if operation == "length": + expected = float(len(solution[source])) + elif operation == "mapping_value": + expected = float(solution[source][source_key]) + else: + raise ValueError("unknown derived-field operation") + except (KeyError, TypeError, ValueError, OverflowError): + violations.append( + f"cannot derive {target} from decision witness field {source}" + ) + continue + if ( + reported != reported + or expected != expected + or reported in (float("inf"), float("-inf")) + or expected in (float("inf"), float("-inf")) + or abs(reported - expected) > 1e-6 + ): + violations.append( + f"{target}={reported} does not match the decision-derived " + f"value {expected}" + ) + + if "objective_value" in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS: + objective = solution.get("objective_value") + if ( + isinstance(objective, bool) + or not isinstance(objective, (int, float)) + or objective != objective + or objective in (float("inf"), float("-inf")) + ): + violations.append("objective_value must be a finite JSON number") + + if not violations: + return None + return { + "feasible": False, + "violated_constraints": ["solution_schema"], + "violations": violations, + "violation_magnitudes": [], + } + + +def _frontieror_write_unexpected_rejection(error): + """Turn malformed candidate/checker exceptions into a normal rejection.""" + import json as _frontieror_json + import sys as _frontieror_sys + + result_path = None + for index, argument in enumerate(_frontieror_sys.argv): + if argument == "--result_path" and index + 1 < len(_frontieror_sys.argv): + result_path = _frontieror_sys.argv[index + 1] + break + if argument.startswith("--result_path="): + result_path = argument.split("=", 1)[1] + break + if not result_path: + return False + try: + with open(result_path, "w") as result_handle: + _frontieror_json.dump( + { + "feasible": False, + "violated_constraints": ["malformed_solution"], + "violations": [ + "checker rejected malformed candidate: " + + type(error).__name__ + + ": " + + str(error)[:500] + ], + "violation_magnitudes": [], + }, + result_handle, + indent=2, + ) + except Exception: + return False + return True + + + +def build_adjacency(num_vertices, edges): + adj = defaultdict(set) + for i, j in edges: + adj[i].add(j) + adj[j].add(i) + return adj + + +def generate_clique_cover(num_vertices, edges, adj): + """ + Heuristically generate a clique cover as described in the paper: + iteratively pick an uncovered edge, grow a maximal clique from it. + """ + uncovered = set() + for e in edges: + uncovered.add((min(e), max(e))) + + cliques = [] + while uncovered: + # Pick an uncovered edge + i, j = next(iter(uncovered)) + clique = {i, j} + + # Try to extend: add vertices connected to all current clique members + candidates = adj[i] & adj[j] + for v in sorted(candidates): + if all(v in adj[u] for u in clique): + clique.add(v) + + # Mark edges within this clique as covered + clique_list = sorted(clique) + for a_idx in range(len(clique_list)): + for b_idx in range(a_idx + 1, len(clique_list)): + edge = (clique_list[a_idx], clique_list[b_idx]) + uncovered.discard(edge) + + cliques.append(clique_list) + + return cliques + + +def check_feasibility(instance, solution): + tol = 1e-5 + eps = 1e-5 + + n = instance["num_items"] + capacity = instance["capacity"] + weights = instance["items"]["weights"] + profits = instance["items"]["profits"] + edges = instance["conflict_graph"]["edges"] + num_vertices = instance["conflict_graph"]["num_vertices"] + + selected = solution["selected_items"] + + violated_constraints = set() + violations = [] + violation_magnitudes = [] + + # Build selection set and vector + selected_set = set(selected) + x = [1 if i in selected_set else 0 for i in range(n)] + + # --- Constraint 1 (1b): Capacity constraint: sum w_i x_i <= c --- + total_weight = sum(weights[i] for i in selected) + lhs_1 = float(total_weight) + rhs_1 = float(capacity) + violation_amount = max(0.0, lhs_1 - rhs_1) + if violation_amount > tol: + violated_constraints.add(1) + violations.append( + f"Capacity exceeded: total weight {total_weight} > capacity {capacity}" + ) + normalizer = max(abs(rhs_1), eps) + violation_magnitudes.append({ + "constraint": 1, + "lhs": lhs_1, + "rhs": rhs_1, + "raw_excess": violation_amount, + "normalizer": normalizer, + "ratio": violation_amount / normalizer, + }) + + # --- Constraint 2 (1c): Edge conflict: x_i + x_j <= 1 for (i,j) in E --- + conflict_violations = [] + for i, j in edges: + lhs_val = float(x[i] + x[j]) + rhs_val = 1.0 + va = max(0.0, lhs_val - rhs_val) + if va > tol: + conflict_violations.append((i, j, lhs_val, rhs_val, va)) + + if conflict_violations: + violated_constraints.add(2) + pairs = [(i, j) for i, j, _, _, _ in conflict_violations] + violations.append( + f"Conflict edge constraint violated for {len(conflict_violations)} " + f"edge(s): {pairs}" + ) + for i, j, lhs_val, rhs_val, va in conflict_violations: + normalizer = max(abs(rhs_val), eps) + violation_magnitudes.append({ + "constraint": 2, + "lhs": lhs_val, + "rhs": rhs_val, + "raw_excess": va, + "normalizer": normalizer, + "ratio": va / normalizer, + }) + + # --- Constraint 3 (1d): Binary: x_i in {0,1} --- + non_binary = [] + for i in selected: + if i < 0 or i >= n: + non_binary.append(i) + # Also check that selected_items values are valid indices and binary-compatible + for i in selected: + if not isinstance(i, int) and not (isinstance(i, float) and i == int(i)): + non_binary.append(i) + + if non_binary: + violated_constraints.add(3) + violations.append( + f"Non-binary or out-of-range item indices: {non_binary}" + ) + for idx in non_binary: + violation_magnitudes.append({ + "constraint": 3, + "lhs": float(idx), + "rhs": 1.0, + "raw_excess": 1.0, + "normalizer": 1.0, + "ratio": 1.0, + }) + + # Check for duplicate selections (violates binary constraint) + if len(selected) != len(selected_set): + violated_constraints.add(3) + from collections import Counter + counts = Counter(selected) + dups = {k: v for k, v in counts.items() if v > 1} + violations.append(f"Duplicate item selections (violates binary): {dups}") + for item, count in dups.items(): + # x_i effectively > 1 + lhs_val = float(count) + rhs_val = 1.0 + va = lhs_val - rhs_val + normalizer = max(abs(rhs_val), eps) + violation_magnitudes.append({ + "constraint": 3, + "lhs": lhs_val, + "rhs": rhs_val, + "raw_excess": va, + "normalizer": normalizer, + "ratio": va / normalizer, + }) + + # --- Constraint 4 (1a): Objective consistency (Tier C anti-exploit) --- + # Profits are positive integers and obj = sum_{i in selected} p_i is a + # full recompute (every variable that determines the obj is in the + # solution). Reject when reported objective_value disagrees. + reported_obj = solution.get("objective_value") + if reported_obj is not None: + try: + reported = float(reported_obj) + except (TypeError, ValueError): + reported = None + if reported is not None: + # Only sum profits of valid in-range indices; out-of-range items + # are already caught by constraint 3 and may IndexError here. + true_obj = float(sum( + profits[i] for i in selected + if isinstance(i, int) and 0 <= i < n + )) + abs_diff = abs(reported - true_obj) + # profits are integers, so an integer mismatch (>=1) must fire; + # keep a 0.1% relative floor for safety on large objs. + obj_tol = max(0.5, 1e-3 * abs(true_obj)) + if abs_diff > obj_tol: + violated_constraints.add(4) + violations.append( + f"Objective consistency violated: reported objective_value=" + f"{reported} differs from recomputed sum_{{i in selected}} p_i=" + f"{true_obj} (|diff|={abs_diff:.6g}, tol={obj_tol:.6g})" + ) + normalizer = max(abs(true_obj), eps) + violation_magnitudes.append({ + "constraint": 4, + "lhs": reported, + "rhs": true_obj, + "raw_excess": abs_diff, + "normalizer": normalizer, + "ratio": abs_diff / normalizer, + }) + + feasible = len(violated_constraints) == 0 + return { + "feasible": feasible, + "violated_constraints": sorted(violated_constraints), + "violations": violations, + "violation_magnitudes": violation_magnitudes if not feasible else [], + } + + +def main(): + parser = argparse.ArgumentParser( + description="Feasibility checker for KPCG (Bettinelli et al., 2017)" + ) + parser.add_argument("--instance_path", required=True, help="Path to instance JSON") + parser.add_argument("--solution_path", required=True, help="Path to solution JSON") + parser.add_argument("--result_path", required=True, help="Path to write result JSON") + args = parser.parse_args() + + with open(args.instance_path) as f: + instance = json.load(f) + with open(args.solution_path) as f: + solution = json.load(f) + _frontieror_contract_result = _frontieror_validate_solution_contract(solution) + if _frontieror_contract_result is not None: + with open(args.result_path, "w") as _frontieror_result_handle: + json.dump( + _frontieror_contract_result, + _frontieror_result_handle, + indent=2, + ) + return + + result = check_feasibility(instance, solution) + + with open(args.result_path, "w") as f: + json.dump(result, f, indent=2) + + if result["feasible"]: + print("FEASIBLE") + else: + print(f"INFEASIBLE - violated constraints: {result['violated_constraints']}") + for v in result["violations"]: + print(f" - {v}") + + +if __name__ == "__main__": + try: + main() + except Exception as _frontieror_unexpected_error: + if not _frontieror_write_unexpected_rejection( + _frontieror_unexpected_error + ): + raise diff --git a/bettinelli2017/gurobi_code.py b/bettinelli2017/gurobi_code.py new file mode 100644 index 0000000000000000000000000000000000000000..1ac3d6430e9ea93f6489e3987cb50119ab1d0ea1 --- /dev/null +++ b/bettinelli2017/gurobi_code.py @@ -0,0 +1,210 @@ +""" +Knapsack Problem with Conflict Graph (KPCG) — Clique-based formulation. + +Implements Model 2 from Bettinelli et al. (2017): the clique cover +formulation where conflict constraints are lifted to maximal-clique +inequalities, yielding a tighter LP relaxation than the edge formulation. + +Usage: + python gurobi_code.py --instance_path instance_1.json \ + --solution_path gurobi_solution_1.json \ + --time_limit 600 +""" + +import argparse +import json +import random +from collections import defaultdict + +import gurobipy as gp +from gurobipy import GRB +import os as _os, sys as _sys +# Walk up from this file's directory to find repo root (containing scripts/). +_repo = _os.path.dirname(_os.path.abspath(__file__)) +while _repo != _os.path.dirname(_repo) and not _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _repo = _os.path.dirname(_repo) +if _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _sys.path.insert(0, _repo) +try: + from scripts.utils.gurobi_log_helper import install_gurobi_logger +except ImportError: + def install_gurobi_logger(log_path): # no-op fallback when scripts/ unavailable + pass# ------------------------------------------------------------------ # +# Heuristic clique cover generation # +# ------------------------------------------------------------------ # + +def build_adjacency(num_vertices, edges): + """Return adjacency set dict from an edge list.""" + adj = defaultdict(set) + for u, v in edges: + adj[u].add(v) + adj[v].add(u) + return adj + + +def generate_clique_cover(num_vertices, edges, profits, weights): + """ + Heuristic clique cover that guarantees every conflict edge is + contained in at least one maximal clique. + + Algorithm (per Bettinelli et al.): + 1. Pick a random uncovered edge (i, j). + 2. Seed a clique C = {i, j}. + 3. Consider every other vertex l in non-increasing p_l / w_l + order; add l to C if l is adjacent to every member of C. + 4. Mark all edges inside C as covered. + 5. Repeat until every edge is covered. + """ + adj = build_adjacency(num_vertices, edges) + + # Efficiency ratios for tie-breaking / greedy extension order + ratio = [] + for i in range(num_vertices): + w = weights[i] if weights[i] > 0 else 1e-9 + ratio.append(profits[i] / w) + + # Vertices sorted by non-increasing efficiency (precomputed once) + sorted_vertices = sorted(range(num_vertices), key=lambda v: -ratio[v]) + + uncovered = set() + for u, v in edges: + uncovered.add((min(u, v), max(u, v))) + + cliques = [] + edge_list = list(uncovered) + + while uncovered: + # Pick a random uncovered edge + # (filter edge_list lazily) + while edge_list: + idx = random.randrange(len(edge_list)) + e = edge_list[idx] + if e in uncovered: + break + # Swap-remove + edge_list[idx] = edge_list[-1] + edge_list.pop() + else: + break # no uncovered edges left + + i, j = e + clique = {i, j} + + # Extend greedily + for l in sorted_vertices: + if l in clique: + continue + if all(l in adj[m] for m in clique): + clique.add(l) + + # Mark all intra-clique edges as covered + members = sorted(clique) + for a_idx in range(len(members)): + for b_idx in range(a_idx + 1, len(members)): + a, b = members[a_idx], members[b_idx] + key = (min(a, b), max(a, b)) + uncovered.discard(key) + + cliques.append(members) + + return cliques + + +# ------------------------------------------------------------------ # +# Gurobi model # +# ------------------------------------------------------------------ # + +def solve_kpcg(instance_path, solution_path, time_limit): + # Load instance + with open(instance_path, "r") as f: + data = json.load(f) + + num_items = data["num_items"] + capacity = data["capacity"] + profits = data["items"]["profits"] + weights = data["items"]["weights"] + cg = data["conflict_graph"] + num_vertices = cg["num_vertices"] + edges = [tuple(e) for e in cg["edges"]] + + # Generate clique cover + cliques = generate_clique_cover(num_vertices, edges, profits, weights) + + # Build model + model = gp.Model("KPCG_clique") + model.setParam("Threads", 1) + model.Params.TimeLimit = time_limit + + # Decision variables + x = model.addVars(num_items, vtype=GRB.BINARY, name="x") + + # Objective + model.setObjective( + gp.quicksum(profits[i] * x[i] for i in range(num_items)), + GRB.MAXIMIZE, + ) + + # Capacity constraint + model.addConstr( + gp.quicksum(weights[i] * x[i] for i in range(num_items)) <= capacity, + name="capacity", + ) + + # Clique constraints + for k, clique in enumerate(cliques): + model.addConstr( + gp.quicksum(x[i] for i in clique) <= 1, + name=f"clique_{k}", + ) + + model.optimize() + + # Extract solution + objective_value = None + selected_items = [] + + if model.SolCount > 0: + objective_value = model.ObjVal + selected_items = [i for i in range(num_items) if x[i].X > 0.5] + + result = { + "objective_value": objective_value, + "selected_items": selected_items, + } + + with open(solution_path, "w") as f: + json.dump(result, f, indent=2) + + print(f"Objective value : {objective_value}") + print(f"Selected items : {selected_items}") + print(f"Solution written to {solution_path}") + + +# ------------------------------------------------------------------ # +# CLI # +# ------------------------------------------------------------------ # + +def main(): + parser = argparse.ArgumentParser( + description="Solve KPCG with clique-based Gurobi formulation." + ) + parser.add_argument( + "--instance_path", type=str, required=True, + help="Path to the instance JSON file.", + ) + parser.add_argument( + "--solution_path", type=str, default="gurobi_solution_1.json", + help="Path for the output solution JSON file.", + ) + parser.add_argument( + "--time_limit", type=int, required=True, + help="Solver time limit in seconds.", + ) + parser.add_argument("--log_path", type=str, default=None, help="Path to log incumbent solutions") + args = parser.parse_args() + install_gurobi_logger(args.log_path) + solve_kpcg(args.instance_path, args.solution_path, args.time_limit) + + +if __name__ == "__main__": + main() diff --git a/bettinelli2017/gurobi_feasi_result/large_feasi_result_1.json b/bettinelli2017/gurobi_feasi_result/large_feasi_result_1.json new file mode 100644 index 0000000000000000000000000000000000000000..e305b82c3d6732211d5033415df1a105476e9cf1 --- /dev/null +++ b/bettinelli2017/gurobi_feasi_result/large_feasi_result_1.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3269392edcbb7720882e77caadb88b4a9cf2fda04cf482cebd23baace4a531fd +size 102 diff --git a/bettinelli2017/gurobi_feasi_result/large_feasi_result_2.json b/bettinelli2017/gurobi_feasi_result/large_feasi_result_2.json new file mode 100644 index 0000000000000000000000000000000000000000..e305b82c3d6732211d5033415df1a105476e9cf1 --- /dev/null +++ b/bettinelli2017/gurobi_feasi_result/large_feasi_result_2.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3269392edcbb7720882e77caadb88b4a9cf2fda04cf482cebd23baace4a531fd +size 102 diff --git 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sha256:b8c3058542fab249cf370594be88a08a871058a961b3b18bc15430c9e61b209a +size 22569 diff --git a/bettinelli2017/instance_schema.json b/bettinelli2017/instance_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..f080fad2e742b4225877401345b90ac0b0d8bc2d --- /dev/null +++ b/bettinelli2017/instance_schema.json @@ -0,0 +1,13 @@ +{ + "num_items": " Number of items available for selection into the knapsack.", + "capacity": " Maximum total weight the knapsack can hold.", + "items": { + "profits": " Profit earned by placing each item in the knapsack.", + "weights": " Weight consumed in the knapsack by each item." + }, + "conflict_graph": { + "num_vertices": " Number of vertices in the conflict graph, equal to the number of items.", + "num_edges": " Number of edges in the conflict graph.", + "edges": " Pairs of item indices that conflict and cannot both be placed in the knapsack." + } +} diff --git a/bettinelli2017/mathematical_formulation.md b/bettinelli2017/mathematical_formulation.md new file mode 100644 index 0000000000000000000000000000000000000000..c9aefcba84a4299bc3dd93842347ce331aa637c3 --- /dev/null +++ b/bettinelli2017/mathematical_formulation.md @@ -0,0 +1,43 @@ +# Original Formulation: Knapsack Problem with Conflict Graph (KPCG) + +*Source: A Branch-and-Bound Algorithm for the Knapsack Problem with Conflict Graph, Andrea Bettinelli, Valentina Cacchiani, Enrico Malaguti, 2017 (INFORMS Journal on Computing, 29(3):457–473).* + +## Sets and Indices + +- $n$: number of items. + +- $i, j$: item indices, $i, j = 1, \ldots, n$. + +- $\mathcal{G} = (\mathcal{V}, \mathcal{E})$: undirected conflict graph; each vertex $i \in \mathcal{V}$ corresponds to one item, so $n = |\mathcal{V}|$. + +- $\mathcal{E}$: set of edges of the conflict graph; an edge $(i,j) \in \mathcal{E}$ denotes that items $i$ and $j$ cannot be packed together. + +## Parameters + +- $c$: positive integer capacity of the knapsack. + +- $p_i$: positive integer profit of item $i$,  $i = 1, \ldots, n$. + +- $w_i$: positive integer weight of item $i$,  $i = 1, \ldots, n$. + +*Assumptions (without loss of generality):* $\sum_{i=1,\ldots,n} w_i > c$,  $w_i \le c$ for all $i = 1, \ldots, n$, and items are sorted by nonincreasing profit-over-weight ratio $\frac{p_1}{w_1} \ge \frac{p_2}{w_2} \ge \cdots \ge \frac{p_n}{w_n}$. + +## Decision Variables + +- $x_i \in \{0,1\}$,  $i = 1, \ldots, n$:  $x_i = 1$ if item $i$ is selected (packed into the knapsack), $0$ otherwise. + +## Objective + +$$\begin{align} +\text{Maximize} \quad & \sum_{i=1,\ldots,n} p_i x_i \tag{1a} +\end{align}$$ + +## Constraints + +$$\begin{align} +\text{s.t.} \quad & \sum_{i=1,\ldots,n} w_i x_i \le c, \tag{1b} \\ +& x_i + x_j \le 1 && (i,j) \in \mathcal{E}, \tag{1c} \\ +& x_i \in \{0,1\} && i = 1, \ldots, n. \tag{1d} +\end{align}$$ + +The objective (1a) maximizes the sum of the profits of the selected items. Constraint (1b) requires not exceeding the capacity of the knapsack. Constraints (1c) impose choosing at most one item for each conflicting pair (edge) of the conflict graph. Constraints (1d) require the variables to be binary. diff --git a/bettinelli2017/problem_description.txt b/bettinelli2017/problem_description.txt new file mode 100644 index 0000000000000000000000000000000000000000..aa6bf594cc87f432bd0d26006c55a943e27b8349 --- /dev/null +++ b/bettinelli2017/problem_description.txt @@ -0,0 +1,5 @@ +# Problem Description + +A collector wishes to fill a single knapsack with a subset of available items so as to maximize the total profit of the selected items. There are n items, each characterized by a positive integer profit and a positive integer weight, and the knapsack has a positive integer capacity. The total weight of all items exceeds the knapsack capacity, but every individual item's weight is at most the capacity, so each item could fit on its own. In addition to the knapsack, an undirected conflict graph is provided: the graph has one vertex per item, and each edge connects a pair of items that are incompatible with one another, meaning they cannot both be placed in the knapsack together. The input therefore specifies the number of items, the profit and weight of each item, the integer knapsack capacity, and the full list of edges in the conflict graph (given as pairs of item indices). + +The task is to choose which items to place in the knapsack. The chosen set of items must satisfy two requirements simultaneously: the sum of the weights of the chosen items must not exceed the knapsack capacity, and for every pair of items connected by an edge in the conflict graph, at most one of the two may be chosen. The goal is to find a selection of items satisfying both requirements whose total profit is as large as possible. diff --git a/bettinelli2017/solution_logger.py b/bettinelli2017/solution_logger.py new file mode 100644 index 0000000000000000000000000000000000000000..5393fa935ce71df9da95cee04fe5199077cb8461 --- /dev/null +++ b/bettinelli2017/solution_logger.py @@ -0,0 +1,61 @@ +""" +Convergence logger for optimization algorithms. + +Records incumbent solutions with timestamps to a JSONL file. +This module is provided to LLM-generated programs — they only need to call +`log(objective_value)` whenever a better feasible solution is found. + +Usage in generated code: + from solution_logger import SolutionLogger + logger = SolutionLogger(log_path, sense="minimize") # or "maximize" + # ... inside algorithm loop: + logger.log(objective_value) +""" + +import json +import time + + +class SolutionLogger: + def __init__(self, log_path, sense="minimize"): + """ + Args: + log_path: Path to the JSONL output file. + sense: "minimize" or "maximize". + """ + self.log_path = log_path + self.sense = sense + self.start_time = time.time() + self.best_obj = None + self.min_interval = 0.1 # seconds, avoid excessive writes + + self._last_log_time = 0.0 + # Clear the file + with open(self.log_path, "w") as f: + pass + + def log(self, objective_value): + """Record a new incumbent if it improves on the best known.""" + if objective_value is None: + return + + # Check if this is an improvement + if self.best_obj is not None: + if self.sense == "minimize" and objective_value >= self.best_obj: + return + if self.sense == "maximize" and objective_value <= self.best_obj: + return + + elapsed = time.time() - self.start_time + + # Throttle writes + if self.best_obj is not None and elapsed - self._last_log_time < self.min_interval: + self.best_obj = objective_value + return + + self.best_obj = objective_value + self._last_log_time = elapsed + + with open(self.log_path, "a") as f: + f.write(json.dumps({"time": round(elapsed, 3), + "objective_value": objective_value}) + "\n") diff --git a/bettinelli2017/solution_schema.json b/bettinelli2017/solution_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..5363d29e8c0b62d55ff5de85625e2c8d86e72bce --- /dev/null +++ b/bettinelli2017/solution_schema.json @@ -0,0 +1,4 @@ +{ + "objective_value": " Total profit of the items placed in the knapsack.", + "selected_items": " Indices of items chosen to be placed in the knapsack." +} diff --git a/bierwirth2017/feasibility_check.py b/bierwirth2017/feasibility_check.py new file mode 100644 index 0000000000000000000000000000000000000000..c6c1ab5f3c8b2fa02ced7758f8accc067e597e5c --- /dev/null +++ b/bierwirth2017/feasibility_check.py @@ -0,0 +1,499 @@ +#!/usr/bin/env python3 +""" +Feasibility checker for JSPTWT (Job Shop Scheduling Problem with Total Weighted Tardiness). +Checks constraints C1-C6 from the mathematical formulation in math_model.txt, plus: + +Constraint 7: Objective consistency (Tier C anti-exploit defense). The reported +objective_value must agree (within tolerance) with the recomputed total weighted +tardiness: sum_j w_j * max(0, c_j - d_j), where c_j = s_last + p_last is computed +from the schedule's actual start times and the instance's processing times. This +catches LLM exploits that fabricate objective_value while the schedule itself is +constraint-feasible. +""" + +import argparse +import json +import sys + +# FRONTIEROR_RELEASE_CONTRACT_V2 +_FRONTIEROR_REQUIRED_SOLUTION_FIELDS = ('objective_value', 'schedule') +_FRONTIEROR_NONEMPTY_SOLUTION_FIELDS = () +_FRONTIEROR_DERIVED_FIELD_RULES = () +_FRONTIEROR_CONDITIONAL_FIELD_RULES = () + + +def _frontieror_validate_solution_contract(solution): + """Return a structured rejection when the public solution contract is absent.""" + violations = [] + if not isinstance(solution, dict): + violations.append("solution must be a JSON object") + else: + missing = [ + field + for field in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS + if field not in solution or solution[field] is None + ] + if missing: + violations.append( + "missing required solution field(s): " + ", ".join(missing) + ) + + for discriminator, expected_value, fields in _FRONTIEROR_CONDITIONAL_FIELD_RULES: + if solution.get(discriminator) != expected_value: + continue + conditional_missing = [ + field + for field in fields + if field not in solution or solution[field] is None + ] + if conditional_missing: + violations.append( + "missing conditionally required solution field(s): " + + ", ".join(conditional_missing) + ) + + empty = [ + field + for field in _FRONTIEROR_NONEMPTY_SOLUTION_FIELDS + if field in solution and solution[field] in (None, [], {}) + ] + if empty: + violations.append( + "empty required decision witness field(s): " + ", ".join(empty) + ) + + for target, operation, source, source_key in _FRONTIEROR_DERIVED_FIELD_RULES: + try: + if operation == "assignment_key_set": + reported_set = set(solution[target]) + expected_set = {entry[source_key] for entry in solution[source]} + if reported_set != expected_set: + violations.append( + f"{target}={sorted(reported_set)} does not match the " + f"decision-derived value {sorted(expected_set)}" + ) + continue + + reported = float(solution[target]) + if operation == "length": + expected = float(len(solution[source])) + elif operation == "mapping_value": + expected = float(solution[source][source_key]) + else: + raise ValueError("unknown derived-field operation") + except (KeyError, TypeError, ValueError, OverflowError): + violations.append( + f"cannot derive {target} from decision witness field {source}" + ) + continue + if ( + reported != reported + or expected != expected + or reported in (float("inf"), float("-inf")) + or expected in (float("inf"), float("-inf")) + or abs(reported - expected) > 1e-6 + ): + violations.append( + f"{target}={reported} does not match the decision-derived " + f"value {expected}" + ) + + if "objective_value" in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS: + objective = solution.get("objective_value") + if ( + isinstance(objective, bool) + or not isinstance(objective, (int, float)) + or objective != objective + or objective in (float("inf"), float("-inf")) + ): + violations.append("objective_value must be a finite JSON number") + + if not violations: + return None + return { + "feasible": False, + "violated_constraints": ["solution_schema"], + "violations": violations, + "violation_magnitudes": [], + } + + +def _frontieror_write_unexpected_rejection(error): + """Turn malformed candidate/checker exceptions into a normal rejection.""" + import json as _frontieror_json + import sys as _frontieror_sys + + result_path = None + for index, argument in enumerate(_frontieror_sys.argv): + if argument == "--result_path" and index + 1 < len(_frontieror_sys.argv): + result_path = _frontieror_sys.argv[index + 1] + break + if argument.startswith("--result_path="): + result_path = argument.split("=", 1)[1] + break + if not result_path: + return False + try: + with open(result_path, "w") as result_handle: + _frontieror_json.dump( + { + "feasible": False, + "violated_constraints": ["malformed_solution"], + "violations": [ + "checker rejected malformed candidate: " + + type(error).__name__ + + ": " + + str(error)[:500] + ], + "violation_magnitudes": [], + }, + result_handle, + indent=2, + ) + except Exception: + return False + return True + + + +def load_instance(path): + with open(path, "r") as f: + return json.load(f) + + +def load_solution(path): + with open(path, "r") as f: + return json.load(f) + + +def extract_schedule(instance, solution): + """ + Extract a unified schedule representation from either efficient or gurobi solution format. + Returns: + start_times: dict (job, op_index) -> start_time + completion_times_op: dict (job, op_index) -> completion_time + job_completion: dict job -> completion_time (of last op) + job_tardiness: dict job -> tardiness reported in solution + """ + num_jobs = instance["num_jobs"] + num_machines = instance["num_machines"] + jobs = instance["jobs"] + + start_times = {} + completion_times_op = {} + job_completion = {} + job_tardiness = {} + + if "machine_schedules" in solution: + # Efficient algorithm format: machine_schedules + job_completions + for ms in solution["machine_schedules"]: + for op in ms["operations"]: + key = (op["job"], op["operation_index"]) + start_times[key] = float(op["start_time"]) + completion_times_op[key] = float(op["completion_time"]) + for jc in solution["job_completions"]: + job_completion[jc["job"]] = float(jc["completion_time"]) + job_tardiness[jc["job"]] = float(jc["tardiness"]) + elif "schedule" in solution: + # Gurobi format: schedule is per-job with operations in technological order + for job_sched in solution["schedule"]: + j = job_sched["job_id"] + job_completion[j] = float(job_sched["completion_time"]) + job_tardiness[j] = float(job_sched["tardiness"]) + for op_idx, op in enumerate(job_sched["operations"]): + key = (j, op_idx) + start_times[key] = float(op["start_time"]) + end = float(op.get("end_time", op["start_time"] + op["processing_time"])) + completion_times_op[key] = end + else: + raise ValueError("Unrecognized solution format") + + return start_times, completion_times_op, job_completion, job_tardiness + + +def check_feasibility(instance, solution): + tol = 1e-5 + eps = 1e-5 + + jobs = instance["jobs"] + num_jobs = instance["num_jobs"] + num_machines = instance["num_machines"] + + start_times, completion_times_op, job_completion, job_tardiness = extract_schedule( + instance, solution + ) + + violations = [] + violation_magnitudes = [] + + def record(constraint_idx, msg, lhs, rhs, op_type): + """op_type: 'ge' for >=, 'eq' for =, 'le' for <=""" + if op_type == "ge": + violation_amount = max(0.0, rhs - lhs) + elif op_type == "le": + violation_amount = max(0.0, lhs - rhs) + elif op_type == "eq": + violation_amount = abs(lhs - rhs) + else: + violation_amount = 0.0 + + if violation_amount > tol: + normalizer = max(abs(rhs), eps) + ratio = violation_amount / normalizer + violations.append((constraint_idx, msg)) + violation_magnitudes.append({ + "constraint": constraint_idx, + "lhs": lhs, + "rhs": rhs, + "raw_excess": violation_amount, + "normalizer": normalizer, + "ratio": ratio, + }) + + # Build lookup: for each job, the technological sequence of (machine, processing_time) + job_ops = {} + for job in jobs: + j = job["job_id"] + job_ops[j] = [] + for op in job["operations"]: + job_ops[j].append((op["machine"], op["processing_time"])) + + # ===================================================================== + # Constraint 1: Tardiness definition + # T_j >= c_j - d_j and T_j >= 0 + # ===================================================================== + for job in jobs: + j = job["job_id"] + d_j = float(job["due_date"]) + c_j = job_completion.get(j) + T_j = job_tardiness.get(j) + if c_j is None or T_j is None: + violations.append((1, f"Job {j}: missing completion or tardiness data")) + continue + + # T_j >= c_j - d_j + lhs = T_j + rhs = c_j - d_j + record(1, f"Job {j}: T_j ({T_j}) < c_j - d_j ({rhs})", lhs, rhs, "ge") + + # T_j >= 0 + record(1, f"Job {j}: T_j ({T_j}) < 0", T_j, 0.0, "ge") + + # ===================================================================== + # Constraint 2: Job completion time + # c_j = s_{last_op} + p_{last_op} + # ===================================================================== + for job in jobs: + j = job["job_id"] + last_op_idx = len(job_ops[j]) - 1 + last_machine, last_pt = job_ops[j][last_op_idx] + key = (j, last_op_idx) + if key not in start_times: + violations.append((2, f"Job {j}: last operation not found in solution")) + continue + s_last = start_times[key] + expected_c = s_last + last_pt + c_j = job_completion.get(j, 0.0) + record(2, f"Job {j}: c_j ({c_j}) != s_last + p_last ({expected_c})", + c_j, expected_c, "eq") + + # ===================================================================== + # Constraint 3: Technological precedence within each job + # s_{op(k+1)} >= s_{op(k)} + p_{op(k)} for k = 0..m-2 + # ===================================================================== + for job in jobs: + j = job["job_id"] + for k in range(len(job_ops[j]) - 1): + key_curr = (j, k) + key_next = (j, k + 1) + if key_curr not in start_times or key_next not in start_times: + continue + s_curr = start_times[key_curr] + p_curr = job_ops[j][k][1] + s_next = start_times[key_next] + rhs_val = s_curr + p_curr + record(3, f"Job {j}, op {k}->{k+1}: s[{k+1}] ({s_next}) < s[{k}]+p[{k}] ({rhs_val})", + s_next, rhs_val, "ge") + + # ===================================================================== + # Constraint 4: Release date + # s_{first_op} >= r_j + # ===================================================================== + for job in jobs: + j = job["job_id"] + r_j = float(job.get("release_date", 0)) + key = (j, 0) + if key not in start_times: + continue + s_first = start_times[key] + record(4, f"Job {j}: s_first ({s_first}) < release_date ({r_j})", + s_first, r_j, "ge") + + # ===================================================================== + # Constraint 5: Machine capacity (disjunctive) — no overlap on same machine + # For each pair of ops on the same machine, one must finish before the other starts. + # ===================================================================== + # Group operations by machine + machine_ops = {} # machine -> list of (job, op_index, start, end, processing_time) + for job in jobs: + j = job["job_id"] + for k, (m, pt) in enumerate(job_ops[j]): + key = (j, k) + if key in start_times: + s = start_times[key] + e = completion_times_op.get(key, s + pt) + machine_ops.setdefault(m, []).append((j, k, s, e, pt)) + + for m, ops_list in machine_ops.items(): + # Sort by start time for efficient pairwise checking + ops_list.sort(key=lambda x: x[2]) + for i in range(len(ops_list)): + for ii in range(i + 1, len(ops_list)): + j1, k1, s1, e1, p1 = ops_list[i] + j2, k2, s2, e2, p2 = ops_list[ii] + # Either j1 finishes before j2 starts, or j2 finishes before j1 starts + # Since sorted by start, check if e1 <= s2 (with tolerance) + overlap = min(e1, e2) - max(s1, s2) + if overlap > tol: + # Violation: overlap + violation_amount = overlap + # For the magnitude, treat as: LHS = overlap, RHS = 0 (should be <= 0) + normalizer = max(abs(max(p1, p2)), eps) + ratio = violation_amount / normalizer + violations.append( + (5, f"Machine {m}: ops (job {j1}, op {k1}) [{s1},{e1}] and " + f"(job {j2}, op {k2}) [{s2},{e2}] overlap by {overlap:.4f}") + ) + violation_magnitudes.append({ + "constraint": 5, + "lhs": overlap, + "rhs": 0.0, + "raw_excess": violation_amount, + "normalizer": normalizer, + "ratio": ratio, + }) + + # ===================================================================== + # Constraint 6: Non-negativity + # s_{ij} >= 0, T_j >= 0, c_j >= 0 + # ===================================================================== + for key, s_val in start_times.items(): + if s_val < -tol: + record(6, f"Op (job {key[0]}, op {key[1]}): start_time ({s_val}) < 0", + s_val, 0.0, "ge") + + for job in jobs: + j = job["job_id"] + T_j = job_tardiness.get(j, 0.0) + if T_j < -tol: + record(6, f"Job {j}: T_j ({T_j}) < 0", T_j, 0.0, "ge") + c_j = job_completion.get(j, 0.0) + if c_j < -tol: + record(6, f"Job {j}: c_j ({c_j}) < 0", c_j, 0.0, "ge") + + # ===================================================================== + # Constraint 7: Objective consistency (Tier C anti-exploit defense) + # Reported objective_value must agree with the recomputed total weighted + # tardiness from the schedule's actual start times: + # true_obj = sum_j w_j * max(0, (s_last_j + p_last_j) - d_j) + # Tolerance: max(0.5, 1e-3 * |true_obj|). Inputs (w_j, p_j, d_j) are + # integers, so an integer mismatch (>= 1) always fires. + # ===================================================================== + reported_obj = solution.get("objective_value") + if reported_obj is not None: + try: + reported = float(reported_obj) + except (TypeError, ValueError): + reported = None + if reported is not None: + true_obj = 0.0 + obj_computable = True + for job in jobs: + j = job["job_id"] + w_j = float(job["weight"]) + d_j = float(job["due_date"]) + last_idx = len(job_ops[j]) - 1 + key = (j, last_idx) + if key not in start_times: + obj_computable = False + break + s_last = start_times[key] + p_last = float(job_ops[j][last_idx][1]) + c_j = s_last + p_last + T_j = max(0.0, c_j - d_j) + true_obj += w_j * T_j + if obj_computable: + abs_diff = abs(reported - true_obj) + obj_tol = max(0.5, 1e-3 * abs(true_obj)) + if abs_diff > obj_tol: + normalizer = max(abs(true_obj), eps) + ratio = abs_diff / normalizer + violations.append(( + 7, + f"Objective consistency violated: reported objective_value=" + f"{reported} differs from recomputed sum_j w_j*max(0,c_j-d_j)=" + f"{true_obj} (|diff|={abs_diff:.6g}, tol={obj_tol:.6g})", + )) + violation_magnitudes.append({ + "constraint": 7, + "lhs": reported, + "rhs": true_obj, + "raw_excess": abs_diff, + "normalizer": normalizer, + "ratio": ratio, + }) + + # Build result + violated_indices = sorted(set(v[0] for v in violations)) + violation_messages = [v[1] for v in violations] + feasible = len(violated_indices) == 0 + + result = { + "feasible": feasible, + "violated_constraints": violated_indices, + "violations": violation_messages, + "violation_magnitudes": violation_magnitudes, + } + return result + + +def main(): + parser = argparse.ArgumentParser( + description="Feasibility checker for JSPTWT solutions" + ) + parser.add_argument("--instance_path", type=str, required=True, + help="Path to the instance JSON file") + parser.add_argument("--solution_path", type=str, required=True, + help="Path to the candidate solution JSON file") + parser.add_argument("--result_path", type=str, required=True, + help="Path to write the feasibility result JSON file") + args = parser.parse_args() + + instance = load_instance(args.instance_path) + solution = load_solution(args.solution_path) + _frontieror_contract_result = _frontieror_validate_solution_contract(solution) + if _frontieror_contract_result is not None: + with open(args.result_path, "w") as _frontieror_result_handle: + json.dump( + _frontieror_contract_result, + _frontieror_result_handle, + indent=2, + ) + return + result = check_feasibility(instance, solution) + + with open(args.result_path, "w") as f: + json.dump(result, f, indent=2) + + status = "FEASIBLE" if result["feasible"] else "INFEASIBLE" + print(f"{status}: {len(result['violated_constraints'])} constraint(s) violated") + + +if __name__ == "__main__": + try: + main() + except Exception as _frontieror_unexpected_error: + if not _frontieror_write_unexpected_rejection( + _frontieror_unexpected_error + ): + raise diff --git a/bierwirth2017/gurobi_code.py b/bierwirth2017/gurobi_code.py new file mode 100644 index 0000000000000000000000000000000000000000..02da19fa2156509fdcc64a4f4b7a0324b7a73ce2 --- /dev/null +++ b/bierwirth2017/gurobi_code.py @@ -0,0 +1,292 @@ +""" +Gurobi MIP solver for the Job Shop Scheduling Problem with Total Weighted +Tardiness (JSPTWT), following the formulation from Bierwirth & Kuhpfahl (2017). + +Mathematical model: + min sum_j w_j * T_j + + s.t. + (C1) T_j >= c_j - d_j for all j + T_j >= 0 (implicit in variable bound) + (C2) c_j = s_{last_machine_j, j} + p_{last_machine_j, j} for all j + (C3) s_{sigma_j(k+1), j} >= s_{sigma_j(k), j} + p_{sigma_j(k), j} + for all j, k=1..m-1 + (C4) s_{sigma_j(1), j} >= r_j for all j + (C5) Disjunctive (machine capacity): + s_{i,k} >= s_{i,j} + p_{i,j} - V*(1 - y_{i,j,k}) + s_{i,j} >= s_{i,k} + p_{i,k} - V*y_{i,j,k} + for all i, j= 0, y binary + + Big-M value V = sum of all processing times + max release date. + This is a safe upper bound on the makespan (and hence any start time), + since even if all operations were serialised, the total time cannot exceed + this value. The paper does not specify V; this is a standard choice. +""" + +import argparse +import json +import os +import time +from collections import defaultdict + +import gurobipy as gp +from gurobipy import GRB +import os as _os, sys as _sys +# Walk up from this file's directory to find repo root (containing scripts/). +_repo = _os.path.dirname(_os.path.abspath(__file__)) +while _repo != _os.path.dirname(_repo) and not _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _repo = _os.path.dirname(_repo) +if _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _sys.path.insert(0, _repo) +try: + from scripts.utils.gurobi_log_helper import install_gurobi_logger +except ImportError: + def install_gurobi_logger(log_path): # no-op fallback when scripts/ unavailable + pass + + +def solve_jsptwt(instance_path: str, solution_path: str, time_limit: float) -> None: + # ------------------------------------------------------------------ # + # 1. Load instance + # ------------------------------------------------------------------ # + with open(instance_path, "r") as f: + data = json.load(f) + + jobs = data["jobs"] + num_jobs = data["num_jobs"] + num_machines = data["num_machines"] + + # Build helper structures + # p[i][j] = processing time of job j on machine i + # order[j] = list of machines in technological order for job j + p = {} + order = {} + weights = {} + due_dates = {} + release_dates = {} + + for job in jobs: + j = job["job_id"] + weights[j] = job["weight"] + due_dates[j] = job["due_date"] + release_dates[j] = job["release_date"] + order[j] = [] + for op in job["operations"]: + m = op["machine"] + order[j].append(m) + p[(m, j)] = op["processing_time"] + + # Compute Big-M: sum of all processing times + max release date + total_processing = sum(p.values()) + max_release = max(release_dates.values()) if release_dates else 0 + V = total_processing + max_release + + # Group jobs by machine for disjunctive constraints + # machine_jobs[i] = list of job ids that visit machine i + machine_jobs = defaultdict(list) + for j in range(num_jobs): + for m in order[j]: + machine_jobs[m].append(j) + + # ------------------------------------------------------------------ # + # 2. Build Gurobi model + # ------------------------------------------------------------------ # + model = gp.Model("JSPTWT") + model.setParam("Threads", 1) + model.Params.TimeLimit = time_limit + # Suppress verbose output; summary will still print at end + model.Params.OutputFlag = 1 + + # -- Decision variables -- + # s[i,j]: start time of operation (i/j) + s = {} + for j in range(num_jobs): + for m in order[j]: + s[(m, j)] = model.addVar(lb=0.0, vtype=GRB.CONTINUOUS, + name=f"s_{m}_{j}") + + # c[j]: completion time of job j + c = {} + for j in range(num_jobs): + c[j] = model.addVar(lb=0.0, vtype=GRB.CONTINUOUS, name=f"c_{j}") + + # T[j]: tardiness of job j + T = {} + for j in range(num_jobs): + T[j] = model.addVar(lb=0.0, vtype=GRB.CONTINUOUS, name=f"T_{j}") + + # y[i,j,k]: binary, 1 if operation (i/j) precedes (i/k) on machine i + y = {} + for m in range(num_machines): + job_list = machine_jobs[m] + for idx_a in range(len(job_list)): + for idx_b in range(idx_a + 1, len(job_list)): + ja = job_list[idx_a] + jb = job_list[idx_b] + y[(m, ja, jb)] = model.addVar(vtype=GRB.BINARY, + name=f"y_{m}_{ja}_{jb}") + + model.update() + + # -- Objective -- + model.setObjective( + gp.quicksum(weights[j] * T[j] for j in range(num_jobs)), + GRB.MINIMIZE, + ) + + # -- Constraints -- + + # (C1) Tardiness: T_j >= c_j - d_j (T_j >= 0 by lb) + for j in range(num_jobs): + model.addConstr(T[j] >= c[j] - due_dates[j], name=f"tard_{j}") + + # (C2) Completion time: c_j = s_{last_op} + p_{last_op} + for j in range(num_jobs): + last_m = order[j][-1] + model.addConstr(c[j] == s[(last_m, j)] + p[(last_m, j)], + name=f"compl_{j}") + + # (C3) Precedence within each job's technological sequence + for j in range(num_jobs): + for k in range(len(order[j]) - 1): + m_curr = order[j][k] + m_next = order[j][k + 1] + model.addConstr( + s[(m_next, j)] >= s[(m_curr, j)] + p[(m_curr, j)], + name=f"prec_{j}_{k}", + ) + + # (C4) Release date: first operation of each job starts no earlier than r_j + for j in range(num_jobs): + first_m = order[j][0] + model.addConstr(s[(first_m, j)] >= release_dates[j], + name=f"release_{j}") + + # (C5) Disjunctive constraints for each machine + for m in range(num_machines): + job_list = machine_jobs[m] + for idx_a in range(len(job_list)): + for idx_b in range(idx_a + 1, len(job_list)): + ja = job_list[idx_a] + jb = job_list[idx_b] + yvar = y[(m, ja, jb)] + # If y=1: ja before jb => s[m,jb] >= s[m,ja] + p[m,ja] + model.addConstr( + s[(m, jb)] >= s[(m, ja)] + p[(m, ja)] - V * (1 - yvar), + name=f"disj1_{m}_{ja}_{jb}", + ) + # If y=0: jb before ja => s[m,ja] >= s[m,jb] + p[m,jb] + model.addConstr( + s[(m, ja)] >= s[(m, jb)] + p[(m, jb)] - V * yvar, + name=f"disj2_{m}_{ja}_{jb}", + ) + + # ------------------------------------------------------------------ # + # 3. Solve + # ------------------------------------------------------------------ # + wall_start = time.time() + model.optimize() + wall_elapsed = time.time() - wall_start + + # ------------------------------------------------------------------ # + # 4. Extract solution and write JSON + # ------------------------------------------------------------------ # + solution = { + "instance_path": instance_path, + "solver": "Gurobi", + "time_limit": time_limit, + "wall_time": round(wall_elapsed, 3), + } + + if model.SolCount > 0: + obj_val = model.ObjVal + solution["objective_value"] = round(obj_val, 6) + solution["best_bound"] = round(model.ObjBound, 6) + solution["mip_gap"] = round(model.MIPGap, 6) if model.MIPGap < GRB.INFINITY else None + solution["status"] = "optimal" if model.Status == GRB.OPTIMAL else "feasible" + solution["num_solutions"] = model.SolCount + + # Build schedule details + schedule = [] + for job in jobs: + j = job["job_id"] + job_schedule = { + "job_id": j, + "weight": weights[j], + "due_date": due_dates[j], + "release_date": release_dates[j], + "completion_time": round(c[j].X, 4), + "tardiness": round(T[j].X, 4), + "weighted_tardiness": round(weights[j] * T[j].X, 4), + "operations": [], + } + for m in order[j]: + start_val = round(s[(m, j)].X, 4) + job_schedule["operations"].append({ + "machine": m, + "start_time": start_val, + "processing_time": p[(m, j)], + "end_time": round(start_val + p[(m, j)], 4), + }) + schedule.append(job_schedule) + + solution["schedule"] = schedule + else: + solution["objective_value"] = None + solution["status"] = "infeasible_or_no_solution" + solution["schedule"] = [] + + # Determine output path + if solution_path is None: + # Derive from instance filename + basename = os.path.splitext(os.path.basename(instance_path))[0] + # e.g. instance_1 -> gurobi_solution_1.json + idx = basename.replace("instance_", "") + solution_path = os.path.join( + os.path.dirname(instance_path), f"gurobi_solution_{idx}.json" + ) + + with open(solution_path, "w") as f: + json.dump(solution, f, indent=2) + + print(f"Solution written to {solution_path}") + if solution["objective_value"] is not None: + print(f"Objective (TWT): {solution['objective_value']}") + print(f"Status: {solution['status']}") + else: + print("No feasible solution found.") + + +def main(): + parser = argparse.ArgumentParser( + description="Solve JSPTWT using Gurobi MIP (Bierwirth & Kuhpfahl 2017 formulation)" + ) + parser.add_argument( + "--instance_path", + type=str, + required=True, + help="Path to the instance JSON file", + ) + parser.add_argument( + "--solution_path", + type=str, + default=None, + help="Path for the output solution JSON file " + "(default: gurobi_solution_{i}.json in the same directory as the instance)", + ) + parser.add_argument( + "--time_limit", + type=float, + default=3600.0, + help="Gurobi time limit in seconds (default: 3600)", + ) + parser.add_argument("--log_path", type=str, default=None, help="Path to log incumbent solutions") + args = parser.parse_args() + install_gurobi_logger(args.log_path) + + solve_jsptwt(args.instance_path, args.solution_path, args.time_limit) + + +if __name__ == "__main__": + main() diff --git 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b/bierwirth2017/instance/tiny_instance_2.json new file mode 100644 index 0000000000000000000000000000000000000000..19b37df180311732fc6be1051a7620e57c770204 --- /dev/null +++ b/bierwirth2017/instance/tiny_instance_2.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d2b1fd98c731e200e8f1a18df856d3d5d7345a8a9b2b81c0a59ef56849267847 +size 5329 diff --git a/bierwirth2017/instance_schema.json b/bierwirth2017/instance_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..03795c4c02de1cc8de0ed227fcada6b1114a9a3e --- /dev/null +++ b/bierwirth2017/instance_schema.json @@ -0,0 +1,20 @@ +{ + "num_jobs": " Total number of jobs to be scheduled.", + "num_machines": " Total number of machines in the job shop.", + "num_operations": " Total number of operations across all jobs.", + "due_date_factor": " Multiplier applied to each job's total processing time to compute its due date.", + "jobs": [ + { + "job_id": " Unique identifier for the job, indexed from 0.", + "weight": " Priority weight of the job, used to penalize late completion.", + "release_date": " Earliest time at which the job's first operation may begin processing.", + "due_date": " Target completion time for the job.", + "operations": [ + { + "machine": " Machine on which this operation must be processed.", + "processing_time": " Time required to process this operation on its assigned machine." + } + ] + } + ] +} diff --git a/bierwirth2017/mathematical_formulation.md b/bierwirth2017/mathematical_formulation.md new file mode 100644 index 0000000000000000000000000000000000000000..f41215f5f84386e7f9b5981c2345cc5752020d0a --- /dev/null +++ b/bierwirth2017/mathematical_formulation.md @@ -0,0 +1,43 @@ +# Original Formulation: Job Shop Scheduling Problem with Total Weighted Tardiness (JSPTWT) + +*Source: “Extended GRASP for the Job Shop Scheduling Problem with Total Weighted Tardiness Objective,” Bierwirth and Kuhpfahl, European Journal of Operational Research, 2017.* + +## Sets and Parameters + +- $J = \{1,\ldots,n\}$: set of jobs; $M = \{1,\ldots,m\}$: set of machines. + +- Each job $j \in J$ has an ordered sequence of $m$ operations; operation $(i,j)$ denotes the processing of job $j$ on machine $i$. + +- $p_{ij} \ge 0$: processing time of job $j$ on machine $i$. + +- $w_j$: weight of job $j$; $d_j$: due date of job $j$; $r_j \ge 0$: release date of job $j$. + +- $\sigma_j(k)$: the machine of the $k$-th operation of job $j$ in its technological sequence. + +- $V$: a sufficiently large constant (e.g., $V = \sum_{i,j} p_{ij} + \max_j r_j$). + +## Decision Variables + +- $s_{ij} \ge 0$: start time of operation $(i,j)$. + +- $c_j \ge 0$: completion time of job $j$; $T_j \ge 0$: tardiness of job $j$. + +- $y_{(i,j),(i,k)} \in \{0,1\}$ for each pair of jobs $j \ne k$ sharing machine $i$: $1$ if $(i,j)$ precedes $(i,k)$ on machine $i$. + +## Objective + +$$\begin{equation} +\min \; TWT \;=\; \sum_{j \in J} w_j\, T_j \tag{1} +\end{equation}$$ + +## Constraints (Reconstruction – paper does not provide a MIP) + +$$\begin{align} +T_j &\ge c_j - d_j, \quad T_j \ge 0, & \forall j \in J \tag{2} \\ +c_j &= s_{\sigma_j(m),\, j} + p_{\sigma_j(m),\, j}, & \forall j \in J \tag{3} \\ +s_{\sigma_j(k+1),\, j} &\ge s_{\sigma_j(k),\, j} + p_{\sigma_j(k),\, j}, & \forall j \in J,\; k = 1,\ldots,m-1 \tag{4} \\ +s_{\sigma_j(1),\, j} &\ge r_j, & \forall j \in J \tag{5} \\ +s_{i,k} &\ge s_{i,j} + p_{i,j} - V\,(1 - y_{(i,j),(i,k)}), & \forall i \in M,\; j \ne k \text{ on } i \tag{6} \\ +s_{i,j} &\ge s_{i,k} + p_{i,k} - V\, y_{(i,j),(i,k)}, & \forall i \in M,\; j \ne k \text{ on } i \tag{7} \\ +s_{ij} &\ge 0,\; T_j \ge 0,\; c_j \ge 0, \; y_{(i,j),(i,k)} \in \{0,1\}. & \tag{8} +\end{align}$$ diff --git a/bierwirth2017/problem_description.txt b/bierwirth2017/problem_description.txt new file mode 100644 index 0000000000000000000000000000000000000000..02c0570e81367129dab091d92bf3a7748dba235b --- /dev/null +++ b/bierwirth2017/problem_description.txt @@ -0,0 +1,9 @@ +# Problem Description + +A job shop contains a set of jobs and a set of machines. Each job consists of exactly as many operations as there are machines, with each operation assigned to a distinct machine, so that every job visits every machine exactly once. The order in which a job visits the machines is prescribed and may differ from job to job; this sequence is called the job's technological sequence. Each operation has a known processing time that depends on both the job and the machine. Every job has a release date (the earliest time its first operation may begin), a due date (the target completion time), and a positive weight reflecting the job's priority. + +The input data for an instance specifies the number of jobs, the number of machines, and, for every job, its weight, release date, due date, and the ordered list of operations with their respective machines and processing times. + +The scheduler must determine, for every operation, when it starts processing. Equivalently, for every machine the scheduler must fix the order in which the jobs assigned to that machine are processed. A schedule is valid when four conditions hold simultaneously. First, the first operation of a job may not start before that job's release date. Second, the operations of each job must be processed in the prescribed technological order: the next operation of a job cannot start until the preceding operation of the same job has finished, where finishing means the start time plus the processing time. Third, each machine can process at most one operation at a time, so for every pair of operations sharing a machine one must finish entirely before the other begins. Fourth, the completion time of a job equals the start time of its last operation plus that operation's processing time, and the tardiness of a job is the amount by which its completion time exceeds its due date, or zero if the job finishes on time or early. + +The goal is to find a valid schedule that minimizes the total weighted tardiness, which is the sum over all jobs of each job's weight multiplied by its tardiness. diff --git a/bierwirth2017/solution_logger.py b/bierwirth2017/solution_logger.py new file mode 100644 index 0000000000000000000000000000000000000000..5393fa935ce71df9da95cee04fe5199077cb8461 --- /dev/null +++ b/bierwirth2017/solution_logger.py @@ -0,0 +1,61 @@ +""" +Convergence logger for optimization algorithms. + +Records incumbent solutions with timestamps to a JSONL file. +This module is provided to LLM-generated programs — they only need to call +`log(objective_value)` whenever a better feasible solution is found. + +Usage in generated code: + from solution_logger import SolutionLogger + logger = SolutionLogger(log_path, sense="minimize") # or "maximize" + # ... inside algorithm loop: + logger.log(objective_value) +""" + +import json +import time + + +class SolutionLogger: + def __init__(self, log_path, sense="minimize"): + """ + Args: + log_path: Path to the JSONL output file. + sense: "minimize" or "maximize". + """ + self.log_path = log_path + self.sense = sense + self.start_time = time.time() + self.best_obj = None + self.min_interval = 0.1 # seconds, avoid excessive writes + + self._last_log_time = 0.0 + # Clear the file + with open(self.log_path, "w") as f: + pass + + def log(self, objective_value): + """Record a new incumbent if it improves on the best known.""" + if objective_value is None: + return + + # Check if this is an improvement + if self.best_obj is not None: + if self.sense == "minimize" and objective_value >= self.best_obj: + return + if self.sense == "maximize" and objective_value <= self.best_obj: + return + + elapsed = time.time() - self.start_time + + # Throttle writes + if self.best_obj is not None and elapsed - self._last_log_time < self.min_interval: + self.best_obj = objective_value + return + + self.best_obj = objective_value + self._last_log_time = elapsed + + with open(self.log_path, "a") as f: + f.write(json.dumps({"time": round(elapsed, 3), + "objective_value": objective_value}) + "\n") diff --git a/bierwirth2017/solution_schema.json b/bierwirth2017/solution_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..ef9f5727a80363ba71f4057468af672bd5641737 --- /dev/null +++ b/bierwirth2017/solution_schema.json @@ -0,0 +1,16 @@ +{ + "objective_value": " Total weighted tardiness across all jobs in the chosen schedule.", + "schedule": [ + { + "job_id": " Identifier of the job whose schedule is being described.", + "completion_time": " Time at which the job finishes its last operation.", + "tardiness": " Amount by which the job's completion time exceeds its due date, or zero if on time.", + "operations": [ + { + "machine": " Machine on which this operation of the job is processed.", + "start_time": " Time at which this operation begins processing on its machine." + } + ] + } + ] +} diff --git a/bode2012/feasibility_check.py b/bode2012/feasibility_check.py new file mode 100644 index 0000000000000000000000000000000000000000..98a6c27c109f7815ae61e615d47ed897dbe55b3d --- /dev/null +++ b/bode2012/feasibility_check.py @@ -0,0 +1,677 @@ +""" +Feasibility checker for the Capacitated Arc-Routing Problem (CARP). +Based on the Two-Index Formulation (Equations 1-6) from: +Bode & Irnich (2012), "Cut-First Branch-and-Price-Second for the CARP", +Operations Research 60(5):1167-1182. + +Constraints checked (numbered as in the paper): + (2) Partitioning: each required edge is serviced exactly once + (3) Subtour-elimination / connectivity: each vehicle route is connected to the depot + (4) Parity: even degree at every node for each vehicle + (5) Capacity: total demand per vehicle does not exceed Q + (6) Integrality / domain: x^k_e in {0,1}, y^k_e in Z+, p^k_i in Z+ + (7) Objective consistency: reported objective_value must equal + sum_{k,e} c^serv_e * x^k_e + sum_{k,e} c_e * y^k_e + (Tier C defense against LLM exploits that report bogus objective values + like 0 or sys.float_info.max while constraints technically pass.) +""" + +import json +import argparse +import math +import heapq +from collections import defaultdict + +# FRONTIEROR_RELEASE_CONTRACT_V2 +_FRONTIEROR_REQUIRED_SOLUTION_FIELDS = ('objective_value', 'routes') +_FRONTIEROR_NONEMPTY_SOLUTION_FIELDS = () +_FRONTIEROR_DERIVED_FIELD_RULES = () +_FRONTIEROR_CONDITIONAL_FIELD_RULES = () + + +def _frontieror_validate_solution_contract(solution): + """Return a structured rejection when the public solution contract is absent.""" + violations = [] + if not isinstance(solution, dict): + violations.append("solution must be a JSON object") + else: + missing = [ + field + for field in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS + if field not in solution or solution[field] is None + ] + if missing: + violations.append( + "missing required solution field(s): " + ", ".join(missing) + ) + + for discriminator, expected_value, fields in _FRONTIEROR_CONDITIONAL_FIELD_RULES: + if solution.get(discriminator) != expected_value: + continue + conditional_missing = [ + field + for field in fields + if field not in solution or solution[field] is None + ] + if conditional_missing: + violations.append( + "missing conditionally required solution field(s): " + + ", ".join(conditional_missing) + ) + + empty = [ + field + for field in _FRONTIEROR_NONEMPTY_SOLUTION_FIELDS + if field in solution and solution[field] in (None, [], {}) + ] + if empty: + violations.append( + "empty required decision witness field(s): " + ", ".join(empty) + ) + + for target, operation, source, source_key in _FRONTIEROR_DERIVED_FIELD_RULES: + try: + reported = float(solution[target]) + if operation == "length": + expected = float(len(solution[source])) + elif operation == "mapping_value": + expected = float(solution[source][source_key]) + else: + raise ValueError("unknown derived-field operation") + except (KeyError, TypeError, ValueError, OverflowError): + violations.append( + f"cannot derive {target} from decision witness field {source}" + ) + continue + if ( + reported != reported + or expected != expected + or reported in (float("inf"), float("-inf")) + or expected in (float("inf"), float("-inf")) + or abs(reported - expected) > 1e-6 + ): + violations.append( + f"{target}={reported} does not match the decision-derived " + f"value {expected}" + ) + + if "objective_value" in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS: + objective = solution.get("objective_value") + if ( + isinstance(objective, bool) + or not isinstance(objective, (int, float)) + or objective != objective + or objective in (float("inf"), float("-inf")) + ): + violations.append("objective_value must be a finite JSON number") + + if not violations: + return None + return { + "feasible": False, + "violated_constraints": ["solution_schema"], + "violations": violations, + "violation_magnitudes": [], + } + + +def _frontieror_write_unexpected_rejection(error): + """Turn malformed candidate/checker exceptions into a normal rejection.""" + import json as _frontieror_json + import sys as _frontieror_sys + + result_path = None + for index, argument in enumerate(_frontieror_sys.argv): + if argument == "--result_path" and index + 1 < len(_frontieror_sys.argv): + result_path = _frontieror_sys.argv[index + 1] + break + if argument.startswith("--result_path="): + result_path = argument.split("=", 1)[1] + break + if not result_path: + return False + try: + with open(result_path, "w") as result_handle: + _frontieror_json.dump( + { + "feasible": False, + "violated_constraints": ["malformed_solution"], + "violations": [ + "checker rejected malformed candidate: " + + type(error).__name__ + + ": " + + str(error)[:500] + ], + "violation_magnitudes": [], + }, + result_handle, + indent=2, + ) + except Exception: + return False + return True + + + +def load_json(path): + with open(path, "r") as f: + return json.load(f) + + +def build_graph(instance): + """Build graph data structures from the instance.""" + num_nodes = instance["graph"]["num_nodes"] + depot = instance["depot"] + capacity = instance["fleet"]["vehicle_capacity"] + num_vehicles = instance["fleet"]["num_vehicles"] + + edges = {} + required_edge_ids = [] + adj = defaultdict(list) + + for e in instance["edges"]: + eid = e["edge_id"] + edges[eid] = { + "endpoints": tuple(e["endpoints"]), + "cost": e["cost"], + "is_required": e["is_required"], + "demand": e["demand"], + "service_cost": e["service_cost"], + } + u, v = e["endpoints"] + adj[u].append((v, eid)) + adj[v].append((u, eid)) + if e["is_required"]: + required_edge_ids.append(eid) + + return { + "num_nodes": num_nodes, + "depot": depot, + "capacity": capacity, + "num_vehicles": num_vehicles, + "edges": edges, + "required_edge_ids": required_edge_ids, + "adj": adj, + } + + +def check_feasibility(instance, solution): + tol = 1e-5 + eps = 1e-5 + + # Handle no-solution cases (solver timeout, infeasible, etc.) + obj = solution.get("objective_value") + status_str = solution.get("status_str", "") + if (obj is None or obj == float("inf") or + status_str in ("NO_SOLUTION_FOUND", "INFEASIBLE", "INF_OR_UNBD")): + return { + "feasible": None, + "violated_constraints": [], + "violations": [ + f"No solution to check (status={status_str!r}, objective_value={obj!r})" + ], + "violation_magnitudes": [], + } + + violations = [] + violation_magnitudes = [] + violated_constraints_set = set() + + graph = build_graph(instance) + depot = graph["depot"] + capacity = graph["capacity"] + num_vehicles = graph["num_vehicles"] + edges = graph["edges"] + required_edge_ids = graph["required_edge_ids"] + adj = graph["adj"] + num_nodes = graph["num_nodes"] + + routes = solution.get("routes", []) + + # ========================================================================= + # Constraint (2): Partitioning — each required edge serviced exactly once + # sum_{k in K} x^k_e = 1 for all e in E_R + # ========================================================================= + service_count = defaultdict(int) + for route in routes: + for eid in route.get("serviced_edges", []): + service_count[eid] += 1 + + for eid in required_edge_ids: + lhs = service_count[eid] + rhs = 1.0 + violation_amount = abs(lhs - rhs) + if violation_amount > tol: + violated_constraints_set.add(2) + u, v = edges[eid]["endpoints"] + if lhs == 0: + violations.append( + f"Constraint (2): Required edge {eid} ({u}-{v}) is not serviced by any vehicle" + ) + else: + violations.append( + f"Constraint (2): Required edge {eid} ({u}-{v}) is serviced {lhs} times instead of exactly once" + ) + normalizer = max(abs(rhs), eps) + violation_magnitudes.append({ + "constraint": 2, + "lhs": float(lhs), + "rhs": float(rhs), + "raw_excess": float(violation_amount), + "normalizer": float(normalizer), + "ratio": float(violation_amount / normalizer), + }) + + # Also check that no non-required edge is serviced + for route in routes: + for eid in route.get("serviced_edges", []): + if eid not in edges: + violated_constraints_set.add(2) + violations.append( + f"Constraint (2): Edge {eid} does not exist in the instance" + ) + violation_magnitudes.append({ + "constraint": 2, + "lhs": 1.0, + "rhs": 0.0, + "raw_excess": 1.0, + "normalizer": eps, + "ratio": 1.0 / eps, + }) + elif not edges[eid]["is_required"]: + violated_constraints_set.add(2) + u, v = edges[eid]["endpoints"] + violations.append( + f"Constraint (2): Non-required edge {eid} ({u}-{v}) is serviced" + ) + violation_magnitudes.append({ + "constraint": 2, + "lhs": 1.0, + "rhs": 0.0, + "raw_excess": 1.0, + "normalizer": eps, + "ratio": 1.0 / eps, + }) + + # ========================================================================= + # Constraint (3): Subtour-elimination / connectivity + # x^k(delta_R(S)) + y^k(delta(S)) >= 2 x^k_f + # for all S subset V\{d}, f in E_R(S), k in K + # + # Practical check: for each vehicle with serviced edges, the multigraph + # formed by its serviced + deadheaded edges must be connected AND must + # include the depot. This is equivalent to ensuring no subtour disconnected + # from the depot exists. + # ========================================================================= + for k, route in enumerate(routes): + serviced = route.get("serviced_edges", []) + if not serviced: + continue + + # Build the multigraph for this vehicle + route_adj = defaultdict(set) + # Add serviced edges + for eid in serviced: + if eid in edges: + u, v = edges[eid]["endpoints"] + route_adj[u].add(v) + route_adj[v].add(u) + # Add deadheaded edges + for dh in route.get("deadheaded_edges", []): + dh_eid = dh["edge_id"] + if dh_eid in edges and dh.get("times", 0) > 0: + u, v = edges[dh_eid]["endpoints"] + route_adj[u].add(v) + route_adj[v].add(u) + + # All nodes that appear in this vehicle's route + route_nodes = set() + for eid in serviced: + if eid in edges: + u, v = edges[eid]["endpoints"] + route_nodes.add(u) + route_nodes.add(v) + for dh in route.get("deadheaded_edges", []): + dh_eid = dh["edge_id"] + if dh_eid in edges and dh.get("times", 0) > 0: + u, v = edges[dh_eid]["endpoints"] + route_nodes.add(u) + route_nodes.add(v) + + # Check connectivity from depot + if depot not in route_nodes: + # Vehicle has serviced edges but route does not visit depot + violated_constraints_set.add(3) + violations.append( + f"Constraint (3): Vehicle {k} has serviced edges but its route does not include the depot (node {depot})" + ) + # Use a violation magnitude based on the number of serviced edges + lhs_val = 0.0 + rhs_val = 2.0 + raw = rhs_val - lhs_val + normalizer = max(abs(rhs_val), eps) + violation_magnitudes.append({ + "constraint": 3, + "lhs": lhs_val, + "rhs": rhs_val, + "raw_excess": raw, + "normalizer": normalizer, + "ratio": raw / normalizer, + }) + else: + # BFS from depot + visited = set() + queue = [depot] + visited.add(depot) + while queue: + node = queue.pop(0) + for nb in route_adj[node]: + if nb not in visited and nb in route_nodes: + visited.add(nb) + queue.append(nb) + + unreachable = route_nodes - visited + if unreachable: + # Find which serviced edges are in disconnected components + disconnected_serviced = [] + for eid in serviced: + if eid in edges: + u, v = edges[eid]["endpoints"] + if u in unreachable or v in unreachable: + disconnected_serviced.append(eid) + + if disconnected_serviced: + violated_constraints_set.add(3) + violations.append( + f"Constraint (3): Vehicle {k} has a disconnected subtour; " + f"serviced edges {disconnected_serviced} are not connected to the depot" + ) + lhs_val = 0.0 + rhs_val = 2.0 + raw = rhs_val - lhs_val + normalizer = max(abs(rhs_val), eps) + violation_magnitudes.append({ + "constraint": 3, + "lhs": lhs_val, + "rhs": rhs_val, + "raw_excess": raw, + "normalizer": normalizer, + "ratio": raw / normalizer, + }) + + # ========================================================================= + # Constraint (4): Parity — even degree at every node for each vehicle + # x^k(delta_R(i)) + y^k(delta(i)) = 2 p^k_i for all i in V, k in K + # + # This means the total edge traversals (service + deadhead) incident to + # each node must be even for each vehicle. + # ========================================================================= + for k, route in enumerate(routes): + serviced = route.get("serviced_edges", []) + deadheaded = route.get("deadheaded_edges", []) + + # Count degree at each node: serviced edges count 1 traversal each, + # deadheaded edges count 'times' traversals each + node_degree = defaultdict(int) + for eid in serviced: + if eid in edges: + u, v = edges[eid]["endpoints"] + node_degree[u] += 1 + node_degree[v] += 1 + for dh in deadheaded: + dh_eid = dh["edge_id"] + times = dh.get("times", 0) + if dh_eid in edges and times > 0: + u, v = edges[dh_eid]["endpoints"] + node_degree[u] += times + node_degree[v] += times + + for node, deg in node_degree.items(): + if deg % 2 != 0: + lhs = float(deg) + # rhs should be an even number; the nearest even number + rhs = float(2 * round(deg / 2)) + violation_amount = abs(lhs - rhs) + if violation_amount > tol: + violated_constraints_set.add(4) + violations.append( + f"Constraint (4): Vehicle {k}, node {node} has odd degree {deg} " + f"(parity violated)" + ) + normalizer = max(abs(rhs), eps) + violation_magnitudes.append({ + "constraint": 4, + "lhs": lhs, + "rhs": rhs, + "raw_excess": violation_amount, + "normalizer": normalizer, + "ratio": violation_amount / normalizer, + }) + + # ========================================================================= + # Constraint (5): Capacity — q^T x^k <= Q for all k in K + # Total demand of serviced edges per vehicle must not exceed capacity. + # ========================================================================= + for k, route in enumerate(routes): + serviced = route.get("serviced_edges", []) + total_demand = 0.0 + for eid in serviced: + if eid in edges: + total_demand += edges[eid]["demand"] + + lhs = total_demand + rhs = float(capacity) + violation_amount = max(lhs - rhs, 0.0) + if violation_amount > tol: + violated_constraints_set.add(5) + violations.append( + f"Constraint (5): Vehicle {k} demand {lhs} exceeds capacity {rhs} " + f"(excess {violation_amount})" + ) + normalizer = max(abs(rhs), eps) + violation_magnitudes.append({ + "constraint": 5, + "lhs": lhs, + "rhs": rhs, + "raw_excess": violation_amount, + "normalizer": normalizer, + "ratio": violation_amount / normalizer, + }) + + # ========================================================================= + # Constraint (6): Integrality and domain + # x^k in {0,1}^{|E_R|}, y^k in Z+^{|E|}, p^k in Z+^{|V|} + # + # Check: + # - x^k_e values are 0 or 1 (each required edge serviced at most once per vehicle) + # - y^k_e values are nonneg integers (deadhead counts) + # - p^k_i values are nonneg integers (implied by parity check above) + # ========================================================================= + for k, route in enumerate(routes): + # Check x^k: each required edge should be serviced at most once per vehicle + serviced = route.get("serviced_edges", []) + edge_counts_per_vehicle = defaultdict(int) + for eid in serviced: + edge_counts_per_vehicle[eid] += 1 + + for eid, count in edge_counts_per_vehicle.items(): + if count > 1: + lhs = float(count) + rhs = 1.0 + violation_amount = abs(lhs - rhs) + if violation_amount > tol: + violated_constraints_set.add(6) + if eid in edges: + u, v = edges[eid]["endpoints"] + violations.append( + f"Constraint (6): Vehicle {k} services edge {eid} ({u}-{v}) " + f"{count} times (x^k_e must be binary)" + ) + else: + violations.append( + f"Constraint (6): Vehicle {k} services unknown edge {eid} " + f"{count} times (x^k_e must be binary)" + ) + normalizer = max(abs(rhs), eps) + violation_magnitudes.append({ + "constraint": 6, + "lhs": lhs, + "rhs": rhs, + "raw_excess": violation_amount, + "normalizer": normalizer, + "ratio": violation_amount / normalizer, + }) + + # Check y^k: deadhead counts must be nonneg integers + for dh in route.get("deadheaded_edges", []): + times = dh.get("times", 0) + if not isinstance(times, int) or times < 0: + lhs = float(times) + rhs = 0.0 + violation_amount = max(-lhs, 0.0) if isinstance(times, (int, float)) else 1.0 + if violation_amount > tol or not isinstance(times, int): + violated_constraints_set.add(6) + violations.append( + f"Constraint (6): Vehicle {k}, deadheaded edge {dh.get('edge_id', '?')} " + f"has invalid times={times} (must be nonneg integer)" + ) + normalizer = max(abs(rhs), eps) + violation_magnitudes.append({ + "constraint": 6, + "lhs": lhs, + "rhs": rhs, + "raw_excess": max(violation_amount, 1.0), + "normalizer": normalizer, + "ratio": max(violation_amount, 1.0) / normalizer, + }) + + # ========================================================================= + # Constraint (7): Objective consistency (Tier C exploit defense) + # Recompute the objective from the solution variables and reject if it + # disagrees with the reported objective_value. The CARP objective (1) is + # obj = sum_{k,e in E_R} c^serv_e * x^k_e + sum_{k,e in E} c_e * y^k_e + # which from the solution is + # sum over routes of (sum of service_cost over serviced_edges + # + sum of cost*times over deadheaded_edges). + # ========================================================================= + try: + reported_f = float(obj) + except (TypeError, ValueError): + reported_f = None + + if reported_f is not None and math.isfinite(reported_f): + recomputed = 0.0 + for route in routes: + for eid in route.get("serviced_edges", []): + if eid in edges: + recomputed += float(edges[eid]["service_cost"]) + for dh in route.get("deadheaded_edges", []): + dh_eid = dh["edge_id"] + times = dh.get("times", 0) + if dh_eid in edges and isinstance(times, (int, float)) and times > 0: + recomputed += float(edges[dh_eid]["cost"]) * float(times) + + # 0.1% relative tolerance with 1e-3 absolute floor. CARP costs are + # typically integers, so this is comfortably tight without being flaky + # on Gurobi's small float roundoff. + obj_tol = max(1e-3, 1e-3 * abs(recomputed)) + abs_diff = abs(reported_f - recomputed) + if abs_diff > obj_tol: + violated_constraints_set.add(7) + violations.append( + f"Constraint (7): Reported objective_value={reported_f} differs from " + f"recomputed sum_k(c^serv*x^k + c*y^k)={recomputed} " + f"(|diff|={abs_diff:.6g}, tol={obj_tol:.6g})" + ) + normalizer = max(abs(recomputed), eps) + violation_magnitudes.append({ + "constraint": 7, + "lhs": float(reported_f), + "rhs": float(recomputed), + "raw_excess": float(abs_diff), + "normalizer": float(normalizer), + "ratio": float(abs_diff / normalizer), + }) + elif reported_f is None or not math.isfinite(reported_f): + # Non-numeric or non-finite reported obj is itself an objective + # consistency violation. + violated_constraints_set.add(7) + violations.append( + f"Constraint (7): Reported objective_value={obj!r} is not a finite number" + ) + violation_magnitudes.append({ + "constraint": 7, + "lhs": float("nan"), + "rhs": 0.0, + "raw_excess": 1.0, + "normalizer": eps, + "ratio": 1.0 / eps, + }) + + # ========================================================================= + # Build result + # ========================================================================= + violated_list = sorted(violated_constraints_set) + feasible = len(violated_list) == 0 + + result = { + "feasible": feasible, + "violated_constraints": violated_list, + "violations": violations, + "violation_magnitudes": violation_magnitudes, + } + return result + + +def main(): + parser = argparse.ArgumentParser( + description="Feasibility checker for CARP solutions (Bode & Irnich 2012)" + ) + parser.add_argument( + "--instance_path", type=str, required=True, + help="Path to the JSON instance file" + ) + parser.add_argument( + "--solution_path", type=str, required=True, + help="Path to the JSON solution file" + ) + parser.add_argument( + "--result_path", type=str, required=True, + help="Path to write the JSON feasibility result" + ) + args = parser.parse_args() + + instance = load_json(args.instance_path) + solution = load_json(args.solution_path) + _frontieror_contract_result = _frontieror_validate_solution_contract(solution) + if _frontieror_contract_result is not None: + with open(args.result_path, "w") as _frontieror_result_handle: + json.dump( + _frontieror_contract_result, + _frontieror_result_handle, + indent=2, + ) + return + result = check_feasibility(instance, solution) + + with open(args.result_path, "w") as f: + json.dump(result, f, indent=2) + + if result["feasible"] is None: + print(f"NO SOLUTION - {result['violations'][0] if result['violations'] else 'No solution to check'}") + elif result["feasible"]: + print(f"FEASIBLE - No constraint violations found.") + else: + print(f"INFEASIBLE - Violated constraints: {result['violated_constraints']}") + for v in result["violations"]: + print(f" {v}") + + +if __name__ == "__main__": + try: + main() + except Exception as _frontieror_unexpected_error: + if not _frontieror_write_unexpected_rejection( + _frontieror_unexpected_error + ): + raise diff --git a/bode2012/gurobi_code.py b/bode2012/gurobi_code.py new file mode 100644 index 0000000000000000000000000000000000000000..fed533597e5928b22d3b7dfaede183b2aef91a06 --- /dev/null +++ b/bode2012/gurobi_code.py @@ -0,0 +1,372 @@ +r""" +Gurobi implementation of the Two-Index CARP Formulation (Equations 1-6) +from Bode & Irnich (2012), "Cut-First Branch-and-Price-Second for the CARP", +Operations Research 60(5):1167-1182. + +The CARP is modeled as: + min sum_k c^serv' x^k + sum_k c' y^k (1) + s.t. sum_k x^k_e = 1 for all e in E_R (2) + x^k(delta_R(S)) + y^k(delta(S)) >= 2 x^k_f + for all S <= V\{d}, f in E_R(S), k in K (3) + x^k(delta_R(i)) + y^k(delta(i)) = 2 p^k_i + for all i in V, k in K (4) + q' x^k <= Q for all k in K (5) + p^k in Z_+^|V|, x^k in {0,1}^|E_R|, y^k in Z_+^|E| (6) + +Since constraint (3) has exponentially many subtour elimination constraints (SEC), +we use a callback-based lazy constraint approach: + - We solve the model without (3), then add violated SECs as lazy constraints. + +**INFERRED ASSUMPTION**: The paper's SEC (3) is separated via connected-component +analysis on the support graph. For each vehicle k, we check if the edges used by k +form a connected subgraph that includes the depot. If not, for each connected +component S not containing the depot, we add the violated SEC for all required +edges f in E_R(S). +""" + +import json +import argparse +import time +import math +from itertools import combinations + +import gurobipy as gp +from gurobipy import GRB +import os as _os, sys as _sys +# Walk up from this file's directory to find repo root (containing scripts/). +_repo = _os.path.dirname(_os.path.abspath(__file__)) +while _repo != _os.path.dirname(_repo) and not _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _repo = _os.path.dirname(_repo) +if _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _sys.path.insert(0, _repo) +try: + from scripts.utils.gurobi_log_helper import install_gurobi_logger +except ImportError: + def install_gurobi_logger(log_path): # no-op fallback when scripts/ unavailable + pass + + +def load_instance(path): + with open(path, "r") as f: + data = json.load(f) + return data + + +def build_adjacency(edges, num_nodes): + """Build adjacency list from edge list.""" + adj = {i: [] for i in range(num_nodes)} + for e in edges: + u, v = e["endpoints"] + adj[u].append((v, e["edge_id"])) + adj[v].append((u, e["edge_id"])) + return adj + + +def get_delta(node_set, edges): + """Get edges with exactly one endpoint in node_set.""" + s = set(node_set) + result = [] + for e in edges: + u, v = e["endpoints"] + if (u in s) != (v in s): + result.append(e["edge_id"]) + return result + + +def get_delta_R(node_set, edges): + """Get required edges with exactly one endpoint in node_set.""" + s = set(node_set) + result = [] + for e in edges: + if not e["is_required"]: + continue + u, v = e["endpoints"] + if (u in s) != (v in s): + result.append(e["edge_id"]) + return result + + +def get_E_R_S(node_set, edges): + """Get required edges with both endpoints in node_set.""" + s = set(node_set) + result = [] + for e in edges: + if not e["is_required"]: + continue + u, v = e["endpoints"] + if u in s and v in s: + result.append(e["edge_id"]) + return result + + +def find_connected_components(num_nodes, active_edges, edges_data): + """Find connected components given a set of active edge ids.""" + adj = {i: set() for i in range(num_nodes)} + active_nodes = set() + for eid in active_edges: + u, v = edges_data[eid]["endpoints"] + adj[u].add(v) + adj[v].add(u) + active_nodes.add(u) + active_nodes.add(v) + + visited = set() + components = [] + for start in active_nodes: + if start in visited: + continue + comp = set() + stack = [start] + while stack: + node = stack.pop() + if node in visited: + continue + visited.add(node) + comp.add(node) + for nb in adj[node]: + if nb not in visited: + stack.append(nb) + components.append(comp) + return components + + +def solve_carp(instance_path, solution_path, time_limit): + data = load_instance(instance_path) + + num_nodes = data["graph"]["num_nodes"] + num_edges = data["graph"]["num_edges"] + depot = data["depot"] + num_vehicles = data["fleet"]["num_vehicles"] + capacity = data["fleet"]["vehicle_capacity"] + edges = data["edges"] + + # Index sets + all_edge_ids = list(range(num_edges)) + required_edge_ids = [e["edge_id"] for e in edges if e["is_required"]] + K = list(range(num_vehicles)) + + # Edge properties + cost = {e["edge_id"]: e["cost"] for e in edges} + serv_cost = {e["edge_id"]: e["service_cost"] for e in edges} + demand = {e["edge_id"]: e["demand"] for e in edges} + is_required = {e["edge_id"]: e["is_required"] for e in edges} + endpoints = {e["edge_id"]: tuple(e["endpoints"]) for e in edges} + + # delta(i): edges incident to node i + delta = {i: [] for i in range(num_nodes)} + delta_R = {i: [] for i in range(num_nodes)} + for e in edges: + u, v = e["endpoints"] + delta[u].append(e["edge_id"]) + delta[v].append(e["edge_id"]) + if e["is_required"]: + delta_R[u].append(e["edge_id"]) + delta_R[v].append(e["edge_id"]) + + # Create model + model = gp.Model("CARP_TwoIndex") + model.setParam("Threads", 1) + model.setParam("TimeLimit", time_limit) + model.setParam("LazyConstraints", 1) + # Reduce output verbosity slightly + model.setParam("OutputFlag", 1) + + # Decision variables + # x[k,e] in {0,1}: vehicle k services required edge e + x = {} + for k in K: + for e_id in required_edge_ids: + x[k, e_id] = model.addVar(vtype=GRB.BINARY, name=f"x_{k}_{e_id}") + + # y[k,e] in Z_+: number of times vehicle k deadheads edge e + y = {} + for k in K: + for e_id in all_edge_ids: + y[k, e_id] = model.addVar(vtype=GRB.INTEGER, lb=0, name=f"y_{k}_{e_id}") + + # p[k,i] in Z_+: parity auxiliary variable + p = {} + for k in K: + for i in range(num_nodes): + p[k, i] = model.addVar(vtype=GRB.INTEGER, lb=0, name=f"p_{k}_{i}") + + model.update() + + # Objective (1): min sum_k c^serv' x^k + sum_k c' y^k + obj = gp.LinExpr() + for k in K: + for e_id in required_edge_ids: + obj += serv_cost[e_id] * x[k, e_id] + for e_id in all_edge_ids: + obj += cost[e_id] * y[k, e_id] + model.setObjective(obj, GRB.MINIMIZE) + + # Constraint (2): sum_k x^k_e = 1 for all e in E_R + for e_id in required_edge_ids: + model.addConstr( + gp.quicksum(x[k, e_id] for k in K) == 1, + name=f"partition_{e_id}" + ) + + # Constraint (4): x^k(delta_R(i)) + y^k(delta(i)) = 2 p^k_i for all i, k + for k in K: + for i in range(num_nodes): + lhs = gp.LinExpr() + for e_id in delta_R[i]: + lhs += x[k, e_id] + for e_id in delta[i]: + lhs += y[k, e_id] + model.addConstr(lhs == 2 * p[k, i], name=f"parity_{k}_{i}") + + # Constraint (5): q' x^k <= Q for all k + for k in K: + model.addConstr( + gp.quicksum(demand[e_id] * x[k, e_id] for e_id in required_edge_ids) <= capacity, + name=f"capacity_{k}" + ) + + # Constraint (3): Subtour Elimination Constraints (SEC) via lazy constraints + # We add these dynamically via a callback. + + def sec_callback(model, where): + if where == GRB.Callback.MIPSOL: + # Get current solution + x_val = {} + y_val = {} + for k in K: + for e_id in required_edge_ids: + x_val[k, e_id] = model.cbGetSolution(x[k, e_id]) + for e_id in all_edge_ids: + y_val[k, e_id] = model.cbGetSolution(y[k, e_id]) + + for k in K: + # Find edges used by vehicle k (serviced or deadheaded) + active_edges = set() + for e_id in required_edge_ids: + if x_val[k, e_id] > 0.5: + active_edges.add(e_id) + for e_id in all_edge_ids: + if y_val[k, e_id] > 0.5: + active_edges.add(e_id) + + if not active_edges: + continue + + # Find connected components + components = find_connected_components(num_nodes, active_edges, edges) + + # For each component not containing the depot, add SEC + for comp in components: + if depot in comp: + continue + + # S = comp (subset of V \ {d}) + S = comp + + # Get required edges with both endpoints in S + er_s = [] + for e_id in required_edge_ids: + u, v = endpoints[e_id] + if u in S and v in S: + er_s.append(e_id) + + if not er_s: + continue + + # Get delta_R(S) and delta(S) + delta_r_s = [] + delta_s = [] + for e_id in all_edge_ids: + u, v = endpoints[e_id] + if (u in S) != (v in S): + delta_s.append(e_id) + if is_required[e_id]: + delta_r_s.append(e_id) + + # Add SEC: x^k(delta_R(S)) + y^k(delta(S)) >= 2 x^k_f + # for all f in E_R(S) + for f in er_s: + if x_val[k, f] > 0.5: + lhs = gp.LinExpr() + for e_id in delta_r_s: + lhs += x[k, e_id] + for e_id in delta_s: + lhs += y[k, e_id] + model.cbLazy(lhs >= 2 * x[k, f]) + + model.optimize(sec_callback) + + # Extract solution + if model.SolCount > 0: + obj_val = model.ObjVal + solution = { + "objective_value": obj_val, + "status": model.Status, + "status_str": { + GRB.OPTIMAL: "OPTIMAL", + GRB.TIME_LIMIT: "TIME_LIMIT", + GRB.INFEASIBLE: "INFEASIBLE", + GRB.INF_OR_UNBD: "INF_OR_UNBD", + }.get(model.Status, f"STATUS_{model.Status}"), + "num_vehicles": num_vehicles, + "vehicle_capacity": capacity, + "routes": [] + } + + for k in K: + route_info = { + "vehicle": k, + "serviced_edges": [], + "deadheaded_edges": [], + "total_demand": 0, + "route_cost": 0.0 + } + for e_id in required_edge_ids: + if x[k, e_id].X > 0.5: + route_info["serviced_edges"].append(e_id) + route_info["total_demand"] += demand[e_id] + route_info["route_cost"] += serv_cost[e_id] + for e_id in all_edge_ids: + yv = round(y[k, e_id].X) + if yv > 0: + route_info["deadheaded_edges"].append({ + "edge_id": e_id, + "times": yv + }) + route_info["route_cost"] += cost[e_id] * yv + solution["routes"].append(route_info) + + with open(solution_path, "w") as f: + json.dump(solution, f, indent=2) + print(f"Solution written to {solution_path}") + print(f"Objective value: {obj_val}") + else: + solution = { + "objective_value": None, + "status": model.Status, + "status_str": "NO_SOLUTION_FOUND" + } + with open(solution_path, "w") as f: + json.dump(solution, f, indent=2) + print("No feasible solution found.") + + +def main(): + parser = argparse.ArgumentParser( + description="Gurobi solver for the CARP (Two-Index Formulation)" + ) + parser.add_argument("--instance_path", type=str, required=True, + help="Path to the JSON instance file") + parser.add_argument("--solution_path", type=str, required=True, + help="Path for the output solution JSON file") + parser.add_argument("--time_limit", type=int, required=True, + help="Maximum solver runtime in seconds") + parser.add_argument("--log_path", type=str, default=None, help="Path to log incumbent solutions") + args = parser.parse_args() + install_gurobi_logger(args.log_path) + + solve_carp(args.instance_path, args.solution_path, args.time_limit) + + +if __name__ == "__main__": + main() diff --git a/bode2012/gurobi_feasi_result/large_feasi_result_1.json b/bode2012/gurobi_feasi_result/large_feasi_result_1.json new file mode 100644 index 0000000000000000000000000000000000000000..e305b82c3d6732211d5033415df1a105476e9cf1 --- /dev/null +++ b/bode2012/gurobi_feasi_result/large_feasi_result_1.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3269392edcbb7720882e77caadb88b4a9cf2fda04cf482cebd23baace4a531fd +size 102 diff --git 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b/bode2012/instance/tiny_instance.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3de7deb3664dbbbd85f99d1265c0d94dd8cca3d186e62b87daa3466bbce6b4b8 +size 6969 diff --git a/bode2012/instance_schema.json b/bode2012/instance_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..49ee99e23deed716e9c89b5c7773c389d1d082cf --- /dev/null +++ b/bode2012/instance_schema.json @@ -0,0 +1,25 @@ +{ + "graph": { + "num_nodes": " Number of nodes in the undirected network.", + "num_edges": " Number of edges in the undirected network.", + "type": " Indicates the network is undirected." + }, + "depot": " Node where all vehicles start and end their routes.", + "fleet": { + "num_vehicles": " Number of identical vehicles available in the fleet.", + "vehicle_capacity": " Maximum total demand that a single vehicle can service on its route.", + "type": " Indicates all vehicles are identical." + }, + "num_required_edges": " Number of edges that must be serviced.", + "total_demand": " Sum of demands across all required edges.", + "edges": [ + { + "edge_id": " Unique identifier for this edge.", + "endpoints": " The two nodes connected by this edge.", + "cost": " Cost incurred each time a vehicle traverses this edge without servicing it.", + "is_required": " Whether this edge must be serviced by exactly one vehicle.", + "demand": " Amount of capacity consumed when a vehicle services this edge (0 if not required).", + "service_cost": " Cost incurred when a vehicle services this edge (0 if not required)." + } + ] +} diff --git a/bode2012/mathematical_formulation.md b/bode2012/mathematical_formulation.md new file mode 100644 index 0000000000000000000000000000000000000000..871c0c0fa5a8927891aa412602b5bb411329cb66 --- /dev/null +++ b/bode2012/mathematical_formulation.md @@ -0,0 +1,41 @@ +# Original Formulation: Capacitated Arc-Routing Problem (CARP) – Two-Index Formulation + +*Source: “Cut-First Branch-and-Price-Second for the Capacitated Arc-Routing Problem,” Bode and Irnich, Operations Research 60(5):1167–1182, 2012.* + +## Sets and Parameters + +- $G = (V, E)$: undirected graph with node set $V$ and edge set $E$. + +- $E_R \subseteq E$: subset of required edges (those with positive demand). + +- $K$: fleet of $|K|$ homogeneous vehicles, stationed at depot $d \in V$. + +- $q_e \ge 0$: integer demand on edge $e \in E$; $Q$: vehicle capacity. + +- $c^{\mathrm{serv}}_e$: service cost for required edge $e \in E_R$; $c_e$: deadheading cost for edge $e \in E$. + +- Notation: for $S \subseteq V$, $\delta(S)$ is the set of edges with exactly one endpoint in $S$, $\delta_R(S) = \delta(S) \cap E_R$, $E_R(S) = E(S) \cap E_R$; $\delta(i) := \delta(\{i\})$; for variable $y$ and subset $F$, $y(F) = \sum_{e \in F} y_e$. + +## Decision Variables + +- $x^k_e \in \{0,1\}$ for $e \in E_R$, $k \in K$: $1$ if vehicle $k$ services edge $e$. + +- $y^k_e \in \mathbb{Z}_+$ for $e \in E$, $k \in K$: number of times vehicle $k$ traverses edge $e$ without servicing it. + +- $p^k_i \in \mathbb{Z}_+$ for $i \in V$, $k \in K$: auxiliary parity variable at node $i$ for vehicle $k$. + +## Objective + +$$\begin{equation} +\min \sum_{k \in K} {c^{\mathrm{serv}}}^{\!\top} x^k + \sum_{k \in K} c^{\top} y^k \tag{1} +\end{equation}$$ + +## Constraints + +$$\begin{align} +\sum_{k \in K} x^k_e &= 1, & \forall\, e \in E_R \tag{2} \\ +x^k(\delta_R(S)) + y^k(\delta(S)) &\ge 2\, x^k_f, & \forall\, S \subseteq V \setminus \{d\},\; f \in E_R(S),\; k \in K \tag{3} \\ +x^k(\delta_R(i)) + y^k(\delta(i)) &= 2\, p^k_i, & \forall\, i \in V,\; k \in K \tag{4} \\ +q^{\top} x^k &\le Q, & \forall\, k \in K \tag{5} \\ +p^k \in \mathbb{Z}_+^{|V|},\; x^k &\in \{0,1\}^{|E_R|},\; y^k \in \mathbb{Z}_+^{|E|}, & \forall\, k \in K \tag{6} +\end{align}$$ diff --git a/bode2012/problem_description.txt b/bode2012/problem_description.txt new file mode 100644 index 0000000000000000000000000000000000000000..0cd10a7ff417e2ea82de4e3d2003411a3baf203f --- /dev/null +++ b/bode2012/problem_description.txt @@ -0,0 +1,7 @@ +# Problem Description + +A service company operates a fleet of identical vehicles, all stationed at a single depot node within an undirected network of nodes and edges. Each edge in the network has a traversal cost representing the expense of traveling along that edge without performing any service (called deadheading). A subset of the edges are designated as required edges, each carrying a positive demand quantity and a service cost incurred when a vehicle services that edge. The number of vehicles in the fleet is fixed and given. + +The company must assign each required edge to exactly one vehicle for servicing and determine the route each vehicle follows through the network. A vehicle's route must start and end at the depot and form a valid closed walk that services all of its assigned edges. Every required edge must be serviced by exactly one vehicle across the entire fleet. A vehicle may traverse any edge (required or not) any number of times without servicing it, purely for the purpose of traveling between locations. A vehicle can only service a required edge that it physically reaches during its walk from the depot. Each vehicle has a capacity limit, and the sum of the demands of all required edges assigned to a single vehicle must not exceed that capacity. + +The input data specifies the set of nodes and edges forming the undirected network, which edges are required, the demand on each required edge, the service cost for each required edge, the deadheading cost for each edge, the depot node, the number of vehicles, and the vehicle capacity. The goal is to minimize the total cost, computed as the sum of all service costs for the required edges serviced plus the sum of all deadheading costs incurred across all vehicles, where each deadheading traversal of an edge contributes its traversal cost once per crossing. Since every required edge is serviced exactly once, the total service cost component is constant across all feasible solutions, so the optimization effectively seeks to minimize total deadheading cost. diff --git a/bode2012/solution_logger.py b/bode2012/solution_logger.py new file mode 100644 index 0000000000000000000000000000000000000000..5393fa935ce71df9da95cee04fe5199077cb8461 --- /dev/null +++ b/bode2012/solution_logger.py @@ -0,0 +1,61 @@ +""" +Convergence logger for optimization algorithms. + +Records incumbent solutions with timestamps to a JSONL file. +This module is provided to LLM-generated programs — they only need to call +`log(objective_value)` whenever a better feasible solution is found. + +Usage in generated code: + from solution_logger import SolutionLogger + logger = SolutionLogger(log_path, sense="minimize") # or "maximize" + # ... inside algorithm loop: + logger.log(objective_value) +""" + +import json +import time + + +class SolutionLogger: + def __init__(self, log_path, sense="minimize"): + """ + Args: + log_path: Path to the JSONL output file. + sense: "minimize" or "maximize". + """ + self.log_path = log_path + self.sense = sense + self.start_time = time.time() + self.best_obj = None + self.min_interval = 0.1 # seconds, avoid excessive writes + + self._last_log_time = 0.0 + # Clear the file + with open(self.log_path, "w") as f: + pass + + def log(self, objective_value): + """Record a new incumbent if it improves on the best known.""" + if objective_value is None: + return + + # Check if this is an improvement + if self.best_obj is not None: + if self.sense == "minimize" and objective_value >= self.best_obj: + return + if self.sense == "maximize" and objective_value <= self.best_obj: + return + + elapsed = time.time() - self.start_time + + # Throttle writes + if self.best_obj is not None and elapsed - self._last_log_time < self.min_interval: + self.best_obj = objective_value + return + + self.best_obj = objective_value + self._last_log_time = elapsed + + with open(self.log_path, "a") as f: + f.write(json.dumps({"time": round(elapsed, 3), + "objective_value": objective_value}) + "\n") diff --git a/bode2012/solution_schema.json b/bode2012/solution_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..d96bf3c9ef89711cebc898702098d8c2e084942e --- /dev/null +++ b/bode2012/solution_schema.json @@ -0,0 +1,15 @@ +{ + "objective_value": " Total cost of the routing plan, combining service costs on required edges and travel costs on deadheaded edges across all vehicles.", + "routes": [ + { + "vehicle": " Identifier of the vehicle performing this route.", + "serviced_edges": " Identifiers of the required edges that this vehicle services during its route.", + "deadheaded_edges": [ + { + "edge_id": " Identifier of an edge traversed by this vehicle without servicing it.", + "times": " Number of times this vehicle traverses the edge as deadheading." + } + ] + } + ] +} diff --git a/bodur2017/feasibility_check.py b/bodur2017/feasibility_check.py new file mode 100644 index 0000000000000000000000000000000000000000..54ed7635ae321471bb6226a0d954181ca0e4ebff --- /dev/null +++ b/bodur2017/feasibility_check.py @@ -0,0 +1,470 @@ +""" +Feasibility checker for the Stochastic Capacitated Facility Location (CAP) problem. +Based on formulation (15) from Bodur, Dash, Günlük, Luedtke (2017). + +Constraints numbered top-to-bottom from the formulation section: + Constraint 1 (15b): sum_i y^k_{ij} >= lambda^k_j, for all j in J, k in K + Constraint 2 (15c): sum_j y^k_{ij} <= s_i * x_i, for all i in I, k in K + Constraint 3 (15d): sum_i s_i * x_i >= max_k sum_j lambda^k_j + Constraint 4 (15e): x in {0,1}^|I|, y >= 0 + Constraint 5 (Tier C obj-consistency): reported objective_value must equal the + recomputed objective sum_i f_i*x_i + sum_k p_k*sum_{i,j} q_{ij}*y^k_{ij}. +""" + +import argparse +import json +import math + +# FRONTIEROR_RELEASE_CONTRACT_V2 +_FRONTIEROR_REQUIRED_SOLUTION_FIELDS = ('objective_value', 'open_facilities', 'x', 'y') +_FRONTIEROR_NONEMPTY_SOLUTION_FIELDS = ('open_facilities', 'y') +_FRONTIEROR_DERIVED_FIELD_RULES = () +_FRONTIEROR_CONDITIONAL_FIELD_RULES = () + + +def _frontieror_validate_solution_contract(solution): + """Return a structured rejection when the public solution contract is absent.""" + violations = [] + if not isinstance(solution, dict): + violations.append("solution must be a JSON object") + else: + missing = [ + field + for field in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS + if field not in solution or solution[field] is None + ] + if missing: + violations.append( + "missing required solution field(s): " + ", ".join(missing) + ) + + for discriminator, expected_value, fields in _FRONTIEROR_CONDITIONAL_FIELD_RULES: + if solution.get(discriminator) != expected_value: + continue + conditional_missing = [ + field + for field in fields + if field not in solution or solution[field] is None + ] + if conditional_missing: + violations.append( + "missing conditionally required solution field(s): " + + ", ".join(conditional_missing) + ) + + empty = [ + field + for field in _FRONTIEROR_NONEMPTY_SOLUTION_FIELDS + if field in solution and solution[field] in (None, [], {}) + ] + if empty: + violations.append( + "empty required decision witness field(s): " + ", ".join(empty) + ) + + for target, operation, source, source_key in _FRONTIEROR_DERIVED_FIELD_RULES: + try: + if operation == "assignment_key_set": + reported_set = set(solution[target]) + expected_set = {entry[source_key] for entry in solution[source]} + if reported_set != expected_set: + violations.append( + f"{target}={sorted(reported_set)} does not match the " + f"decision-derived value {sorted(expected_set)}" + ) + continue + + reported = float(solution[target]) + if operation == "length": + expected = float(len(solution[source])) + elif operation == "mapping_value": + expected = float(solution[source][source_key]) + else: + raise ValueError("unknown derived-field operation") + except (KeyError, TypeError, ValueError, OverflowError): + violations.append( + f"cannot derive {target} from decision witness field {source}" + ) + continue + if ( + reported != reported + or expected != expected + or reported in (float("inf"), float("-inf")) + or expected in (float("inf"), float("-inf")) + or abs(reported - expected) > 1e-6 + ): + violations.append( + f"{target}={reported} does not match the decision-derived " + f"value {expected}" + ) + + if "objective_value" in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS: + objective = solution.get("objective_value") + if ( + isinstance(objective, bool) + or not isinstance(objective, (int, float)) + or objective != objective + or objective in (float("inf"), float("-inf")) + ): + violations.append("objective_value must be a finite JSON number") + + if not violations: + return None + return { + "feasible": False, + "violated_constraints": ["solution_schema"], + "violations": violations, + "violation_magnitudes": [], + } + + +def _frontieror_write_unexpected_rejection(error): + """Turn malformed candidate/checker exceptions into a normal rejection.""" + import json as _frontieror_json + import sys as _frontieror_sys + + result_path = None + for index, argument in enumerate(_frontieror_sys.argv): + if argument == "--result_path" and index + 1 < len(_frontieror_sys.argv): + result_path = _frontieror_sys.argv[index + 1] + break + if argument.startswith("--result_path="): + result_path = argument.split("=", 1)[1] + break + if not result_path: + return False + try: + with open(result_path, "w") as result_handle: + _frontieror_json.dump( + { + "feasible": False, + "violated_constraints": ["malformed_solution"], + "violations": [ + "checker rejected malformed candidate: " + + type(error).__name__ + + ": " + + str(error)[:500] + ], + "violation_magnitudes": [], + }, + result_handle, + indent=2, + ) + except Exception: + return False + return True + + + +def check_feasibility(instance, solution): + tol = 1e-5 + eps = 1e-5 + + violations = [] + violation_magnitudes = [] + + I = instance["num_facilities"] + J = instance["num_customers"] + K = instance["num_scenarios"] + + facilities = instance["facilities"] + scenarios = instance["scenarios"] + + s_cap = [facilities[i]["capacity"] for i in range(I)] + lam = [[scenarios[k]["demands"][j] for j in range(J)] for k in range(K)] + + # Parse x values + x_sol = solution.get("x", {}) + x = [0.0] * I + for key, val in x_sol.items(): + x[int(key)] = val + + # Parse y values (may not exist for efficient solutions) + y_raw = solution.get("y", None) + has_y = y_raw is not None and len(y_raw) > 0 + + # Build y[k][i][j] sparse structure + y = {} + if has_y: + for k_str, scen_data in y_raw.items(): + k_idx = int(k_str) + y[k_idx] = {} + for i_str, row_data in scen_data.items(): + i_idx = int(i_str) + y[k_idx][i_idx] = {} + for j_str, val in row_data.items(): + y[k_idx][i_idx][int(j_str)] = val + + # ------------------------------------------------------------------------- + # Constraint 1 (15b): sum_i y^k_{ij} >= lambda^k_j, for all j, k + # This is a >= constraint. violation_amount = max(0, RHS - LHS) + # ------------------------------------------------------------------------- + if has_y: + for k in range(K): + for j in range(J): + lhs = 0.0 + scen_y = y.get(k, {}) + for i in range(I): + lhs += scen_y.get(i, {}).get(j, 0.0) + rhs = lam[k][j] + violation_amount = rhs - lhs + if violation_amount > tol: + normalizer = max(abs(rhs), eps) + ratio = violation_amount / normalizer + violations.append( + f"Constraint 1 (15b): Demand not met for customer {j}, " + f"scenario {k}: sum_i y[{k}][i][{j}] = {lhs:.6f} < " + f"lambda[{k}][{j}] = {rhs:.6f}" + ) + violation_magnitudes.append({ + "constraint": 1, + "lhs": lhs, + "rhs": rhs, + "raw_excess": violation_amount, + "normalizer": normalizer, + "ratio": ratio, + }) + + # ------------------------------------------------------------------------- + # Constraint 2 (15c): sum_j y^k_{ij} <= s_i * x_i, for all i, k + # This is a <= constraint. violation_amount = max(0, LHS - RHS) + # ------------------------------------------------------------------------- + if has_y: + for k in range(K): + for i in range(I): + lhs = 0.0 + scen_y = y.get(k, {}) + fac_y = scen_y.get(i, {}) + for j_idx, val in fac_y.items(): + lhs += val + rhs = s_cap[i] * x[i] + violation_amount = lhs - rhs + if violation_amount > tol: + normalizer = max(abs(rhs), eps) + ratio = violation_amount / normalizer + violations.append( + f"Constraint 2 (15c): Capacity exceeded for facility {i}, " + f"scenario {k}: sum_j y[{k}][{i}][j] = {lhs:.6f} > " + f"s[{i}]*x[{i}] = {rhs:.6f}" + ) + violation_magnitudes.append({ + "constraint": 2, + "lhs": lhs, + "rhs": rhs, + "raw_excess": violation_amount, + "normalizer": normalizer, + "ratio": ratio, + }) + + # ------------------------------------------------------------------------- + # Constraint 3 (15d): sum_i s_i * x_i >= max_k sum_j lambda^k_j + # This is a >= constraint. violation_amount = max(0, RHS - LHS) + # ------------------------------------------------------------------------- + lhs_3 = sum(s_cap[i] * x[i] for i in range(I)) + max_total_demand = max(sum(lam[k][j] for j in range(J)) for k in range(K)) + rhs_3 = max_total_demand + violation_amount_3 = rhs_3 - lhs_3 + if violation_amount_3 > tol: + normalizer = max(abs(rhs_3), eps) + ratio = violation_amount_3 / normalizer + violations.append( + f"Constraint 3 (15d): Total open capacity insufficient: " + f"sum_i s_i*x_i = {lhs_3:.6f} < max_k sum_j lambda^k_j = {rhs_3:.6f}" + ) + violation_magnitudes.append({ + "constraint": 3, + "lhs": lhs_3, + "rhs": rhs_3, + "raw_excess": violation_amount_3, + "normalizer": normalizer, + "ratio": ratio, + }) + + # ------------------------------------------------------------------------- + # Constraint 4 (15e): x in {0,1}^|I|, y >= 0 + # For x binary: violation_amount = min(|x_i|, |x_i - 1|) if not 0 or 1 + # For y non-negative: violation_amount = max(0, -y^k_{ij}) + # ------------------------------------------------------------------------- + # Check x binary + for i in range(I): + xi = x[i] + dist_to_binary = min(abs(xi), abs(xi - 1.0)) + if dist_to_binary > tol: + rhs = round(xi) # nearest binary value + violation_amount = dist_to_binary + normalizer = max(abs(rhs), eps) + ratio = violation_amount / normalizer + violations.append( + f"Constraint 4 (15e): x[{i}] = {xi} is not binary" + ) + violation_magnitudes.append({ + "constraint": 4, + "lhs": xi, + "rhs": rhs, + "raw_excess": violation_amount, + "normalizer": normalizer, + "ratio": ratio, + }) + + # Check y >= 0 + if has_y: + for k_idx, scen_data in y.items(): + for i_idx, row_data in scen_data.items(): + for j_idx, val in row_data.items(): + if val < -tol: + violation_amount = -val + rhs = 0.0 + normalizer = eps + ratio = violation_amount / normalizer + violations.append( + f"Constraint 4 (15e): y[{k_idx}][{i_idx}][{j_idx}] " + f"= {val:.6f} < 0" + ) + violation_magnitudes.append({ + "constraint": 4, + "lhs": val, + "rhs": rhs, + "raw_excess": violation_amount, + "normalizer": normalizer, + "ratio": ratio, + }) + + # ------------------------------------------------------------------------- + # Constraint 5 (Tier C): objective consistency. + # Recompute obj = sum_i f_i*x_i + sum_k p_k * sum_{i,j} q_{ij} * y[k,i,j] + # and reject when reported objective_value disagrees by more than tolerance. + # Only applied when y is present (efficient checker mode without y cannot + # verify the second-stage transportation cost). + # ------------------------------------------------------------------------- + reported_obj = solution.get("objective_value") + if has_y and reported_obj is not None: + try: + reported = float(reported_obj) + except (TypeError, ValueError): + reported = None + if reported is not None and math.isfinite(reported): + f_cost = [facilities[i]["opening_cost"] for i in range(I)] + p_k = [scenarios[k]["probability"] for k in range(K)] + transport = instance["transportation_costs"] + + opening_part = sum(f_cost[i] * float(x[i]) for i in range(I)) + transport_part = 0.0 + for k_idx, scen_data in y.items(): + if k_idx < 0 or k_idx >= K: + continue + p = p_k[k_idx] + for i_idx, row_data in scen_data.items(): + if i_idx < 0 or i_idx >= I: + continue + q_row = transport[i_idx] + for j_idx, val in row_data.items(): + if j_idx < 0 or j_idx >= J: + continue + transport_part += p * q_row[j_idx] * float(val) + true_obj = opening_part + transport_part + + abs_diff = abs(reported - true_obj) + # 0.1% relative tolerance with 1e-3 absolute floor + obj_tol = max(1e-3, 1e-3 * abs(true_obj)) + if abs_diff > obj_tol: + normalizer = max(abs(true_obj), eps) + ratio = abs_diff / normalizer + violations.append( + f"Constraint 5 (obj): reported objective_value={reported} " + f"differs from recomputed sum_i f_i*x_i + sum_k p_k*" + f"sum_{{i,j}} q_{{ij}}*y[k,i,j]={true_obj} " + f"(|diff|={abs_diff:.6g}, tol={obj_tol:.6g})" + ) + violation_magnitudes.append({ + "constraint": 5, + "lhs": reported, + "rhs": true_obj, + "raw_excess": abs_diff, + "normalizer": normalizer, + "ratio": ratio, + }) + elif reported is not None and not math.isfinite(reported): + # Non-finite (inf, nan, sys.float_info.max-ish overflow path): + # always flag as obj-inconsistent regardless of y. + violations.append( + f"Constraint 5 (obj): reported objective_value={reported} is " + f"not a finite number." + ) + violation_magnitudes.append({ + "constraint": 5, + "lhs": reported, + "rhs": 0.0, + "raw_excess": float("inf"), + "normalizer": eps, + "ratio": float("inf"), + }) + + # Build result + violated_constraints = sorted(set(vm["constraint"] for vm in violation_magnitudes)) + feasible = len(violated_constraints) == 0 + + # Aggregate violation messages per constraint + aggregated_violations = [] + for c in violated_constraints: + msgs = [v for v, vm in zip(violations, violation_magnitudes) if vm["constraint"] == c] + if len(msgs) <= 3: + aggregated_violations.append("; ".join(msgs)) + else: + aggregated_violations.append( + f"{msgs[0]}; ... ({len(msgs)} total violations for constraint {c})" + ) + + result = { + "feasible": feasible, + "violated_constraints": violated_constraints, + "violations": aggregated_violations, + "violation_magnitudes": violation_magnitudes, + } + return result + + +def main(): + parser = argparse.ArgumentParser( + description="Feasibility checker for Stochastic CAP (Bodur et al. 2017, formulation 15)." + ) + parser.add_argument("--instance_path", type=str, required=True, + help="Path to the JSON instance file.") + parser.add_argument("--solution_path", type=str, required=True, + help="Path to the JSON solution file.") + parser.add_argument("--result_path", type=str, required=True, + help="Path to write the JSON feasibility result.") + args = parser.parse_args() + + with open(args.instance_path) as f: + instance = json.load(f) + with open(args.solution_path) as f: + solution = json.load(f) + _frontieror_contract_result = _frontieror_validate_solution_contract(solution) + if _frontieror_contract_result is not None: + with open(args.result_path, "w") as _frontieror_result_handle: + json.dump( + _frontieror_contract_result, + _frontieror_result_handle, + indent=2, + ) + return + + result = check_feasibility(instance, solution) + + with open(args.result_path, "w") as f: + json.dump(result, f, indent=2) + + status = "FEASIBLE" if result["feasible"] else "INFEASIBLE" + n_violations = len(result["violation_magnitudes"]) + print(f"{status} ({n_violations} violation(s) across " + f"{len(result['violated_constraints'])} constraint(s))") + + +if __name__ == "__main__": + try: + main() + except Exception as _frontieror_unexpected_error: + if not _frontieror_write_unexpected_rejection( + _frontieror_unexpected_error + ): + raise diff --git a/bodur2017/gurobi_code.py b/bodur2017/gurobi_code.py new file mode 100644 index 0000000000000000000000000000000000000000..c687248b3d275469031e6f41bec92f54daffcb7e --- /dev/null +++ b/bodur2017/gurobi_code.py @@ -0,0 +1,164 @@ +""" +Gurobi implementation of the Stochastic Capacitated Facility Location Problem (CAP) +from Bodur, Dash, Günlük, Luedtke (2017), "Strengthened Benders Cuts for Stochastic +Integer Programs with Continuous Recourse", INFORMS Journal on Computing, 29(1):77-91. + +Implements formulation (15) directly as an extensive MIP. +""" + +import argparse +import json +import math +import sys + +import gurobipy as gp +from gurobipy import GRB +import os as _os, sys as _sys +# Walk up from this file's directory to find repo root (containing scripts/). +_repo = _os.path.dirname(_os.path.abspath(__file__)) +while _repo != _os.path.dirname(_repo) and not _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _repo = _os.path.dirname(_repo) +if _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _sys.path.insert(0, _repo) +try: + from scripts.utils.gurobi_log_helper import install_gurobi_logger +except ImportError: + def install_gurobi_logger(log_path): # no-op fallback when scripts/ unavailable + pass + + +def load_instance(instance_path): + with open(instance_path) as f: + return json.load(f) + + +def solve(instance_path, solution_path, time_limit): + data = load_instance(instance_path) + + I = data["num_facilities"] # number of facilities + J = data["num_customers"] # number of customers + K = data["num_scenarios"] # number of scenarios + + facilities = data["facilities"] + customers = data["customers"] + transport = data["transportation_costs"] # transport[i][j] = cost q_{ij} + scenarios = data["scenarios"] + + f_cost = [facilities[i]["opening_cost"] for i in range(I)] + s_cap = [facilities[i]["capacity"] for i in range(I)] + q_cost = [[transport[i][j] for j in range(J)] for i in range(I)] + + p_k = [scenarios[k]["probability"] for k in range(K)] + lam = [[scenarios[k]["demands"][j] for j in range(J)] for k in range(K)] + + # max total demand across scenarios (for constraint 15d) + max_total_demand = max(sum(lam[k][j] for j in range(J)) for k in range(K)) + + model = gp.Model("CAP_SIP") + model.setParam("TimeLimit", time_limit) + model.setParam("MIPGap", 0.001) # 0.1% relative gap as in paper + model.setParam("Threads", 1) + + # First-stage binary variables: x[i] = 1 if facility i is opened + x = model.addVars(I, vtype=GRB.BINARY, name="x") + + # Second-stage continuous variables: y[k,i,j] = flow from facility i to customer j under scenario k + y = model.addVars(K, I, J, lb=0.0, vtype=GRB.CONTINUOUS, name="y") + + # Objective (15a): minimize opening costs + expected transportation costs + obj = gp.quicksum(f_cost[i] * x[i] for i in range(I)) + \ + gp.quicksum(p_k[k] * q_cost[i][j] * y[k, i, j] + for k in range(K) for i in range(I) for j in range(J)) + model.setObjective(obj, GRB.MINIMIZE) + + # Constraint (15b): demand satisfaction for each customer j, scenario k + for k in range(K): + for j in range(J): + model.addConstr( + gp.quicksum(y[k, i, j] for i in range(I)) >= lam[k][j], + name=f"demand_{k}_{j}" + ) + + # Constraint (15c): capacity constraint for each facility i, scenario k + for k in range(K): + for i in range(I): + model.addConstr( + gp.quicksum(y[k, i, j] for j in range(J)) <= s_cap[i] * x[i], + name=f"capacity_{k}_{i}" + ) + + # Constraint (15d): relatively complete recourse — total open capacity >= max total demand + model.addConstr( + gp.quicksum(s_cap[i] * x[i] for i in range(I)) >= max_total_demand, + name="recourse_feasibility" + ) + + model.optimize() + + # Extract best solution found + status = model.Status + obj_val = None + + if status == GRB.OPTIMAL or (status == GRB.TIME_LIMIT and model.SolCount > 0): + obj_val = model.ObjVal + + # Build solution dict + x_sol = {i: round(x[i].X) for i in range(I)} + y_sol = {} + for k in range(K): + y_sol[k] = {} + for i in range(I): + y_sol[k][i] = {} + for j in range(J): + val = y[k, i, j].X + if val > 1e-9: + y_sol[k][i][j] = val + + solution = { + "instance_id": data.get("instance_id", 1), + "objective_value": obj_val, + "status": model.Status, + "mip_gap": model.MIPGap, + "open_facilities": [i for i in range(I) if x_sol[i] > 0.5], + "x": x_sol, + "y": {str(k): {str(i): {str(j): v for j, v in row.items()} + for i, row in scen.items()} + for k, scen in y_sol.items()}, + } + else: + # No feasible solution found + solution = { + "instance_id": data.get("instance_id", 1), + "objective_value": None, + "status": model.Status, + "message": "No feasible solution found within time limit." + } + + with open(solution_path, "w") as f: + json.dump(solution, f, indent=2) + + print(f"Objective value: {obj_val}") + print(f"Solution written to {solution_path}") + + return obj_val + + +def main(): + parser = argparse.ArgumentParser( + description="Solve Stochastic CAP using Gurobi (extensive formulation)." + ) + parser.add_argument("--instance_path", type=str, required=True, + help="Path to the JSON instance file.") + parser.add_argument("--solution_path", type=str, required=True, + help="Path to write the JSON solution file.") + parser.add_argument("--time_limit", type=int, default=3600, + help="Maximum solver runtime in seconds.") + parser.add_argument("--log_path", type=str, default=None, help="Path to log incumbent solutions") + args = parser.parse_args() + install_gurobi_logger(args.log_path) + + solve(args.instance_path, args.solution_path, args.time_limit) + + +if __name__ == "__main__": + main() diff --git a/bodur2017/gurobi_feasi_result/large_feasi_result_1.json b/bodur2017/gurobi_feasi_result/large_feasi_result_1.json new file mode 100644 index 0000000000000000000000000000000000000000..e305b82c3d6732211d5033415df1a105476e9cf1 --- /dev/null +++ b/bodur2017/gurobi_feasi_result/large_feasi_result_1.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3269392edcbb7720882e77caadb88b4a9cf2fda04cf482cebd23baace4a531fd +size 102 diff --git a/bodur2017/gurobi_feasi_result/large_feasi_result_2.json b/bodur2017/gurobi_feasi_result/large_feasi_result_2.json new file mode 100644 index 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sha256:cf74a4525c390d9998703c659cc5cfa043677d9271cf7dd0264fec8762fd7e37 +size 409993 diff --git a/bodur2017/instance_schema.json b/bodur2017/instance_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..1d24b104fbd9932492c23d292ddabc79755a4f48 --- /dev/null +++ b/bodur2017/instance_schema.json @@ -0,0 +1,33 @@ +{ + "num_facilities": " Number of candidate facility locations.", + "num_customers": " Number of customers whose demand must be satisfied.", + "num_scenarios": " Number of demand scenarios considered.", + "capacity_factor": " Ratio of total facility capacity to total expected demand, controlling how tight capacity is.", + "variance_level": " Level of demand variability across scenarios ('normal' or 'high').", + "facilities": [ + { + "id": " Zero-based index identifying the facility.", + "opening_cost": " Fixed cost incurred if this facility is opened.", + "capacity": " Maximum total units this facility can ship across all customers in any scenario.", + "location_x": " X-coordinate of the facility location, used to compute transportation costs.", + "location_y": " Y-coordinate of the facility location, used to compute transportation costs." + } + ], + "customers": [ + { + "id": " Zero-based index identifying the customer.", + "deterministic_demand": " Expected (mean) demand of this customer.", + "demand_std_fraction": " Ratio of demand standard deviation to mean demand for this customer.", + "location_x": " X-coordinate of the customer location, used to compute transportation costs.", + "location_y": " Y-coordinate of the customer location, used to compute transportation costs." + } + ], + "transportation_costs": " Per-unit cost of shipping from each facility to each customer, equal to the Euclidean distance between them.", + "scenarios": [ + { + "id": " Zero-based index identifying the scenario.", + "probability": " Probability of this scenario occurring.", + "demands": " Realized demand of each customer under this scenario." + } + ] +} diff --git a/bodur2017/mathematical_formulation.md b/bodur2017/mathematical_formulation.md new file mode 100644 index 0000000000000000000000000000000000000000..547472f55dddacf6a5a62b3fdf16a9aea26f5eda --- /dev/null +++ b/bodur2017/mathematical_formulation.md @@ -0,0 +1,40 @@ +# Original Formulation: Stochastic Capacitated Facility Location Problem (CAP) – Extensive Form + +*Source: “Strengthened Benders Cuts for Stochastic Integer Programs with Continuous Recourse,” Bodur, Dash, Günlük, and Luedtke, INFORMS Journal on Computing 29(1):77–91, 2017. (Test problem of Section 4, equations (15a)–(15e).)* + +## Sets and Parameters + +- $\mathcal{I}$: set of potential facilities (indexed by $i$). + +- $\mathcal{J}$: set of customers (indexed by $j$). + +- $\mathcal{K}$: set of scenarios (indexed by $k$), each with probability $1/|\mathcal{K}|$. + +- $f_i$: fixed opening cost of facility $i$. + +- $s_i$: capacity of facility $i$. + +- $q_{ij}$: per-unit flow cost from facility $i$ to customer $j$. + +- $\lambda^k_j$: demand of customer $j$ under scenario $k$ (sampled from a normal distribution; see paper). + +## Decision Variables + +- $x_i \in \{0,1\}$: $1$ if facility $i$ is opened. + +- $y^k_{ij} \ge 0$: amount of customer $j$’s demand satisfied by facility $i$ under scenario $k$. + +## Objective + +$$\begin{equation} +\min \sum_{i \in \mathcal{I}} f_i\, x_i \;+\; \frac{1}{|\mathcal{K}|} \sum_{k \in \mathcal{K}} \sum_{i \in \mathcal{I}} \sum_{j \in \mathcal{J}} q_{ij}\, y^k_{ij} \tag{15a} +\end{equation}$$ + +## Constraints + +$$\begin{align} +\sum_{i \in \mathcal{I}} y^k_{ij} &\ge \lambda^k_j, & j \in \mathcal{J},\; k \in \mathcal{K} \tag{15b} \\ +\sum_{j \in \mathcal{J}} y^k_{ij} &\le s_i\, x_i, & i \in \mathcal{I},\; k \in \mathcal{K} \tag{15c} \\ +\sum_{i \in \mathcal{I}} s_i\, x_i &\ge \max_{k \in \mathcal{K}} \sum_{j \in \mathcal{J}} \lambda^k_j, & \tag{15d} \\ +x \in \{0,1\}^{|\mathcal{I}|}, \quad y &\in \mathbb{R}_+^{|\mathcal{I}| \times |\mathcal{J}| \times |\mathcal{K}|}. & \tag{15e} +\end{align}$$ diff --git a/bodur2017/problem_description.txt b/bodur2017/problem_description.txt new file mode 100644 index 0000000000000000000000000000000000000000..4e19a7fb48b296b54887b5e7953e738163fc27bf --- /dev/null +++ b/bodur2017/problem_description.txt @@ -0,0 +1,11 @@ +# Problem Description + +A logistics company must decide which facilities to open among a set of potential facility locations in order to serve a set of customers whose demands are uncertain. There are a given number of potential facilities and a given number of customers. Uncertainty in customer demands is captured through a finite set of scenarios, each occurring with a known probability (all scenarios are equally likely when the scenario set is generated by sampling). The company must commit to facility opening decisions before observing which demand scenario materializes; once a scenario is realized, the company allocates supply from open facilities to customers. + +Each potential facility has a fixed opening cost and a maximum capacity. Each customer has a demand that varies across scenarios; the demand of each customer under each scenario is provided as input. For each facility-customer pair, a unit transportation cost is given, representing the cost of shipping one unit of product from that facility to that customer. The input data also includes the probability associated with each scenario. + +The company must make two kinds of choices. First, for each potential facility, it must decide whether or not to open it. Second, for each scenario, it must determine how much of each customer's demand is served by each open facility. The facility opening decisions are made before the demand scenario is known, while the allocation decisions are made after the scenario is revealed. + +Every customer's demand must be fully satisfied in every scenario: the total amount shipped to a customer from all facilities must be at least as large as that customer's demand under the given scenario. In each scenario, the total amount shipped from any facility across all customers may not exceed that facility's capacity if the facility is open, and must be zero if the facility is not open. Additionally, the total capacity of all opened facilities must be at least as large as the maximum total customer demand across all scenarios, ensuring that the company can always serve all customers regardless of which scenario occurs. + +The goal is to minimize total expected cost, defined as the sum of the fixed opening costs of all opened facilities plus the expected (probability-weighted average over all scenarios) total transportation cost of shipping goods from facilities to customers. diff --git a/bodur2017/solution_logger.py b/bodur2017/solution_logger.py new file mode 100644 index 0000000000000000000000000000000000000000..5393fa935ce71df9da95cee04fe5199077cb8461 --- /dev/null +++ b/bodur2017/solution_logger.py @@ -0,0 +1,61 @@ +""" +Convergence logger for optimization algorithms. + +Records incumbent solutions with timestamps to a JSONL file. +This module is provided to LLM-generated programs — they only need to call +`log(objective_value)` whenever a better feasible solution is found. + +Usage in generated code: + from solution_logger import SolutionLogger + logger = SolutionLogger(log_path, sense="minimize") # or "maximize" + # ... inside algorithm loop: + logger.log(objective_value) +""" + +import json +import time + + +class SolutionLogger: + def __init__(self, log_path, sense="minimize"): + """ + Args: + log_path: Path to the JSONL output file. + sense: "minimize" or "maximize". + """ + self.log_path = log_path + self.sense = sense + self.start_time = time.time() + self.best_obj = None + self.min_interval = 0.1 # seconds, avoid excessive writes + + self._last_log_time = 0.0 + # Clear the file + with open(self.log_path, "w") as f: + pass + + def log(self, objective_value): + """Record a new incumbent if it improves on the best known.""" + if objective_value is None: + return + + # Check if this is an improvement + if self.best_obj is not None: + if self.sense == "minimize" and objective_value >= self.best_obj: + return + if self.sense == "maximize" and objective_value <= self.best_obj: + return + + elapsed = time.time() - self.start_time + + # Throttle writes + if self.best_obj is not None and elapsed - self._last_log_time < self.min_interval: + self.best_obj = objective_value + return + + self.best_obj = objective_value + self._last_log_time = elapsed + + with open(self.log_path, "a") as f: + f.write(json.dumps({"time": round(elapsed, 3), + "objective_value": objective_value}) + "\n") diff --git a/bodur2017/solution_schema.json b/bodur2017/solution_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..0a2305729fb1c440836f9ba207252bb7af11dae7 --- /dev/null +++ b/bodur2017/solution_schema.json @@ -0,0 +1,6 @@ +{ + "objective_value": " Total expected cost combining facility opening costs and the probability-weighted transportation costs across all demand scenarios.", + "open_facilities": " Identifiers of the facilities selected to be opened.", + "x": " Whether each facility is opened (1 = opened, 0 = closed).", + "y": " '{facility}' -> '{customer}'> Quantity shipped from each facility to each customer under each demand scenario." +} diff --git a/boland2019/feasibility_check.py b/boland2019/feasibility_check.py new file mode 100644 index 0000000000000000000000000000000000000000..d673ff4490dbe7080c66c95ddc71ea2b0ecee15c --- /dev/null +++ b/boland2019/feasibility_check.py @@ -0,0 +1,459 @@ +#!/usr/bin/env python3 +""" +Feasibility checker for BO-SSCFLP solutions from: +Gadegaard, Nielsen, and Ehrgott (2019), +"Bi-objective branch-and-cut algorithms based on LP relaxation and bound sets" + +Constraints numbered top-to-bottom from the formulation in math_model.txt: + Constraint 1: Ax <= b + Maps to SSCFLP constraints (inferred from code, not specified in paper): + (a) Single-source assignment: sum_i x[i,j] = 1 for all j + (b) Capacity: sum_j d[j]*x[i,j] <= s[i]*y[i] for all i + (c) Linking: x[i,j] <= y[i] for all i,j + Constraint 2: x in {0,1}^n (binary integrality) + Constraint 3: Objective consistency -- reported objective_value must equal + the minimum over non-dominated solutions of (z1+z2)/2, where each + (z1, z2) is recomputed from open_facilities and assignments using the + instance fixed_costs / assignment_costs. Guards against fabricated + objective_value exploits (Tier C defense). + Constraints 4-11: Subproblem constraints (weighted sum, fathoming LPs) -- skipped. +""" + +import argparse +import json + +# FRONTIEROR_RELEASE_CONTRACT_V2 +_FRONTIEROR_REQUIRED_SOLUTION_FIELDS = ('objective_value', 'pareto_front', 'solutions') +_FRONTIEROR_NONEMPTY_SOLUTION_FIELDS = ('pareto_front',) +_FRONTIEROR_DERIVED_FIELD_RULES = () +_FRONTIEROR_CONDITIONAL_FIELD_RULES = () + + +def _frontieror_validate_solution_contract(solution): + """Return a structured rejection when the public solution contract is absent.""" + violations = [] + if not isinstance(solution, dict): + violations.append("solution must be a JSON object") + else: + missing = [ + field + for field in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS + if field not in solution or solution[field] is None + ] + if missing: + violations.append( + "missing required solution field(s): " + ", ".join(missing) + ) + + for discriminator, expected_value, fields in _FRONTIEROR_CONDITIONAL_FIELD_RULES: + if solution.get(discriminator) != expected_value: + continue + conditional_missing = [ + field + for field in fields + if field not in solution or solution[field] is None + ] + if conditional_missing: + violations.append( + "missing conditionally required solution field(s): " + + ", ".join(conditional_missing) + ) + + empty = [ + field + for field in _FRONTIEROR_NONEMPTY_SOLUTION_FIELDS + if field in solution and solution[field] in (None, [], {}) + ] + if empty: + violations.append( + "empty required decision witness field(s): " + ", ".join(empty) + ) + + for target, operation, source, source_key in _FRONTIEROR_DERIVED_FIELD_RULES: + try: + reported = float(solution[target]) + if operation == "length": + expected = float(len(solution[source])) + elif operation == "mapping_value": + expected = float(solution[source][source_key]) + else: + raise ValueError("unknown derived-field operation") + except (KeyError, TypeError, ValueError, OverflowError): + violations.append( + f"cannot derive {target} from decision witness field {source}" + ) + continue + if ( + reported != reported + or expected != expected + or reported in (float("inf"), float("-inf")) + or expected in (float("inf"), float("-inf")) + or abs(reported - expected) > 1e-6 + ): + violations.append( + f"{target}={reported} does not match the decision-derived " + f"value {expected}" + ) + + if "objective_value" in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS: + objective = solution.get("objective_value") + if ( + isinstance(objective, bool) + or not isinstance(objective, (int, float)) + or objective != objective + or objective in (float("inf"), float("-inf")) + ): + violations.append("objective_value must be a finite JSON number") + + if not violations: + return None + return { + "feasible": False, + "violated_constraints": ["solution_schema"], + "violations": violations, + "violation_magnitudes": [], + } + + +def _frontieror_write_unexpected_rejection(error): + """Turn malformed candidate/checker exceptions into a normal rejection.""" + import json as _frontieror_json + import sys as _frontieror_sys + + result_path = None + for index, argument in enumerate(_frontieror_sys.argv): + if argument == "--result_path" and index + 1 < len(_frontieror_sys.argv): + result_path = _frontieror_sys.argv[index + 1] + break + if argument.startswith("--result_path="): + result_path = argument.split("=", 1)[1] + break + if not result_path: + return False + try: + with open(result_path, "w") as result_handle: + _frontieror_json.dump( + { + "feasible": False, + "violated_constraints": ["malformed_solution"], + "violations": [ + "checker rejected malformed candidate: " + + type(error).__name__ + + ": " + + str(error)[:500] + ], + "violation_magnitudes": [], + }, + result_handle, + indent=2, + ) + except Exception: + return False + return True + + + +def check_feasibility(instance, solution): + tol = 1e-5 + eps = 1e-5 + + violations = [] + violation_magnitudes = [] + violated_constraints = set() + + nI = instance["num_facilities"] + nJ = instance["num_customers"] + d = instance["demands"] + s = instance["capacities"] + f1 = instance["fixed_costs_obj1"] + f2 = instance["fixed_costs_obj2"] + c1 = instance["assignment_costs_obj1"] + c2 = instance["assignment_costs_obj2"] + + # Extract solutions with variable details + solutions = solution.get("solutions", []) + + if not solutions: + # No detailed solution data (e.g. efficient algorithm output with only + # pareto_front points). Cannot verify structural constraints. + return { + "feasible": False, + "violated_constraints": [], + "violations": [ + "No detailed solution variables available (open_facilities, " + "assignments missing); feasibility cannot be verified" + ], + "violation_magnitudes": [] + } + + for sol_idx, sol in enumerate(solutions): + open_fac = sol.get("open_facilities", []) + assignments = sol.get("assignments", {}) + + # Reconstruct y[i] and x[i][j] + y = [0] * nI + for i in open_fac: + if 0 <= i < nI: + y[i] = 1 + + x = [[0] * nJ for _ in range(nI)] + for j_str, i_val in assignments.items(): + j = int(j_str) + i = int(i_val) + if 0 <= i < nI and 0 <= j < nJ: + x[i][j] = 1 + + # ================================================================== + # Constraint 1: Ax <= b + # Encompasses all SSCFLP structural constraints. + # ================================================================== + + # (a) Single-source assignment: sum_i x[i][j] = 1 for all j (= constraint) + for j in range(nJ): + lhs = sum(x[i][j] for i in range(nI)) + rhs = 1.0 + violation_amount = abs(lhs - rhs) + if violation_amount > tol: + violated_constraints.add(1) + normalizer = max(abs(rhs), eps) + violations.append( + f"Solution {sol_idx}: Customer {j} assigned to " + f"{lhs} facilities (expected exactly 1)" + ) + violation_magnitudes.append({ + "constraint": 1, + "lhs": float(lhs), + "rhs": float(rhs), + "raw_excess": float(violation_amount), + "normalizer": float(normalizer), + "ratio": float(violation_amount / normalizer) + }) + + # (b) Capacity: sum_j d[j]*x[i][j] <= s[i]*y[i] for all i + for i in range(nI): + lhs = sum(d[j] * x[i][j] for j in range(nJ)) + rhs = float(s[i] * y[i]) + violation_amount = lhs - rhs # <= constraint: violation if lhs > rhs + if violation_amount > tol: + violated_constraints.add(1) + normalizer = max(abs(rhs), eps) + violations.append( + f"Solution {sol_idx}: Capacity exceeded at facility {i}: " + f"load {lhs} > capacity {rhs}" + ) + violation_magnitudes.append({ + "constraint": 1, + "lhs": float(lhs), + "rhs": float(rhs), + "raw_excess": float(violation_amount), + "normalizer": float(normalizer), + "ratio": float(violation_amount / normalizer) + }) + + # (c) Linking: x[i][j] <= y[i] for all i, j + for i in range(nI): + for j in range(nJ): + lhs = float(x[i][j]) + rhs = float(y[i]) + violation_amount = lhs - rhs # <= constraint + if violation_amount > tol: + violated_constraints.add(1) + normalizer = max(abs(rhs), eps) + violations.append( + f"Solution {sol_idx}: Customer {j} assigned to " + f"closed facility {i} (x[{i},{j}]={lhs}, y[{i}]={rhs})" + ) + violation_magnitudes.append({ + "constraint": 1, + "lhs": float(lhs), + "rhs": float(rhs), + "raw_excess": float(violation_amount), + "normalizer": float(normalizer), + "ratio": float(violation_amount / normalizer) + }) + + # ================================================================== + # Constraint 2: x in {0,1}^n (binary integrality) + # ================================================================== + # Since we reconstruct from open_facilities/assignments, values are + # inherently 0 or 1. Check that assignment targets are valid indices + # and that every customer is assigned (covered by constraint 1a above). + for j_str, i_val in assignments.items(): + j = int(j_str) + i = int(i_val) + if i < 0 or i >= nI: + violated_constraints.add(2) + rhs_val = float(nI - 1) + raw = float(abs(i) - rhs_val) if abs(i) > rhs_val else 0.0 + normalizer = max(abs(rhs_val), eps) + violations.append( + f"Solution {sol_idx}: Customer {j} assigned to " + f"invalid facility index {i} (valid: 0..{nI-1})" + ) + violation_magnitudes.append({ + "constraint": 2, + "lhs": float(i), + "rhs": rhs_val, + "raw_excess": raw, + "normalizer": normalizer, + "ratio": raw / normalizer + }) + if j < 0 or j >= nJ: + violated_constraints.add(2) + rhs_val = float(nJ - 1) + raw = float(abs(j) - rhs_val) if abs(j) > rhs_val else 0.0 + normalizer = max(abs(rhs_val), eps) + violations.append( + f"Solution {sol_idx}: Invalid customer index {j} " + f"(valid: 0..{nJ-1})" + ) + violation_magnitudes.append({ + "constraint": 2, + "lhs": float(j), + "rhs": rhs_val, + "raw_excess": raw, + "normalizer": normalizer, + "ratio": raw / normalizer + }) + + # Check that open_facilities indices are valid + for i in open_fac: + if i < 0 or i >= nI: + violated_constraints.add(2) + rhs_val = float(nI - 1) + raw = float(abs(i) - rhs_val) if abs(i) > rhs_val else 0.0 + normalizer = max(abs(rhs_val), eps) + violations.append( + f"Solution {sol_idx}: Invalid facility index {i} " + f"in open_facilities (valid: 0..{nI-1})" + ) + violation_magnitudes.append({ + "constraint": 2, + "lhs": float(i), + "rhs": rhs_val, + "raw_excess": raw, + "normalizer": normalizer, + "ratio": raw / normalizer + }) + + # ================================================================== + # Constraint 3: Objective consistency (Tier C defense against + # fabricated objective_value exploits). The reported objective_value + # is documented as the minimum over non-dominated solutions of + # (z1+z2)/2; recompute it from open_facilities and assignments and + # reject if it disagrees with what the program reported. + # ================================================================== + reported_obj_raw = solution.get("objective_value") + try: + reported_obj = float(reported_obj_raw) if reported_obj_raw is not None else None + except (TypeError, ValueError): + reported_obj = None + + if reported_obj is not None: + true_half_min = None + for sol_idx, sol in enumerate(solutions): + open_fac = sol.get("open_facilities", []) or [] + assignments = sol.get("assignments", {}) or {} + + # Recompute z1, z2 from variables using instance data. + z1_rec = 0 + z2_rec = 0 + for i in open_fac: + if 0 <= i < nI: + z1_rec += f1[i] + z2_rec += f2[i] + for j_str, i_val in assignments.items(): + try: + j = int(j_str) + i = int(i_val) + except (TypeError, ValueError): + continue + if 0 <= i < nI and 0 <= j < nJ: + z1_rec += c1[i][j] + z2_rec += c2[i][j] + + half = (z1_rec + z2_rec) / 2.0 + if true_half_min is None or half < true_half_min: + true_half_min = half + + if true_half_min is not None: + abs_diff = abs(reported_obj - true_half_min) + # Objectives are integer per the paper (outcome space Z^2), + # so (z1+z2)/2 lives on a half-integer grid. A 0.5 absolute + # floor plus 0.1% relative tolerance lets legitimate rounding + # through while catching any nontrivial fabrication. + obj_tol = max(0.5, 1e-3 * abs(true_half_min)) + if abs_diff > obj_tol: + violated_constraints.add(3) + normalizer = max(abs(true_half_min), eps) + violations.append( + f"Objective consistency violated: reported objective_value=" + f"{reported_obj} differs from recomputed " + f"min_s (z1_s+z2_s)/2 = {true_half_min} " + f"(|diff|={abs_diff:.3g}, tol={obj_tol:.3g})" + ) + violation_magnitudes.append({ + "constraint": 3, + "lhs": float(reported_obj), + "rhs": float(true_half_min), + "raw_excess": float(abs_diff), + "normalizer": float(normalizer), + "ratio": float(abs_diff / normalizer) + }) + + feasible = len(violated_constraints) == 0 + return { + "feasible": feasible, + "violated_constraints": sorted(violated_constraints), + "violations": violations, + "violation_magnitudes": violation_magnitudes + } + + +def main(): + parser = argparse.ArgumentParser( + description="Feasibility checker for BO-SSCFLP solutions" + ) + parser.add_argument("--instance_path", type=str, required=True, + help="Path to the JSON instance file") + parser.add_argument("--solution_path", type=str, required=True, + help="Path to the JSON solution file") + parser.add_argument("--result_path", type=str, required=True, + help="Path to write the JSON feasibility result") + args = parser.parse_args() + + with open(args.instance_path, "r") as f: + instance = json.load(f) + with open(args.solution_path, "r") as f: + solution = json.load(f) + _frontieror_contract_result = _frontieror_validate_solution_contract(solution) + if _frontieror_contract_result is not None: + with open(args.result_path, "w") as _frontieror_result_handle: + json.dump( + _frontieror_contract_result, + _frontieror_result_handle, + indent=2, + ) + return + + result = check_feasibility(instance, solution) + + with open(args.result_path, "w") as f: + json.dump(result, f, indent=2) + + if result["feasible"]: + print("FEASIBLE: No hard constraint violations detected.") + else: + print(f"INFEASIBLE: Violated constraints: {result['violated_constraints']}") + for v in result["violations"]: + print(f" - {v}") + + +if __name__ == "__main__": + try: + main() + except Exception as _frontieror_unexpected_error: + if not _frontieror_write_unexpected_rejection( + _frontieror_unexpected_error + ): + raise diff --git a/boland2019/gurobi_code.py b/boland2019/gurobi_code.py new file mode 100644 index 0000000000000000000000000000000000000000..741b91b208f81d6804760d2380e8daf25fee3267 --- /dev/null +++ b/boland2019/gurobi_code.py @@ -0,0 +1,348 @@ +""" +Bi-Objective Single-Source Capacitated Facility Location Problem (BO-SSCFLP) +solved via epsilon-constraint method using Gurobi. + +Reference: Boland, Charkhgard, Savelsbergh (2019) + +NOTE: The BO-SSCFLP formulation is **NOT SPECIFIED IN THE PAPER** (deferred to +an online supplement). The formulation below is inferred from standard SSCFLP +literature. + +Formulation +----------- +Sets: + I: facilities (i = 0..num_facilities-1) + J: customers (j = 0..num_customers-1) + +Parameters (from JSON instance): + f1[i], f2[i] : fixed costs for opening facility i (obj1, obj2) + c1[i][j], c2[i][j] : assignment cost of customer j to facility i (obj1, obj2) + d[j] : demand of customer j + s[i] : capacity of facility i + +Decision Variables: + y[i] in {0,1} : 1 if facility i is opened + x[i,j] in {0,1} : 1 if customer j is assigned to facility i + +Objectives (both minimize): + z1 = sum_i f1[i]*y[i] + sum_i sum_j c1[i][j]*x[i,j] + z2 = sum_i f2[i]*y[i] + sum_i sum_j c2[i][j]*x[i,j] + +Constraints: + sum_i x[i,j] = 1 for all j (single-source assignment) + sum_j d[j]*x[i,j] <= s[i]*y[i] for all i (capacity) + x[i,j] <= y[i] for all i,j (linking -- inferred assumption) + x[i,j], y[i] in {0,1} + +Algorithm: Epsilon-Constraint Method +------------------------------------- +1. Lexicographic min obj1 then obj2 -> upper-left point (z1_ul, z2_ul) +2. Lexicographic min obj2 then obj1 -> lower-right point (z1_lr, z2_lr) +3. Sweep eps from z2_ul - 1 down to z2_lr, constrain z2 <= eps, minimise z1. + Each new non-dominated point updates eps = z2_new - 1. +4. Collect all non-dominated outcome vectors. + +Since objectives are integer-valued (Z^2 outcome space), step by 1. +""" + +import argparse +import json +import time + +import gurobipy as gp +from gurobipy import GRB +import os as _os, sys as _sys +# Walk up from this file's directory to find repo root (containing scripts/). +_repo = _os.path.dirname(_os.path.abspath(__file__)) +while _repo != _os.path.dirname(_repo) and not _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _repo = _os.path.dirname(_repo) +if _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _sys.path.insert(0, _repo) +try: + from scripts.utils.gurobi_log_helper import install_gurobi_logger +except ImportError: + def install_gurobi_logger(log_path): # no-op fallback when scripts/ unavailable + pass + + +def load_instance(path): + with open(path, "r") as f: + return json.load(f) + + +def build_model(data): + """Build the base SSCFLP model (without objective or epsilon constraint).""" + nI = data["num_facilities"] + nJ = data["num_customers"] + + f1 = data["fixed_costs_obj1"] + f2 = data["fixed_costs_obj2"] + c1 = data["assignment_costs_obj1"] + c2 = data["assignment_costs_obj2"] + d = data["demands"] + s = data["capacities"] + + m = gp.Model("BO_SSCFLP") + m.setParam("Threads", 1) + m.Params.OutputFlag = 0 + + # Decision variables + y = m.addVars(nI, vtype=GRB.BINARY, name="y") + x = m.addVars(nI, nJ, vtype=GRB.BINARY, name="x") + + # Single-source assignment: each customer assigned to exactly one facility + for j in range(nJ): + m.addConstr(gp.quicksum(x[i, j] for i in range(nI)) == 1, + name=f"assign_{j}") + + # Capacity constraints + for i in range(nI): + m.addConstr(gp.quicksum(d[j] * x[i, j] for j in range(nJ)) <= s[i] * y[i], + name=f"cap_{i}") + + # Linking constraints: x[i,j] <= y[i] + # NOTE: These linking constraints are an **inferred assumption**, not + # explicitly stated in the paper (which defers formulation to online supplement). + # They are standard in SSCFLP formulations and are implied by the capacity + # constraints when demands are positive, but we include them explicitly + # to strengthen the LP relaxation. + for i in range(nI): + for j in range(nJ): + m.addConstr(x[i, j] <= y[i], name=f"link_{i}_{j}") + + # Build objective expressions + z1_expr = (gp.quicksum(f1[i] * y[i] for i in range(nI)) + + gp.quicksum(c1[i][j] * x[i, j] for i in range(nI) for j in range(nJ))) + z2_expr = (gp.quicksum(f2[i] * y[i] for i in range(nI)) + + gp.quicksum(c2[i][j] * x[i, j] for i in range(nI) for j in range(nJ))) + + m.update() + return m, y, x, z1_expr, z2_expr + + +def eval_objectives(y, x, data): + """Evaluate both objective values from the current solution.""" + nI = data["num_facilities"] + nJ = data["num_customers"] + f1 = data["fixed_costs_obj1"] + f2 = data["fixed_costs_obj2"] + c1 = data["assignment_costs_obj1"] + c2 = data["assignment_costs_obj2"] + + z1 = sum(f1[i] * round(y[i].X) for i in range(nI)) + z1 += sum(c1[i][j] * round(x[i, j].X) for i in range(nI) for j in range(nJ)) + + z2 = sum(f2[i] * round(y[i].X) for i in range(nI)) + z2 += sum(c2[i][j] * round(x[i, j].X) for i in range(nI) for j in range(nJ)) + + return int(round(z1)), int(round(z2)) + + +def extract_solution(y, x, data): + """Extract the current solution as a dict.""" + nI = data["num_facilities"] + nJ = data["num_customers"] + open_facilities = [i for i in range(nI) if round(y[i].X) > 0.5] + assignments = {} + for j in range(nJ): + for i in range(nI): + if round(x[i, j].X) > 0.5: + assignments[j] = i + break + return {"open_facilities": open_facilities, "assignments": assignments} + + +def solve_epsilon_constraint(data, time_limit): + """ + Enumerate all non-dominated points of the BO-SSCFLP using the + epsilon-constraint method. + """ + start_time = time.time() + pareto_front = [] + solutions = [] + + def remaining_time(): + return max(0, time_limit - (time.time() - start_time)) + + def time_expired(): + return time.time() - start_time >= time_limit + + # ------------------------------------------------------------------ + # Step 1: Lexicographic optimum min z1, then min z2 + # ------------------------------------------------------------------ + m, y, x, z1_expr, z2_expr = build_model(data) + m.Params.TimeLimit = remaining_time() + m.setObjective(z1_expr, GRB.MINIMIZE) + m.optimize() + + if m.Status not in (GRB.OPTIMAL, GRB.TIME_LIMIT) or m.SolCount == 0: + # No feasible solution found at all + return pareto_front, solutions + + z1_star = round(m.ObjVal) + + # Fix z1 = z1_star, minimize z2 + eps_constr_z1 = m.addConstr(z1_expr <= z1_star, name="fix_z1") + m.setObjective(z2_expr, GRB.MINIMIZE) + m.Params.TimeLimit = remaining_time() + m.optimize() + + if m.Status not in (GRB.OPTIMAL, GRB.TIME_LIMIT) or m.SolCount == 0: + # Return with just the first solution + m.remove(eps_constr_z1) + m.setObjective(z1_expr, GRB.MINIMIZE) + m.Params.TimeLimit = remaining_time() + m.optimize() + if m.SolCount > 0: + z1_val, z2_val = eval_objectives(y, x, data) + pareto_front.append([z1_val, z2_val]) + solutions.append(extract_solution(y, x, data)) + return pareto_front, solutions + + z1_ul, z2_ul = eval_objectives(y, x, data) + pareto_front.append([z1_ul, z2_ul]) + solutions.append(extract_solution(y, x, data)) + + m.remove(eps_constr_z1) + m.update() + + if time_expired(): + return pareto_front, solutions + + # ------------------------------------------------------------------ + # Step 2: Lexicographic optimum min z2, then min z1 + # ------------------------------------------------------------------ + m.setObjective(z2_expr, GRB.MINIMIZE) + m.Params.TimeLimit = remaining_time() + m.optimize() + + if m.Status not in (GRB.OPTIMAL, GRB.TIME_LIMIT) or m.SolCount == 0: + return pareto_front, solutions + + z2_star = round(m.ObjVal) + + eps_constr_z2 = m.addConstr(z2_expr <= z2_star, name="fix_z2") + m.setObjective(z1_expr, GRB.MINIMIZE) + m.Params.TimeLimit = remaining_time() + m.optimize() + + if m.Status not in (GRB.OPTIMAL, GRB.TIME_LIMIT) or m.SolCount == 0: + return pareto_front, solutions + + z1_lr, z2_lr = eval_objectives(y, x, data) + + # Only add if it is a genuinely different point + if [z1_lr, z2_lr] != pareto_front[-1]: + pareto_front.append([z1_lr, z2_lr]) + solutions.append(extract_solution(y, x, data)) + + m.remove(eps_constr_z2) + m.update() + + if time_expired(): + return pareto_front, solutions + + # ------------------------------------------------------------------ + # Step 3: Sweep epsilon from z2_ul - 1 down to z2_lr + # ------------------------------------------------------------------ + # We already have the two extreme points. Now find interior points. + eps = z2_ul - 1 + + # Add an epsilon constraint on z2 that we will update each iteration + eps_constr = m.addConstr(z2_expr <= eps, name="eps_z2") + m.setObjective(z1_expr, GRB.MINIMIZE) + + while eps >= z2_lr and not time_expired(): + # Update the epsilon constraint RHS + eps_constr.RHS = eps + + m.Params.TimeLimit = remaining_time() + m.optimize() + + if m.Status not in (GRB.OPTIMAL, GRB.TIME_LIMIT) or m.SolCount == 0: + # No feasible solution with z2 <= eps; we are done + break + + if m.Status == GRB.TIME_LIMIT and m.SolCount == 0: + break + + z1_new, z2_new = eval_objectives(y, x, data) + + # Record this non-dominated point (skip if already recorded) + if [z1_new, z2_new] not in pareto_front: + pareto_front.append([z1_new, z2_new]) + solutions.append(extract_solution(y, x, data)) + + # Next epsilon: just below the z2 value we found + eps = z2_new - 1 + + # Sort Pareto front by z1 (ascending), z2 (descending) for clean output + combined = list(zip(pareto_front, solutions)) + combined.sort(key=lambda p: (p[0][0], -p[0][1])) + pareto_front = [p for p, _ in combined] + solutions = [s for _, s in combined] + + return pareto_front, solutions + + +def main(): + parser = argparse.ArgumentParser( + description="Solve BO-SSCFLP via epsilon-constraint method (Gurobi)") + parser.add_argument("--instance_path", type=str, required=True, + help="Path to the JSON instance file") + parser.add_argument("--solution_path", type=str, + default="gurobi_solution_1.json", + help="Path to write the JSON solution file") + parser.add_argument("--time_limit", type=int, default=3600, + help="Total time limit in seconds across all solves") + parser.add_argument("--log_path", type=str, default=None, help="Path to log incumbent solutions") + args = parser.parse_args() + install_gurobi_logger(args.log_path) + + data = load_instance(args.instance_path) + start_time = time.time() + + pareto_front, solutions = solve_epsilon_constraint(data, args.time_limit) + + elapsed = time.time() - start_time + + # Compute the best weighted-sum objective (lambda = 0.5) across all + # non-dominated points, consistent with the paper's scalarisation. + if pareto_front: + best_ws = min(0.5 * z1 + 0.5 * z2 for z1, z2 in pareto_front) + best_idx = next( + idx for idx, (z1, z2) in enumerate(pareto_front) + if 0.5 * z1 + 0.5 * z2 == best_ws + ) + else: + best_ws = None + best_idx = None + + result = { + "objective_value": best_ws, + "num_nondominated_points": len(pareto_front), + "pareto_front": pareto_front, + "solutions": [ + { + "z1": pf[0], + "z2": pf[1], + "open_facilities": sol["open_facilities"], + "assignments": {str(k): v for k, v in sol["assignments"].items()}, + } + for pf, sol in zip(pareto_front, solutions) + ], + "best_weighted_sum_index": best_idx, + "elapsed_time": elapsed, + } + + with open(args.solution_path, "w") as f: + json.dump(result, f, indent=2) + + print(f"Found {len(pareto_front)} non-dominated points in {elapsed:.2f}s") + print(f"Best weighted-sum (0.5*z1 + 0.5*z2) = {best_ws}") + print(f"Pareto front: {pareto_front}") + print(f"Solution written to {args.solution_path}") + + +if __name__ == "__main__": + main() diff --git a/boland2019/gurobi_feasi_result/large_feasi_result_1.json b/boland2019/gurobi_feasi_result/large_feasi_result_1.json new file mode 100644 index 0000000000000000000000000000000000000000..e305b82c3d6732211d5033415df1a105476e9cf1 --- /dev/null +++ b/boland2019/gurobi_feasi_result/large_feasi_result_1.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3269392edcbb7720882e77caadb88b4a9cf2fda04cf482cebd23baace4a531fd +size 102 diff --git a/boland2019/gurobi_feasi_result/large_feasi_result_2.json b/boland2019/gurobi_feasi_result/large_feasi_result_2.json new file mode 100644 index 0000000000000000000000000000000000000000..e305b82c3d6732211d5033415df1a105476e9cf1 --- /dev/null +++ b/boland2019/gurobi_feasi_result/large_feasi_result_2.json @@ -0,0 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0000000000000000000000000000000000000000..ab8247cf7cd29c408cfd376e24f7ca664e2a128a --- /dev/null +++ b/boland2019/instance_schema.json @@ -0,0 +1,10 @@ +{ + "num_facilities": " Number of candidate facility locations that may be opened.", + "num_customers": " Number of customers that must each be served by exactly one open facility.", + "demands": " Quantity of goods or service required by each customer.", + "capacities": " Maximum total demand that each facility can handle if opened.", + "fixed_costs_obj1": " Fixed cost incurred for opening each facility under the first cost structure.", + "fixed_costs_obj2": " Fixed cost incurred for opening each facility under the second cost structure.", + "assignment_costs_obj1": " Cost of assigning each customer to each facility under the first cost structure.", + "assignment_costs_obj2": " Cost of assigning each customer to each facility under the second cost structure." +} diff --git a/boland2019/mathematical_formulation.md b/boland2019/mathematical_formulation.md new file mode 100644 index 0000000000000000000000000000000000000000..760c5d40d913cd2b79e6521dd5c02808911c5fc9 --- /dev/null +++ b/boland2019/mathematical_formulation.md @@ -0,0 +1,39 @@ +# Original Formulation: Bi-Objective Single-Source Capacitated Facility Location Problem (BO-SSCFLP) + +*Source: “Bi-objective branch-and-cut algorithms based on LP relaxation and bound sets,” Gadegaard, Nielsen, and Ehrgott, submitted to INFORMS Journal on Computing (2019).* + +## Sets and Parameters (Reconstruction) + +- $\mathcal{I}$: set of potential facilities (indexed by $i$). + +- $\mathcal{J}$: set of customers (indexed by $j$). + +- $f^1_i, f^2_i$: fixed cost of opening facility $i$ under objectives $1$ and $2$ (integer-valued). + +- $c^1_{ij}, c^2_{ij}$: assignment cost of customer $j$ to facility $i$ under objectives $1$ and $2$ (integer-valued). + +- $s_i$: capacity of facility $i$; $d_j$: demand of customer $j$. + +## Decision Variables (Reconstruction) + +- $y_i \in \{0,1\}$: $1$ if facility $i$ is opened. + +- $x_{ij} \in \{0,1\}$: $1$ if customer $j$ is single-sourced by facility $i$. + +## Objectives (Reconstruction – paper defers to online supplement) + +$$\begin{align} +\min \; z_1 &= \sum_{i \in \mathcal{I}} f^1_i\, y_i + \sum_{i \in \mathcal{I}} \sum_{j \in \mathcal{J}} c^1_{ij}\, x_{ij} \tag{1} \\ +\min \; z_2 &= \sum_{i \in \mathcal{I}} f^2_i\, y_i + \sum_{i \in \mathcal{I}} \sum_{j \in \mathcal{J}} c^2_{ij}\, x_{ij} \tag{2} +\end{align}$$ + +## Constraints (Reconstruction) + +$$\begin{align} +\sum_{i \in \mathcal{I}} x_{ij} &= 1, & j \in \mathcal{J} \tag{3} \\ +\sum_{j \in \mathcal{J}} d_j\, x_{ij} &\le s_i\, y_i, & i \in \mathcal{I} \tag{4} \\ +x_{ij} &\le y_i, & i \in \mathcal{I},\; j \in \mathcal{J} \tag{5} \\ +x_{ij} \in \{0,1\},\quad y_i &\in \{0,1\}, & i \in \mathcal{I},\; j \in \mathcal{J} \tag{6} +\end{align}$$ + +Equations (3) are the SOS1 single-sourcing assignment equalities, (4) are knapsack capacity constraints (an indicator-style linking with $y_i$), and (5) are the standard linking inequalities enforcing that a customer may be assigned only to an open facility. Both objectives map into $\mathbb{Z}$ when all cost coefficients are integer-valued, as assumed by the paper. diff --git a/boland2019/problem_description.txt b/boland2019/problem_description.txt new file mode 100644 index 0000000000000000000000000000000000000000..a78cbf9e329f231767830fff0426c690f316ffb1 --- /dev/null +++ b/boland2019/problem_description.txt @@ -0,0 +1,11 @@ +# Problem Description + +A logistics company operates a set of candidate facility locations and must serve a set of customers. The number of customers is twice the number of facilities. Each facility has a capacity limit, and each customer has a known demand. The company must decide which facilities to open and assign every customer to exactly one open facility, so that the total demand assigned to any facility does not exceed that facility's capacity. A customer can only be served by a facility that is open. + +Two competing cost structures are evaluated simultaneously. Under the first cost structure, opening a given facility incurs a fixed cost specific to that facility, and assigning a given customer to that facility incurs an assignment cost specific to that facility-customer pair. The first total cost is the sum of all fixed opening costs plus all assignment costs under this first structure. The second cost structure is defined identically in form but with an entirely independent set of fixed costs and assignment costs, yielding a second total cost. Both total costs are to be minimized simultaneously; among non-dominated solutions, improving one cost necessarily comes at the expense of the other. + +The input data specifies the number of facilities and the number of customers, a demand for each customer, a capacity for each facility, fixed opening costs for each facility under each of the two cost structures, and assignment costs for each facility-customer pair under each of the two cost structures. All costs are integers, and all demands and capacities are integers. + +A solution is called non-dominated if no other solution satisfying all the requirements achieves a value at least as good on both cost structures and strictly better on at least one. The goal is to identify the complete set of non-dominated cost pairs. In particular, using a weighted combination with equal weights of one-half on each cost structure, the reported single combined cost is one-half times the first total cost plus one-half times the second total cost, evaluated at the non-dominated solution that minimizes this weighted sum. + +Each customer must be assigned to exactly one facility. The total demand of all customers assigned to a given facility must not exceed that facility's capacity multiplied by whether the facility is open; if the facility is closed, no customer may be assigned to it. In addition, each individual customer assignment is linked to the opening status of the corresponding facility: a customer may be assigned to a facility only if that facility is open. All opening and assignment decisions are indivisible, meaning each facility is either fully open or fully closed, and each customer is either fully assigned to a given facility or not at all. diff --git a/boland2019/solution_logger.py b/boland2019/solution_logger.py new file mode 100644 index 0000000000000000000000000000000000000000..5393fa935ce71df9da95cee04fe5199077cb8461 --- /dev/null +++ b/boland2019/solution_logger.py @@ -0,0 +1,61 @@ +""" +Convergence logger for optimization algorithms. + +Records incumbent solutions with timestamps to a JSONL file. +This module is provided to LLM-generated programs — they only need to call +`log(objective_value)` whenever a better feasible solution is found. + +Usage in generated code: + from solution_logger import SolutionLogger + logger = SolutionLogger(log_path, sense="minimize") # or "maximize" + # ... inside algorithm loop: + logger.log(objective_value) +""" + +import json +import time + + +class SolutionLogger: + def __init__(self, log_path, sense="minimize"): + """ + Args: + log_path: Path to the JSONL output file. + sense: "minimize" or "maximize". + """ + self.log_path = log_path + self.sense = sense + self.start_time = time.time() + self.best_obj = None + self.min_interval = 0.1 # seconds, avoid excessive writes + + self._last_log_time = 0.0 + # Clear the file + with open(self.log_path, "w") as f: + pass + + def log(self, objective_value): + """Record a new incumbent if it improves on the best known.""" + if objective_value is None: + return + + # Check if this is an improvement + if self.best_obj is not None: + if self.sense == "minimize" and objective_value >= self.best_obj: + return + if self.sense == "maximize" and objective_value <= self.best_obj: + return + + elapsed = time.time() - self.start_time + + # Throttle writes + if self.best_obj is not None and elapsed - self._last_log_time < self.min_interval: + self.best_obj = objective_value + return + + self.best_obj = objective_value + self._last_log_time = elapsed + + with open(self.log_path, "a") as f: + f.write(json.dumps({"time": round(elapsed, 3), + "objective_value": objective_value}) + "\n") diff --git a/boland2019/solution_schema.json b/boland2019/solution_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..c9887bb12ad26171587ded42951d20e169857190 --- /dev/null +++ b/boland2019/solution_schema.json @@ -0,0 +1,12 @@ +{ + "objective_value": " Equal-weighted combined cost (one-half of the first total cost plus one-half of the second total cost) at the non-dominated solution that minimizes this weighted sum.", + "pareto_front": " Complete set of non-dominated cost pairs, each pair giving the first total cost and the second total cost of a non-dominated solution.", + "solutions": [ + { + "z1": " First total cost (sum of fixed opening costs and assignment costs under the first cost structure) of this non-dominated solution.", + "z2": " Second total cost (sum of fixed opening costs and assignment costs under the second cost structure) of this non-dominated solution.", + "open_facilities": " Indices of facilities that are opened in this non-dominated solution.", + "assignments": " Index of the open facility to which each customer is assigned in this non-dominated solution." + } + ] +} diff --git a/bollapragada2001/feasibility_check.py b/bollapragada2001/feasibility_check.py new file mode 100644 index 0000000000000000000000000000000000000000..76cd4299359b451351ad2d8638c9dc33cdd26059 --- /dev/null +++ b/bollapragada2001/feasibility_check.py @@ -0,0 +1,647 @@ +""" +Feasibility checker for the Discrete Truss Structure Design problem. + +Based on the MILP formulation (5) from: + Bollapragada, Ghattas, and Hooker (2001) + "Optimal Design of Truss Structures by Logic-Based Branch and Cut" + Operations Research, 49(1):42-51 + +Constraints checked (numbered top-to-bottom from formulation (5)): + 1. Equilibrium equations: sum_i b[i][j]*s[i][l] = p[j][l] for all j, l + 2. Compatibility equations: sum_j b[i][j]*d[j][l] = v[i][l] for all i, l + 3. Hooke's law (linearized): (E_i/h_i)*A_i*v[i][l] = s[i][l] for all i, l + 4. Exactly one discrete size per bar: A_i in {A_{i1},...,A_{iK}} + 5. Elongation bounds: v_i^L <= v[i][l] <= v_i^U for all i, l + (where v_i^L, v_i^U incorporate stress bounds as per paper) + 6. Displacement bounds: d_j^L <= d[j][l] <= d_j^U for all j, l + 7. Integrality / linking: bars in same linking group must have same area + 8. Objective consistency: reported objective_value must equal + c * sum_i h_i * A_i within tolerance (full recompute -- all variables + required by the obj formula are present in the solution). + +Note: The solution files store original-formulation variables (A_i, s_il, d_jl). + Elongation v[i][l] is derived from compatibility: v[i][l] = sum_j b[i][j]*d[j][l]. + Constraints 2 and 3 from the MILP are checked using these derived elongations + against Hooke's law with the chosen discrete area. +""" + +import argparse +import json +import math +import numpy as np + +# FRONTIEROR_RELEASE_CONTRACT_V2 +_FRONTIEROR_REQUIRED_SOLUTION_FIELDS = ('bar_areas', 'bar_forces', 'displacements', 'objective_value') +_FRONTIEROR_NONEMPTY_SOLUTION_FIELDS = () +_FRONTIEROR_DERIVED_FIELD_RULES = () +_FRONTIEROR_CONDITIONAL_FIELD_RULES = () + + +def _frontieror_validate_solution_contract(solution): + """Return a structured rejection when the public solution contract is absent.""" + violations = [] + if not isinstance(solution, dict): + violations.append("solution must be a JSON object") + else: + missing = [ + field + for field in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS + if field not in solution or solution[field] is None + ] + if missing: + violations.append( + "missing required solution field(s): " + ", ".join(missing) + ) + + for discriminator, expected_value, fields in _FRONTIEROR_CONDITIONAL_FIELD_RULES: + if solution.get(discriminator) != expected_value: + continue + conditional_missing = [ + field + for field in fields + if field not in solution or solution[field] is None + ] + if conditional_missing: + violations.append( + "missing conditionally required solution field(s): " + + ", ".join(conditional_missing) + ) + + empty = [ + field + for field in _FRONTIEROR_NONEMPTY_SOLUTION_FIELDS + if field in solution and solution[field] in (None, [], {}) + ] + if empty: + violations.append( + "empty required decision witness field(s): " + ", ".join(empty) + ) + + for target, operation, source, source_key in _FRONTIEROR_DERIVED_FIELD_RULES: + try: + if operation == "assignment_key_set": + reported_set = set(solution[target]) + expected_set = {entry[source_key] for entry in solution[source]} + if reported_set != expected_set: + violations.append( + f"{target}={sorted(reported_set)} does not match the " + f"decision-derived value {sorted(expected_set)}" + ) + continue + + reported = float(solution[target]) + if operation == "length": + expected = float(len(solution[source])) + elif operation == "mapping_value": + expected = float(solution[source][source_key]) + else: + raise ValueError("unknown derived-field operation") + except (KeyError, TypeError, ValueError, OverflowError): + violations.append( + f"cannot derive {target} from decision witness field {source}" + ) + continue + if ( + reported != reported + or expected != expected + or reported in (float("inf"), float("-inf")) + or expected in (float("inf"), float("-inf")) + or abs(reported - expected) > 1e-6 + ): + violations.append( + f"{target}={reported} does not match the decision-derived " + f"value {expected}" + ) + + if "objective_value" in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS: + objective = solution.get("objective_value") + if ( + isinstance(objective, bool) + or not isinstance(objective, (int, float)) + or objective != objective + or objective in (float("inf"), float("-inf")) + ): + violations.append("objective_value must be a finite JSON number") + + if not violations: + return None + return { + "feasible": False, + "violated_constraints": ["solution_schema"], + "violations": violations, + "violation_magnitudes": [], + } + + +def _frontieror_write_unexpected_rejection(error): + """Turn malformed candidate/checker exceptions into a normal rejection.""" + import json as _frontieror_json + import sys as _frontieror_sys + + result_path = None + for index, argument in enumerate(_frontieror_sys.argv): + if argument == "--result_path" and index + 1 < len(_frontieror_sys.argv): + result_path = _frontieror_sys.argv[index + 1] + break + if argument.startswith("--result_path="): + result_path = argument.split("=", 1)[1] + break + if not result_path: + return False + try: + with open(result_path, "w") as result_handle: + _frontieror_json.dump( + { + "feasible": False, + "violated_constraints": ["malformed_solution"], + "violations": [ + "checker rejected malformed candidate: " + + type(error).__name__ + + ": " + + str(error)[:500] + ], + "violation_magnitudes": [], + }, + result_handle, + indent=2, + ) + except Exception: + return False + return True + + + +def load_json(path): + with open(path, "r") as f: + return json.load(f) + + +def build_b_matrix(instance): + """Build direction cosine matrix b[i][j] consistent with the algorithm code.""" + bars = instance["bars"] + dofs = instance["degrees_of_freedom"] + nodes = {n["node_id"]: n for n in instance["nodes"]} + + num_bars = len(bars) + num_dofs = len(dofs) + + dof_map = {} + for dof in dofs: + dof_map[(dof["node"], dof["direction"])] = dof["dof_id"] - 1 + + b = np.zeros((num_bars, num_dofs)) + + dim = instance.get("dimension", 2) + directions = ["x", "y"] if dim == 2 else ["x", "y", "z"] + + for bar_idx, bar in enumerate(bars): + ni = bar["node_i"] + nj = bar["node_j"] + node_i = nodes[ni] + node_j = nodes[nj] + + dx = node_j["x"] - node_i["x"] + dy = node_j["y"] - node_i["y"] + dz = 0.0 + if dim == 3: + dz = node_j.get("z", 0.0) - node_i.get("z", 0.0) + + length = math.sqrt(dx * dx + dy * dy + dz * dz) + if length < 1e-12: + continue + + cos_vals = [dx / length, dy / length] + if dim == 3: + cos_vals.append(dz / length) + + for d_idx, direction in enumerate(directions): + if (ni, direction) in dof_map: + j = dof_map[(ni, direction)] + b[bar_idx, j] = -cos_vals[d_idx] + if (nj, direction) in dof_map: + j = dof_map[(nj, direction)] + b[bar_idx, j] = cos_vals[d_idx] + + return b + + +def extract_solution(instance, solution): + """ + Extract bar areas, displacements, and bar forces from solution, + handling both efficient_algorithm and gurobi solution formats. + """ + num_bars = instance["num_bars"] + num_dofs = instance["num_free_dofs"] + num_loads = instance["num_loading_conditions"] + + # Bar areas + areas = {} + for entry in solution["bar_areas"]: + areas[entry["bar_id"]] = entry["area"] + + # Displacements: d[j][l] (0-indexed j, 0-indexed l) + d = np.zeros((num_dofs, num_loads)) + disp_data = solution["displacements"] + + if isinstance(disp_data, list) and len(disp_data) > 0: + if isinstance(disp_data[0], dict): + # Gurobi format: list of {dof_id, load, value} + for entry in disp_data: + j = entry["dof_id"] - 1 + l = entry["load"] - 1 + d[j, l] = entry["value"] + elif isinstance(disp_data[0], list): + # Efficient algorithm format: list of lists, d[j] = [val_l1, val_l2, ...] + for j, vals in enumerate(disp_data): + for l, val in enumerate(vals): + d[j, l] = val + else: + # Single load, flat list of values per DOF + for j, val in enumerate(disp_data): + d[j, 0] = val + + # Bar forces: s[i][l] (0-indexed i, 0-indexed l) + s = np.zeros((num_bars, num_loads)) + force_data = solution["bar_forces"] + + if isinstance(force_data, list) and len(force_data) > 0: + if isinstance(force_data[0], dict): + # Gurobi format: list of {bar_id, load, force} + for entry in force_data: + i = entry["bar_id"] - 1 + l = entry["load"] - 1 + s[i, l] = entry["force"] + elif isinstance(force_data[0], list): + # Efficient algorithm format: list of lists, s[i] = [val_l1, val_l2, ...] + for i, vals in enumerate(force_data): + for l, val in enumerate(vals): + s[i, l] = val + else: + # Single load, flat list of values per bar + for i, val in enumerate(force_data): + s[i, 0] = val + + return areas, d, s + + +def check_feasibility(instance, solution): + """ + Check all hard constraints from formulation (5) of the paper. + Returns (feasible, violated_constraints, violations, violation_magnitudes). + """ + tol = 1e-5 + eps = 1e-5 + + num_bars = instance["num_bars"] + num_dofs = instance["num_free_dofs"] + num_loads = instance["num_loading_conditions"] + bars = instance["bars"] + discrete_areas = instance["discrete_areas"] + E = instance["material_properties"]["modulus_of_elasticity"] + cost_density = instance["material_properties"]["cost_density"] + + # Stress bounds per bar + stress_lb = [] + stress_ub = [] + if "bar_specific_stress_bounds" in instance: + for sb in instance["bar_specific_stress_bounds"]: + stress_lb.append(sb["lower"]) + stress_ub.append(sb["upper"]) + else: + sl = instance["stress_bounds"]["lower"] + su = instance["stress_bounds"]["upper"] + stress_lb = [sl] * num_bars + stress_ub = [su] * num_bars + + # Displacement bounds + d_lb_val = instance["displacement_bounds"]["lower"] + d_ub_val = instance["displacement_bounds"]["upper"] + + # Compute elongation bounds incorporating stress bounds + # v_i^L = (h_i / E_i) * sigma_i^L + # v_i^U = (h_i / E_i) * sigma_i^U + v_lb = np.zeros(num_bars) + v_ub = np.zeros(num_bars) + for i, bar in enumerate(bars): + h_i = bar["length"] + v_lb[i] = (h_i / E) * stress_lb[i] + v_ub[i] = (h_i / E) * stress_ub[i] + + # Build direction cosine matrix + b = build_b_matrix(instance) + + # Build load vector p[j][l] + p = np.zeros((num_dofs, num_loads)) + for load_idx, lc in enumerate(instance["loading_conditions"]): + for ld in lc["loads"]: + dof_idx = ld["dof_id"] - 1 + p[dof_idx, load_idx] = ld["force"] + + # Extract solution + area_map, d, s = extract_solution(instance, solution) + + # Ordered bar areas (0-indexed) + A = np.zeros(num_bars) + for i, bar in enumerate(bars): + A[i] = area_map[bar["bar_id"]] + + # Derive elongations from compatibility: v[i][l] = sum_j b[i][j] * d[j][l] + v = np.zeros((num_bars, num_loads)) + for i in range(num_bars): + for l in range(num_loads): + v[i, l] = sum(b[i, j] * d[j, l] for j in range(num_dofs)) + + # Linking groups + linking_groups = instance.get("linking_groups", []) + + violated_set = set() + violations = [] + violation_magnitudes = [] + + def record_violation(constraint_idx, message, lhs, rhs, violation_amount): + violated_set.add(constraint_idx) + violations.append(message) + normalizer = max(abs(rhs), eps) + ratio = violation_amount / normalizer + violation_magnitudes.append({ + "constraint": constraint_idx, + "lhs": float(lhs), + "rhs": float(rhs), + "raw_excess": float(violation_amount), + "normalizer": float(normalizer), + "ratio": float(ratio), + }) + + # ========================================================================= + # Constraint 1: Equilibrium equations + # sum_i b[i][j] * s[i][l] = p[j][l] for all j, l + # ========================================================================= + for j in range(num_dofs): + for l in range(num_loads): + lhs = sum(b[i, j] * s[i, l] for i in range(num_bars)) + rhs = p[j, l] + violation_amount = abs(lhs - rhs) + if violation_amount > tol: + dof_info = instance["degrees_of_freedom"][j] + record_violation( + 1, + f"Equilibrium violated at DOF {dof_info['dof_id']} " + f"(node {dof_info['node']}, dir {dof_info['direction']}), " + f"load {l+1}: LHS={lhs:.6f}, RHS={rhs:.6f}, " + f"diff={violation_amount:.6f}", + lhs, rhs, violation_amount, + ) + + # ========================================================================= + # Constraint 2: Compatibility equations + # sum_j b[i][j] * d[j][l] = v[i][l] for all i, l + # + # Since v[i][l] is derived from compatibility, this is satisfied by + # construction. However we still check Hooke's law (Constraint 3) which + # ties together A_i, v[i][l], and s[i][l]. + # + # We verify compatibility indirectly: the elongation used to check other + # constraints is computed directly from displacements, so compatibility + # is inherently satisfied. We include it for completeness by checking + # that the force s[i][l] is consistent with A_i * v[i][l] via Hooke's law. + # That check is Constraint 3. + # ========================================================================= + # Compatibility is satisfied by construction of v from d, so no separate + # violation is possible here. Constraint 2 is trivially satisfied. + + # ========================================================================= + # Constraint 3: Hooke's law + # (E_i / h_i) * A_i * v[i][l] = s[i][l] for all i, l + # + # IMPORTANT NOTE on the MILP formulation (5) vs original formulation (1): + # The MILP linearises Hooke's law using disaggregated elongation variables + # v_{ik,l}, yielding: (E_i/h_i) * sum_k A_{ik} * v_{ik,l} = s_{i,l} + # The solution files store only the aggregate A_i, s_{i,l}, and d_{j,l} + # (not the disaggregated v_{ik,l}), so we check the equivalent original + # nonlinear form: (E_i/h_i) * A_i * v_{i,l} = s_{i,l}. + # + # For near-zero bars (A_i = A_{i1} ≈ 0, representing effectively absent + # bars), MILP solver tolerances can cause small residual forces that are + # negligible relative to the overall force magnitudes but produce large + # absolute violations in Hooke's law. To avoid false positives we scale + # the tolerance by the maximum absolute force across all bars/loads. + # ========================================================================= + max_abs_force = max( + (abs(s[i, l]) for i in range(num_bars) for l in range(num_loads)), + default=1.0, + ) + hooke_tol = max(tol, 1e-4 * max_abs_force) + for i in range(num_bars): + h_i = bars[i]["length"] + for l in range(num_loads): + lhs = (E / h_i) * A[i] * v[i, l] + rhs = s[i, l] + violation_amount = abs(lhs - rhs) + if violation_amount > hooke_tol: + record_violation( + 3, + f"Hooke's law violated for bar {bars[i]['bar_id']}, " + f"load {l+1}: (E/h)*A*v={lhs:.6f}, s={rhs:.6f}, " + f"diff={violation_amount:.6f}", + lhs, rhs, violation_amount, + ) + + # ========================================================================= + # Constraint 4: Exactly one discrete size per bar + # A_i in {A_{i1}, ..., A_{iK}} for all i + # ========================================================================= + for i in range(num_bars): + area_val = A[i] + min_dist = min(abs(area_val - da) for da in discrete_areas) + if min_dist > tol: + # Find nearest for reporting + nearest = min(discrete_areas, key=lambda da: abs(da - area_val)) + violation_amount = min_dist + record_violation( + 4, + f"Bar {bars[i]['bar_id']} area {area_val:.6f} is not a " + f"discrete area (nearest: {nearest})", + area_val, nearest, violation_amount, + ) + + # ========================================================================= + # Constraint 5: Elongation bounds (incorporating stress bounds) + # v_i^L <= v[i][l] <= v_i^U for all i, l + # ========================================================================= + for i in range(num_bars): + for l in range(num_loads): + vil = v[i, l] + # Lower bound: v_i^L <= v[i][l] + if v_lb[i] - vil > tol: + violation_amount = v_lb[i] - vil + record_violation( + 5, + f"Elongation lower bound violated for bar " + f"{bars[i]['bar_id']}, load {l+1}: " + f"v={vil:.6f} < v_L={v_lb[i]:.6f}", + vil, v_lb[i], violation_amount, + ) + # Upper bound: v[i][l] <= v_i^U + if vil - v_ub[i] > tol: + violation_amount = vil - v_ub[i] + record_violation( + 5, + f"Elongation upper bound violated for bar " + f"{bars[i]['bar_id']}, load {l+1}: " + f"v={vil:.6f} > v_U={v_ub[i]:.6f}", + vil, v_ub[i], violation_amount, + ) + + # ========================================================================= + # Constraint 6: Displacement bounds + # d_j^L <= d[j][l] <= d_j^U for all j, l + # ========================================================================= + if d_lb_val is not None and d_ub_val is not None: + for j in range(num_dofs): + for l in range(num_loads): + djl = d[j, l] + dof_info = instance["degrees_of_freedom"][j] + # Lower bound + if d_lb_val - djl > tol: + violation_amount = d_lb_val - djl + record_violation( + 6, + f"Displacement lower bound violated at DOF " + f"{dof_info['dof_id']} (node {dof_info['node']}, " + f"dir {dof_info['direction']}), load {l+1}: " + f"d={djl:.6f} < d_L={d_lb_val:.6f}", + djl, d_lb_val, violation_amount, + ) + # Upper bound + if djl - d_ub_val > tol: + violation_amount = djl - d_ub_val + record_violation( + 6, + f"Displacement upper bound violated at DOF " + f"{dof_info['dof_id']} (node {dof_info['node']}, " + f"dir {dof_info['direction']}), load {l+1}: " + f"d={djl:.6f} > d_U={d_ub_val:.6f}", + djl, d_ub_val, violation_amount, + ) + + # ========================================================================= + # Constraint 7: Integrality / Linking constraints + # Bars in the same linking group must have the same area. + # ========================================================================= + for group in linking_groups: + bar_ids = group["bar_ids"] + if len(bar_ids) < 2: + continue + ref_area = area_map[bar_ids[0]] + for bid in bar_ids[1:]: + other_area = area_map[bid] + violation_amount = abs(ref_area - other_area) + if violation_amount > tol: + record_violation( + 7, + f"Linking group {group.get('group_id', '?')}: " + f"bar {bar_ids[0]} area={ref_area}, " + f"bar {bid} area={other_area} (should be equal)", + other_area, ref_area, violation_amount, + ) + + # ========================================================================= + # Constraint 8: Objective consistency (Tier C anti-exploit defense) + # reported objective_value must equal c * sum_i h_i * A_i within + # tolerance. Full recompute: every variable required by the objective + # formula (A_i, h_i, c) is available from the solution + instance, so + # we can recompute exactly rather than just lower-bound. + # ========================================================================= + reported_obj = solution.get("objective_value") + if reported_obj is not None: + try: + reported = float(reported_obj) + except (TypeError, ValueError): + reported = None + if reported is not None and math.isfinite(reported): + true_obj = float( + cost_density * sum(bars[i]["length"] * A[i] for i in range(num_bars)) + ) + abs_diff = abs(reported - true_obj) + # 0.1% relative tolerance, 1e-3 absolute floor + obj_tol = max(1e-3, 1e-3 * abs(true_obj)) + if abs_diff > obj_tol: + record_violation( + 8, + f"Objective consistency violated: reported objective_value=" + f"{reported} differs from recomputed c*sum_i(h_i*A_i)=" + f"{true_obj} (|diff|={abs_diff:.3g}, tol={obj_tol:.3g})", + reported, true_obj, abs_diff, + ) + elif reported is not None: + # Non-finite reported obj (inf/nan) -- always a violation since the + # truss objective is a finite positive sum of finite quantities. + true_obj = float( + cost_density * sum(bars[i]["length"] * A[i] for i in range(num_bars)) + ) + record_violation( + 8, + f"Objective consistency violated: reported objective_value=" + f"{reported} is not finite; recomputed c*sum_i(h_i*A_i)={true_obj}", + reported, true_obj, float("inf"), + ) + + feasible = len(violated_set) == 0 + violated_constraints = sorted(violated_set) + + return feasible, violated_constraints, violations, violation_magnitudes + + +def main(): + parser = argparse.ArgumentParser( + description="Feasibility checker for discrete truss design " + "(Bollapragada et al. 2001)" + ) + parser.add_argument("--instance_path", type=str, required=True, + help="Path to the JSON instance file") + parser.add_argument("--solution_path", type=str, required=True, + help="Path to the JSON solution file") + parser.add_argument("--result_path", type=str, required=True, + help="Path for the output feasibility result JSON") + args = parser.parse_args() + + instance = load_json(args.instance_path) + solution = load_json(args.solution_path) + _frontieror_contract_result = _frontieror_validate_solution_contract(solution) + if _frontieror_contract_result is not None: + with open(args.result_path, "w") as _frontieror_result_handle: + json.dump( + _frontieror_contract_result, + _frontieror_result_handle, + indent=2, + ) + return + + feasible, violated_constraints, violations, violation_magnitudes = \ + check_feasibility(instance, solution) + + result = { + "feasible": feasible, + "violated_constraints": violated_constraints, + "violations": violations, + "violation_magnitudes": violation_magnitudes, + } + + with open(args.result_path, "w") as f: + json.dump(result, f, indent=2) + + print(f"Feasibility: {'PASS' if feasible else 'FAIL'}") + if not feasible: + print(f"Violated constraints: {violated_constraints}") + for v in violations: + print(f" - {v}") + + +if __name__ == "__main__": + try: + main() + except Exception as _frontieror_unexpected_error: + if not _frontieror_write_unexpected_rejection( + _frontieror_unexpected_error + ): + raise diff --git a/bollapragada2001/gurobi_code.py b/bollapragada2001/gurobi_code.py new file mode 100644 index 0000000000000000000000000000000000000000..cf01c812d949d12ed3545ad114300144f2a69050 --- /dev/null +++ b/bollapragada2001/gurobi_code.py @@ -0,0 +1,375 @@ +""" +Gurobi MILP implementation for the Discrete Truss Structure Design Problem. + +Based on Formulation (5) from: + Bollapragada, Ghattas, and Hooker (2001) + "Optimal Design of Truss Structures by Logic-Based Branch and Cut" + Operations Research, 49(1):42-51 + +The MILP uses binary variables y_{ik} to select discrete cross-sectional areas, +disaggregated elongation variables v_{ikl}, and linearized Hooke's law. +""" + +import argparse +import json +import math +import sys + +import gurobipy as gp +from gurobipy import GRB +import os as _os, sys as _sys +# Walk up from this file's directory to find repo root (containing scripts/). +_repo = _os.path.dirname(_os.path.abspath(__file__)) +while _repo != _os.path.dirname(_repo) and not _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _repo = _os.path.dirname(_repo) +if _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _sys.path.insert(0, _repo) +try: + from scripts.utils.gurobi_log_helper import install_gurobi_logger +except ImportError: + def install_gurobi_logger(log_path): # no-op fallback when scripts/ unavailable + pass + + +def load_instance(path): + """Load a problem instance from a JSON file.""" + with open(path, "r") as f: + return json.load(f) + + +def build_b_matrix(instance): + """ + Build the direction cosine matrix b[i][j] where: + i = bar index (0-based) + j = DOF index (0-based) + + b[i][j] is the cosine of the angle between bar i and DOF j. + For a 2D problem, each bar connects two nodes. Each free node contributes + 2 DOFs (x, y). The direction cosine for a bar at one of its endpoints + equals the component of the bar's unit direction vector along that DOF's + direction, with sign depending on whether the node is the start or end node. + """ + bars = instance["bars"] + dofs = instance["degrees_of_freedom"] + nodes = {n["node_id"]: n for n in instance["nodes"]} + + num_bars = len(bars) + num_dofs = len(dofs) + + # Build a map from (node_id, direction) -> dof_index (0-based) + dof_map = {} + for dof in dofs: + dof_map[(dof["node"], dof["direction"])] = dof["dof_id"] - 1 + + b = [[0.0] * num_dofs for _ in range(num_bars)] + + dim = instance.get("dimension", 2) + directions = ["x", "y"] if dim == 2 else ["x", "y", "z"] + + for bar_idx, bar in enumerate(bars): + ni = bar["node_i"] + nj = bar["node_j"] + node_i = nodes[ni] + node_j = nodes[nj] + + # Compute unit direction vector from node_i to node_j + dx = node_j["x"] - node_i["x"] + dy = node_j["y"] - node_i["y"] + dz = 0.0 + if dim == 3: + dz = node_j.get("z", 0.0) - node_i.get("z", 0.0) + + length = math.sqrt(dx * dx + dy * dy + dz * dz) + if length < 1e-12: + continue + + # Unit direction vector components + cos_vals = [dx / length, dy / length] + if dim == 3: + cos_vals.append(dz / length) + + # For node_i (start node): contribution is +cos along each direction + # For node_j (end node): contribution is -cos along each direction + # Convention: bar force s_i positive = tension. + # The equilibrium eq is: sum_i b[i][j] * s[i][l] = p[j][l] + # For a bar going from node_i to node_j with unit vector e: + # At node_j: +e contributes to equilibrium + # At node_i: -e contributes to equilibrium + # The compatibility eq is: sum_j b[i][j] * d[j][l] = v[i][l] + # For consistency, b[i][j] for DOFs at node_j = +cos, at node_i = -cos + # This follows standard structural analysis sign conventions. + + for d_idx, direction in enumerate(directions): + # node_i DOFs (if free) + if (ni, direction) in dof_map: + j = dof_map[(ni, direction)] + b[bar_idx][j] = -cos_vals[d_idx] + # node_j DOFs (if free) + if (nj, direction) in dof_map: + j = dof_map[(nj, direction)] + b[bar_idx][j] = cos_vals[d_idx] + + return b + + +def solve(instance, time_limit): + """Build and solve the MILP formulation (5).""" + num_bars = instance["num_bars"] + num_dofs = instance["num_free_dofs"] + num_loads = instance["num_loading_conditions"] + + bars = instance["bars"] + dofs = instance["degrees_of_freedom"] + loads = instance["loading_conditions"] + + E = instance["material_properties"]["modulus_of_elasticity"] + c = instance["material_properties"]["cost_density"] + + areas = instance["discrete_areas"] + K = len(areas) + + # Build stress bounds per bar + stress_bounds = {} + if "bar_specific_stress_bounds" in instance: + for sb in instance["bar_specific_stress_bounds"]: + stress_bounds[sb["bar_id"]] = (sb["lower"], sb["upper"]) + else: + sl = instance["stress_bounds"]["lower"] + su = instance["stress_bounds"]["upper"] + for bar in bars: + stress_bounds[bar["bar_id"]] = (sl, su) + + # Displacement bounds + d_lb = instance["displacement_bounds"]["lower"] + d_ub = instance["displacement_bounds"]["upper"] + + # Build b matrix + b = build_b_matrix(instance) + + # Pre-compute elongation bounds per bar (incorporating stress bounds) + # v_i^L = max(v_i^L, (h_i / E_i) * sigma_i^L) + # v_i^U = min(v_i^U, (h_i / E_i) * sigma_i^U) + # Paper assumes no explicit elongation bounds are given beyond stress bounds, + # so we derive them from stress bounds. + v_lb = [] + v_ub = [] + for bar in bars: + h_i = bar["length"] + E_i = E # uniform modulus + sigma_L, sigma_U = stress_bounds[bar["bar_id"]] + # Elongation bounds from stress: v = (h/E) * sigma + vL = (h_i / E_i) * sigma_L + vU = (h_i / E_i) * sigma_U + v_lb.append(vL) + v_ub.append(vU) + + # Build load vectors p[j][l] + p = [[0.0] * num_loads for _ in range(num_dofs)] + for load_idx, lc in enumerate(loads): + for ld in lc["loads"]: + dof_idx = ld["dof_id"] - 1 + p[dof_idx][load_idx] = ld["force"] + + # ---- Build Gurobi Model ---- + model = gp.Model("TrussDesign_MILP") + model.setParam("Threads", 1) + model.setParam("TimeLimit", time_limit) + model.setParam("OutputFlag", 1) + + # Decision variables + # y[i][k] binary: 1 if bar i uses area k + y = {} + for i in range(num_bars): + for k in range(K): + y[i, k] = model.addVar(vtype=GRB.BINARY, name=f"y_{i}_{k}") + + # s[i][l]: force in bar i under load l (unrestricted) + s = {} + for i in range(num_bars): + for l in range(num_loads): + s[i, l] = model.addVar(lb=-GRB.INFINITY, name=f"s_{i}_{l}") + + # d[j][l]: displacement at DOF j under load l + d = {} + for j in range(num_dofs): + for l in range(num_loads): + lb_val = d_lb if d_lb is not None else -GRB.INFINITY + ub_val = d_ub if d_ub is not None else GRB.INFINITY + d[j, l] = model.addVar(lb=lb_val, ub=ub_val, name=f"d_{j}_{l}") + + # v[i][k][l]: disaggregated elongation variable + v = {} + for i in range(num_bars): + for k in range(K): + for l in range(num_loads): + v[i, k, l] = model.addVar(lb=-GRB.INFINITY, name=f"v_{i}_{k}_{l}") + + model.update() + + # ---- Objective: min sum_i c_i * h_i * sum_k A_{ik} * y_{ik} ---- + obj = gp.LinExpr() + for i in range(num_bars): + h_i = bars[i]["length"] + for k in range(K): + obj += c * h_i * areas[k] * y[i, k] + model.setObjective(obj, GRB.MINIMIZE) + + # ---- Constraints ---- + + # 1. Exactly one size per bar: sum_k y[i][k] = 1 + for i in range(num_bars): + model.addConstr( + gp.quicksum(y[i, k] for k in range(K)) == 1, + name=f"one_size_{i}" + ) + + # 2. Equilibrium: sum_i b[i][j] * s[i][l] = p[j][l] + for j in range(num_dofs): + for l in range(num_loads): + model.addConstr( + gp.quicksum(b[i][j] * s[i, l] for i in range(num_bars)) == p[j][l], + name=f"equil_{j}_{l}" + ) + + # 3. Compatibility: sum_j b[i][j] * d[j][l] = sum_k v[i][k][l] + for i in range(num_bars): + for l in range(num_loads): + model.addConstr( + gp.quicksum(b[i][j] * d[j, l] for j in range(num_dofs)) + == gp.quicksum(v[i, k, l] for k in range(K)), + name=f"compat_{i}_{l}" + ) + + # 4. Hooke's law (linearized): (E_i/h_i) * sum_k A_{ik} * v[i][k][l] = s[i][l] + for i in range(num_bars): + h_i = bars[i]["length"] + E_i = E + for l in range(num_loads): + model.addConstr( + (E_i / h_i) * gp.quicksum(areas[k] * v[i, k, l] for k in range(K)) + == s[i, l], + name=f"hooke_{i}_{l}" + ) + + # 5. Elongation bounds: v_i^L * y[i][k] <= v[i][k][l] <= v_i^U * y[i][k] + for i in range(num_bars): + for k in range(K): + for l in range(num_loads): + model.addConstr( + v[i, k, l] >= v_lb[i] * y[i, k], + name=f"vlo_{i}_{k}_{l}" + ) + model.addConstr( + v[i, k, l] <= v_ub[i] * y[i, k], + name=f"vhi_{i}_{k}_{l}" + ) + + # 6. Linking groups: bars in the same group must have the same y variables + if instance.get("linking_groups"): + for group in instance["linking_groups"]: + bar_ids = group["bar_ids"] + ref_bar = bar_ids[0] - 1 # 0-based + for bid in bar_ids[1:]: + bi = bid - 1 # 0-based + for k in range(K): + model.addConstr( + y[bi, k] == y[ref_bar, k], + name=f"link_{ref_bar}_{bi}_{k}" + ) + + # Solve + model.optimize() + + # Extract solution + result = {"solver": "Gurobi_MILP", "status": "unknown"} + + if model.SolCount > 0: + result["objective_value"] = model.ObjVal + result["status"] = "optimal" if model.Status == GRB.OPTIMAL else "feasible" + result["mip_gap"] = model.MIPGap + + # Extract bar areas + bar_areas = [] + for i in range(num_bars): + for k in range(K): + if y[i, k].X > 0.5: + bar_areas.append({ + "bar_id": bars[i]["bar_id"], + "area": areas[k], + "area_index": k + }) + break + result["bar_areas"] = bar_areas + + # Extract displacements + displacements = [] + for j in range(num_dofs): + for l in range(num_loads): + displacements.append({ + "dof_id": dofs[j]["dof_id"], + "load": l + 1, + "value": d[j, l].X + }) + result["displacements"] = displacements + + # Extract bar forces + bar_forces = [] + for i in range(num_bars): + for l in range(num_loads): + bar_forces.append({ + "bar_id": bars[i]["bar_id"], + "load": l + 1, + "force": s[i, l].X + }) + result["bar_forces"] = bar_forces + else: + result["objective_value"] = None + result["status"] = "infeasible" + + result["solve_time"] = model.Runtime + result["num_vars"] = model.NumVars + result["num_constrs"] = model.NumConstrs + + return result + + +def main(): + parser = argparse.ArgumentParser( + description="Gurobi MILP solver for discrete truss design (Bollapragada et al. 2001)" + ) + parser.add_argument("--instance_path", type=str, required=True, + help="Path to the JSON instance file") + parser.add_argument("--solution_path", type=str, required=True, + help="Path for the output solution JSON file") + parser.add_argument("--time_limit", type=int, required=True, + help="Maximum solver runtime in seconds") + parser.add_argument("--log_path", type=str, default=None, help="Path to log incumbent solutions") + args = parser.parse_args() + install_gurobi_logger(args.log_path) + + instance = load_instance(args.instance_path) + print(f"Loaded instance: {instance['problem_name']}") + print(f" Bars: {instance['num_bars']}, DOFs: {instance['num_free_dofs']}, " + f"Loads: {instance['num_loading_conditions']}") + print(f" Discrete areas: {instance['discrete_areas']}") + print(f" Time limit: {args.time_limit}s") + + result = solve(instance, args.time_limit) + + print(f"\nResult: status={result['status']}, objective={result['objective_value']}") + if "mip_gap" in result: + print(f" MIP gap: {result['mip_gap']:.6f}") + print(f" Solve time: {result['solve_time']:.2f}s") + + if result.get("bar_areas"): + print("\nBar areas:") + for ba in result["bar_areas"]: + print(f" Bar {ba['bar_id']}: area = {ba['area']}") + + with open(args.solution_path, "w") as f: + json.dump(result, f, indent=2) + print(f"\nSolution written to {args.solution_path}") + + +if __name__ == "__main__": + main() diff --git a/bollapragada2001/gurobi_feasi_result/large_feasi_result_1.json b/bollapragada2001/gurobi_feasi_result/large_feasi_result_1.json new file mode 100644 index 0000000000000000000000000000000000000000..e305b82c3d6732211d5033415df1a105476e9cf1 --- /dev/null +++ b/bollapragada2001/gurobi_feasi_result/large_feasi_result_1.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3269392edcbb7720882e77caadb88b4a9cf2fda04cf482cebd23baace4a531fd +size 102 diff --git a/bollapragada2001/gurobi_feasi_result/large_feasi_result_2.json b/bollapragada2001/gurobi_feasi_result/large_feasi_result_2.json new file mode 100644 index 0000000000000000000000000000000000000000..e305b82c3d6732211d5033415df1a105476e9cf1 --- /dev/null +++ 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a/bollapragada2001/instance/tiny_instance.json b/bollapragada2001/instance/tiny_instance.json new file mode 100644 index 0000000000000000000000000000000000000000..f2ab1363a3774effe885afbb38d3f687f826db6b --- /dev/null +++ b/bollapragada2001/instance/tiny_instance.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:10731986fd38f2e8702e12bcce5b9ed6515819ea5918e1b37c4c8d25349f136a +size 5196 diff --git a/bollapragada2001/instance_schema.json b/bollapragada2001/instance_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..a208ff13b3c73c9c94ac8411636ea6a806fdd953 --- /dev/null +++ b/bollapragada2001/instance_schema.json @@ -0,0 +1,71 @@ +{ + "structure_type": " Identifier for the truss topology used in this instance.", + "dimension": " Number of spatial dimensions of the truss (2 for planar, 3 for space trusses).", + "num_bars": " Total number of bars in the truss structure.", + "num_nodes": " Total number of nodes in the truss structure, including supports.", + "num_loading_conditions": " Number of independent external loading conditions the structure must withstand.", + "num_free_dofs": " Number of unconstrained nodal displacement directions across all free nodes.", + "num_linking_groups": " Number of groups of symmetrically located bars that must share the same cross-sectional area.", + "linking_groups": " Groups of bars required to receive identical cross-sectional areas due to structural symmetry.", + "nodes": [ + { + "node_id": " Unique identifier for this node.", + "x": " Horizontal coordinate of the node.", + "y": " Vertical coordinate of the node.", + "is_support": " Whether this node is a fixed support with no freedom of movement.", + "fixed_dofs": " Coordinate directions in which this support node is fixed (present only for support nodes)." + } + ], + "bars": [ + { + "bar_id": " Unique identifier for this bar.", + "node_i": " Node at the start end of the bar.", + "node_j": " Node at the finish end of the bar.", + "length": " Physical length of the bar computed from node coordinates.", + "direction_cosines": " Cosines of the angle between the bar's orientation and each coordinate axis." + } + ], + "degrees_of_freedom": [ + { + "dof_id": " Unique identifier for this degree of freedom.", + "node": " Node to which this degree of freedom belongs.", + "direction": " Coordinate direction of this degree of freedom." + } + ], + "material_properties": { + "modulus_of_elasticity": " Young's modulus of the bar material, relating stress to strain.", + "cost_density": " Cost per unit volume used to compute the total structural cost." + }, + "discrete_areas": " Candidate cross-sectional areas from which exactly one must be chosen for each bar.", + "num_discrete_areas": " Number of candidate cross-sectional areas available for selection.", + "stress_bounds": { + "lower": " Default minimum allowable stress in any bar (negative denotes compression).", + "upper": " Default maximum allowable stress in any bar (positive denotes tension)." + }, + "bar_specific_stress_bounds": [ + { + "bar_id": " Bar to which these stress limits apply.", + "lower": " Minimum allowable stress for this bar.", + "upper": " Maximum allowable stress for this bar." + } + ], + "displacement_bounds": { + "lower": " Minimum allowable nodal displacement at any free degree of freedom, or null if unbounded.", + "upper": " Maximum allowable nodal displacement at any free degree of freedom, or null if unbounded." + }, + "loading_conditions": [ + { + "load_id": " Unique identifier for this loading condition.", + "description": " Human-readable summary of the applied loads.", + "loads": [ + { + "node": " Node at which this external force is applied.", + "direction": " Coordinate direction of the applied force.", + "dof_id": " Degree of freedom at which the force acts.", + "force": " Magnitude and sign of the applied external force." + } + ] + } + ], + "expected_optimal_mass": " Known best objective value for this instance from the literature." +} diff --git a/bollapragada2001/mathematical_formulation.md b/bollapragada2001/mathematical_formulation.md new file mode 100644 index 0000000000000000000000000000000000000000..f60c9baeb9be4556f6603c822bcc24f92432c361 --- /dev/null +++ b/bollapragada2001/mathematical_formulation.md @@ -0,0 +1,68 @@ +# Original Formulation: Optimal Design of Truss Structures (Truss Design Problem) + +*Source: Optimal Design of Truss Structures by Logic-Based Branch and Cut, S. Bollapragada, O. Ghattas, and J. N. Hooker, 2001 (Operations Research 49(1):42–51).* + +## Sets and Indices + +$$\begin{align*} +& i = 1, \dots, I && \text{bars} \\ +& j = 1, \dots, J && \text{degrees of freedom (summed over all nodes)} \\ +& \ell = 1, \dots, L && \text{loading conditions} \\ +& k = 1, \dots, K_i && \text{discrete cross-sectional areas available for bar } i +\end{align*}$$ + +## Parameters + +$$\begin{align*} +& I && \text{number of bars} \\ +& J && \text{number of degrees of freedom (summed over all nodes)} \\ +& L && \text{number of loading conditions} \\ +& K_i && \text{number of discrete cross-sectional areas for bar } i \\ +& h_i && \text{length of bar } i \\ +& A_{ik} && k\text{-th discrete cross-sectional area of bar } i,\ \text{with } 0 \leqslant A_{i1} \leqslant \cdots \leqslant A_{iK_i} \\ +& E_i && \text{modulus of elasticity of bar } i \\ +& p_{j\ell} && \text{force imposed by load condition } \ell \text{ at degree of freedom } j \\ +& b_{ij} && \text{cosine of the angle between bar } i \text{ and degree of freedom } j \\ +& c_i && \text{cost per unit volume of bar } i \text{ (typically the weight density)} \\ +& \sigma_i^L,\ \sigma_i^U && \text{minimum and maximum allowable stress in bar } i \\ +& v_i^L,\ v_i^U && \text{limits on elongation (contraction if negative) of bar } i \\ +& d_j^L,\ d_j^U && \text{limits on displacement for degree of freedom } j +\end{align*}$$ + +## Decision Variables + +$$\begin{align*} +& A_i && \text{cross-sectional area of bar } i \\ +& s_{i\ell} && \text{force in bar } i \text{ due to loading condition } \ell \\ +& \sigma_{i\ell}&& \text{stress in bar } i \text{ due to loading condition } \ell \\ +& v_{i\ell} && \text{elongation (contraction if negative) of bar } i \text{ due to loading condition } \ell \\ +& d_{j\ell} && \text{node displacement along degree of freedom } j \text{ for loading condition } \ell +\end{align*}$$ + +## Objective + +$$\begin{align} +\min \quad & \sum_{i=1}^{I} c_i h_i A_i \tag{1} +\end{align}$$ + +## Constraints + +$$\begin{align} +\text{s.t.} \quad +& \sum_{i=1}^{I} b_{ij}\, s_{i\ell} = p_{j\ell}, && \forall\, j,\, \ell + && \text{(equilibrium equations)} \\ +& \sum_{j=1}^{J} b_{ij}\, d_{j\ell} = v_{i\ell}, && \forall\, i,\, \ell + && \text{(compatibility equations)} \\ +& \frac{E_i}{h_i}\, A_i\, v_{i\ell} = s_{i\ell}, && \forall\, i,\, \ell + && \text{(Hooke's law)} \\ +& \sigma_{i\ell} = \frac{E_i}{h_i}\, v_{i\ell}, && \forall\, i,\, \ell + && \text{(stress equations)} \\ +& v_i^L \leqslant v_{i\ell} \leqslant v_i^U, && \forall\, i,\, \ell + && \text{(elongation bounds)} \\ +& \sigma_i^L \leqslant \sigma_{i\ell} \leqslant \sigma_i^U, && \forall\, i,\, \ell + && \text{(stress bounds)} \\ +& d_j^L \leqslant d_{j\ell} \leqslant d_j^U, && \forall\, j,\, \ell + && \text{(displacement bounds)} \\ +& \bigvee_{k=1}^{K_i} \left( A_i = A_{ik} \right), && \forall\, i + && \text{(logical disjunction)} \tag{1} +\end{align}$$ diff --git a/bollapragada2001/problem_description.txt b/bollapragada2001/problem_description.txt new file mode 100644 index 0000000000000000000000000000000000000000..f17392a3502b870f63e2bfca6079637f0de01485 --- /dev/null +++ b/bollapragada2001/problem_description.txt @@ -0,0 +1,11 @@ +# Problem Description + +A truss structure consists of a network of nodes connected by bars (structural members). The positions of the nodes and the topology of the network (which pairs of nodes are connected by bars) are given. Some nodes are fixed supports with no freedom of movement, while the remaining free nodes can displace along each coordinate direction, giving rise to a set of degrees of freedom across the structure. The structure must withstand one or more independent loading conditions, each of which specifies external forces applied at certain degrees of freedom. + +The input data for each problem instance includes the number of bars, the number of free degrees of freedom, and the number of loading conditions. For every bar, the data specifies its length, the modulus of elasticity of its material, and the direction cosines that relate the bar's orientation to each degree of freedom. Each bar also has a cost per unit volume (typically its weight density) and a list of discrete cross-sectional areas from which exactly one must be chosen; the smallest available area is a near-zero value (such as 0.01) representing the bar being effectively absent from the structure. For every loading condition, the data gives the external force applied at each degree of freedom. The data further specifies, for each bar, lower and upper bounds on allowable stress, and lower and upper bounds on allowable elongation (where negative elongation denotes contraction). For each degree of freedom, the data may specify lower and upper bounds on nodal displacement. In some instances, certain groups of symmetrically located bars are required to share the same cross-sectional area, and the data identifies which bars belong to each such linking group. + +The task is to select a discrete cross-sectional area for each bar and to determine, for each loading condition, the internal force in every bar, the elongation of every bar, and the displacement at every free degree of freedom. Exactly one discrete area must be chosen per bar, and if bars belong to a linking group they must all receive the same area selection. + +The choices must satisfy several physical laws and engineering limits. First, equilibrium must hold at every degree of freedom under every loading condition: the sum over all bars of the product of a bar's direction cosine for that degree of freedom and the bar's internal force must equal the external load applied at that degree of freedom. Second, compatibility must hold for every bar under every loading condition: the sum over all degrees of freedom of the product of the bar's direction cosine for that degree of freedom and the corresponding nodal displacement must equal the bar's elongation. Third, Hooke's law must be satisfied for every bar under every loading condition: the product of the bar's modulus of elasticity divided by its length, the bar's chosen cross-sectional area, and its elongation must equal the bar's internal force. The elongation of every bar under every loading condition must lie within its allowable elongation bounds, and the induced stress in the bar, equal to the modulus of elasticity divided by the bar length times the elongation, must lie within its allowable stress bounds. Nodal displacements at each degree of freedom under each loading condition must lie within their specified bounds when such bounds are provided. + +The goal is to minimize the total cost of the structure, computed as the sum over all bars of the product of the bar's cost per unit volume, its length, and its chosen cross-sectional area. diff --git a/bollapragada2001/solution_logger.py b/bollapragada2001/solution_logger.py new file mode 100644 index 0000000000000000000000000000000000000000..5393fa935ce71df9da95cee04fe5199077cb8461 --- /dev/null +++ b/bollapragada2001/solution_logger.py @@ -0,0 +1,61 @@ +""" +Convergence logger for optimization algorithms. + +Records incumbent solutions with timestamps to a JSONL file. +This module is provided to LLM-generated programs — they only need to call +`log(objective_value)` whenever a better feasible solution is found. + +Usage in generated code: + from solution_logger import SolutionLogger + logger = SolutionLogger(log_path, sense="minimize") # or "maximize" + # ... inside algorithm loop: + logger.log(objective_value) +""" + +import json +import time + + +class SolutionLogger: + def __init__(self, log_path, sense="minimize"): + """ + Args: + log_path: Path to the JSONL output file. + sense: "minimize" or "maximize". + """ + self.log_path = log_path + self.sense = sense + self.start_time = time.time() + self.best_obj = None + self.min_interval = 0.1 # seconds, avoid excessive writes + + self._last_log_time = 0.0 + # Clear the file + with open(self.log_path, "w") as f: + pass + + def log(self, objective_value): + """Record a new incumbent if it improves on the best known.""" + if objective_value is None: + return + + # Check if this is an improvement + if self.best_obj is not None: + if self.sense == "minimize" and objective_value >= self.best_obj: + return + if self.sense == "maximize" and objective_value <= self.best_obj: + return + + elapsed = time.time() - self.start_time + + # Throttle writes + if self.best_obj is not None and elapsed - self._last_log_time < self.min_interval: + self.best_obj = objective_value + return + + self.best_obj = objective_value + self._last_log_time = elapsed + + with open(self.log_path, "a") as f: + f.write(json.dumps({"time": round(elapsed, 3), + "objective_value": objective_value}) + "\n") diff --git a/bollapragada2001/solution_schema.json b/bollapragada2001/solution_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..85ea6a3c5cf39eb13cc93e3adb02bb17deb1caab --- /dev/null +++ b/bollapragada2001/solution_schema.json @@ -0,0 +1,24 @@ +{ + "objective_value": " Total cost of the truss structure, summing cost density times length times chosen area for every bar.", + "bar_areas": [ + { + "bar_id": " Bar to which this area assignment applies.", + "area": " Cross-sectional area selected for this bar from the discrete candidate set.", + "area_index": " Zero-based position of the selected area within the discrete candidate list." + } + ], + "displacements": [ + { + "dof_id": " Degree of freedom at which this displacement occurs.", + "load": " Loading condition under which this displacement occurs.", + "value": " Nodal displacement at this degree of freedom under this loading condition." + } + ], + "bar_forces": [ + { + "bar_id": " Bar in which this internal force acts.", + "load": " Loading condition under which this force occurs.", + "force": " Internal axial force in this bar under this loading condition (positive is tension, negative is compression)." + } + ] +} diff --git a/borndorfer2007/feasibility_check.py b/borndorfer2007/feasibility_check.py new file mode 100644 index 0000000000000000000000000000000000000000..2d2eb6bf6ed66cba993e1db2371737f149ea6c69 --- /dev/null +++ b/borndorfer2007/feasibility_check.py @@ -0,0 +1,958 @@ +#!/usr/bin/env python3 +""" +Feasibility checker for the Line Planning Problem (LPP). + +Paper: "A Column-Generation Approach to Line Planning in Public Transport" +Authors: Borndoerfer, Groetschel, Pfetsch (2007), Transportation Science 41(1), pp. 123-132. + +Constraints (numbered top-to-bottom from the mathematical formulation): + (1) y(P_st) = d_st for all (s,t) in D + (2) y(P_a) - sum_{l: e(a) in l} kappa_l f_l <= 0 for all a in A + (3) f(L_e) <= Lambda_e for all e in E + (4) f_l <= F x_l for all l in L + (5) x_l in {0, 1} for all l in L + (6) f_l >= 0 for all l in L + (7) y_p >= 0 for all p in P + (8) [Tier C defense] objective_value reported by the program must be + consistent with the variables in the solution. Specifically: + obj_true = lambda * (sum_l C_l + sum_l f_l * sum_{e in l} c_e) + + (1 - lambda) * sum_a tau_a * y_a + The first term (line costs) is exactly recomputable from active_lines. + The second term (passenger travel time) is recomputed exactly when the + solution provides either `arc_total_flow` or `active_passenger_paths`. + When neither is provided (e.g. the Gurobi reference, which writes only + the line plan), we bracket the travel time with + lower = sum_{(s,t)} d_st * shortest_path_tt(s,t) (full graph, no cap) + upper = sum_a cap_a * tau_a (every arc full) + and require the reported objective to lie in [obj_lb, obj_ub] within + tolerance. + +Since the solution files store only the line plan (active lines with edges and +frequencies) but not individual passenger path flows, constraints (1), (2), and (7) +are verified by solving a feasibility LP that checks whether a valid passenger +routing exists given the line capacities. +""" + +import argparse +import heapq +import json +import sys +from collections import defaultdict + +# FRONTIEROR_RELEASE_CONTRACT_V2 +_FRONTIEROR_REQUIRED_SOLUTION_FIELDS = ( + 'active_lines', + 'active_passenger_paths', + 'objective_value', +) +_FRONTIEROR_NONEMPTY_SOLUTION_FIELDS = ( + 'active_lines', + 'active_passenger_paths', +) +_FRONTIEROR_DERIVED_FIELD_RULES = () +_FRONTIEROR_CONDITIONAL_FIELD_RULES = () + + +def _frontieror_validate_solution_contract(solution): + """Return a structured rejection when the public solution contract is absent.""" + violations = [] + if not isinstance(solution, dict): + violations.append("solution must be a JSON object") + else: + missing = [ + field + for field in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS + if field not in solution or solution[field] is None + ] + if missing: + violations.append( + "missing required solution field(s): " + ", ".join(missing) + ) + + for discriminator, expected_value, fields in _FRONTIEROR_CONDITIONAL_FIELD_RULES: + if solution.get(discriminator) != expected_value: + continue + conditional_missing = [ + field + for field in fields + if field not in solution or solution[field] is None + ] + if conditional_missing: + violations.append( + "missing conditionally required solution field(s): " + + ", ".join(conditional_missing) + ) + + empty = [ + field + for field in _FRONTIEROR_NONEMPTY_SOLUTION_FIELDS + if field in solution and solution[field] in (None, [], {}) + ] + if empty: + violations.append( + "empty required decision witness field(s): " + ", ".join(empty) + ) + + for target, operation, source, source_key in _FRONTIEROR_DERIVED_FIELD_RULES: + try: + if operation == "assignment_key_set": + reported_set = set(solution[target]) + expected_set = {entry[source_key] for entry in solution[source]} + if reported_set != expected_set: + violations.append( + f"{target}={sorted(reported_set)} does not match the " + f"decision-derived value {sorted(expected_set)}" + ) + continue + + reported = float(solution[target]) + if operation == "length": + expected = float(len(solution[source])) + elif operation == "mapping_value": + expected = float(solution[source][source_key]) + else: + raise ValueError("unknown derived-field operation") + except (KeyError, TypeError, ValueError, OverflowError): + violations.append( + f"cannot derive {target} from decision witness field {source}" + ) + continue + if ( + reported != reported + or expected != expected + or reported in (float("inf"), float("-inf")) + or expected in (float("inf"), float("-inf")) + or abs(reported - expected) > 1e-6 + ): + violations.append( + f"{target}={reported} does not match the decision-derived " + f"value {expected}" + ) + + if "objective_value" in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS: + objective = solution.get("objective_value") + if ( + isinstance(objective, bool) + or not isinstance(objective, (int, float)) + or objective != objective + or objective in (float("inf"), float("-inf")) + ): + violations.append("objective_value must be a finite JSON number") + + if not violations: + return None + return { + "feasible": False, + "violated_constraints": ["solution_schema"], + "violations": violations, + "violation_magnitudes": [], + } + + +def _frontieror_write_unexpected_rejection(error): + """Turn malformed candidate/checker exceptions into a normal rejection.""" + import json as _frontieror_json + import sys as _frontieror_sys + + result_path = None + for index, argument in enumerate(_frontieror_sys.argv): + if argument == "--result_path" and index + 1 < len(_frontieror_sys.argv): + result_path = _frontieror_sys.argv[index + 1] + break + if argument.startswith("--result_path="): + result_path = argument.split("=", 1)[1] + break + if not result_path: + return False + try: + with open(result_path, "w") as result_handle: + _frontieror_json.dump( + { + "feasible": False, + "violated_constraints": ["malformed_solution"], + "violations": [ + "checker rejected malformed candidate: " + + type(error).__name__ + + ": " + + str(error)[:500] + ], + "violation_magnitudes": [], + }, + result_handle, + indent=2, + ) + except Exception: + return False + return True + + +# Numerical tolerances. Gurobi default FeasibilityTol/IntFeasTol = 1e-6, +# but accumulated FP error on multi-thousand-arc instances can drift to ~1e-5. +# 1e-4 absolute floor + 1e-5 relative gives ~10x slack over solver default -- +# enough to absorb floating-point noise without masking real violations. +tol = 1e-4 +eps = 1e-5 +rel_tol = 1e-5 + + +def load_json(path): + with open(path, 'r') as f: + return json.load(f) + + +def compute_violation(lhs, rhs, op): + """ + Compute violation_amount for a constraint. + op: 'le' (<=), 'ge' (>=), 'eq' (=) + """ + if op == 'le': + return max(0.0, lhs - rhs) + elif op == 'ge': + return max(0.0, rhs - lhs) + elif op == 'eq': + return abs(lhs - rhs) + return 0.0 + + +def record_violation(violations_list, magnitudes_list, constraint_idx, msg, lhs, rhs, op): + """Check and record a violation if violation_amount > tol (absolute or relative).""" + va = compute_violation(lhs, rhs, op) + threshold = max(tol, rel_tol * max(abs(lhs), abs(rhs))) + if va > threshold: + violations_list.append((constraint_idx, msg)) + normalizer = max(abs(rhs), eps) + magnitudes_list.append({ + "constraint": constraint_idx, + "lhs": lhs, + "rhs": rhs, + "raw_excess": va, + "normalizer": normalizer, + "ratio": va / normalizer, + }) + return True + return False + + +def _shortest_path_demand_sum(edges_data, od_pairs): + """Lower bound on sum_p tau_p * y_p: each passenger takes the shortest + s-t path on the full undirected graph (capacity ignored). Since the actual + feasible set is a subset (passengers may only use arcs covered by active + lines and subject to capacity), the LP-optimal travel time is at least + this much.""" + adj = defaultdict(list) + for e in edges_data: + u, v = e["endpoints"] + tt_e = float(e["traveling_time_seconds"]) + adj[u].append((v, tt_e)) + adj[v].append((u, tt_e)) + od_by_origin = defaultdict(list) + for od in od_pairs: + od_by_origin[od["origin"]].append((od["destination"], float(od["demand"]))) + total = 0.0 + for s, dest_demand in od_by_origin.items(): + dist = {s: 0.0} + heap = [(0.0, s)] + remaining = {d for d, _ in dest_demand} + while heap and remaining: + du, u = heapq.heappop(heap) + if du > dist.get(u, float('inf')): + continue + if u in remaining: + remaining.discard(u) + for v, w in adj[u]: + nd = du + w + if nd < dist.get(v, float('inf')): + dist[v] = nd + heapq.heappush(heap, (nd, v)) + for d, demand in dest_demand: + sp = dist.get(d, None) + if sp is None: + # Unreachable -- contributes 0 to lower bound (cannot tighten). + continue + total += sp * demand + return total + + +def _check_objective_consistency( + violations, magnitudes, + solution, params, mode_by_name, edge_by_id, lines, + arc_travel_time, arc_cap, num_edges, od_pairs, + edges_data, + arc_total_flow_in, active_paths, +): + """Constraint (8): reported objective_value must be consistent with the + solution variables. See module docstring for the formula and the + full-recompute vs bracketed cases.""" + reported_obj = solution.get("objective_value") + if reported_obj is None: + return + try: + reported = float(reported_obj) + except (TypeError, ValueError): + return + if reported != reported: # NaN + return + + try: + lam = float(params.get("lambda", 0.0)) + except (TypeError, ValueError): + lam = 0.0 + + # === Exact line cost from active_lines + mode/edge data === + line_cost_exact = 0.0 + for line in lines: + mode = mode_by_name.get(line["mode_name"]) + if mode is None: + continue + try: + C_l = float(mode.get("fixed_cost_per_line", 0.0)) + except (TypeError, ValueError): + C_l = 0.0 + c_l_total = 0.0 + for eid in line["edges"]: + edge = edge_by_id.get(eid) + if edge is None: + continue + try: + c_l_total += float(edge.get("operating_cost", 0.0)) + except (TypeError, ValueError): + pass + try: + f_l = float(line["frequency"]) + except (TypeError, ValueError): + f_l = 0.0 + line_cost_exact += C_l + c_l_total * f_l + + # === Travel time component === + tt_actual = None + if arc_total_flow_in is not None: + tt_actual = 0.0 + for aid, f in arc_total_flow_in.items(): + try: + aid_norm = int(aid) + except (ValueError, TypeError): + aid_norm = aid + try: + tt_actual += float(f) * arc_travel_time.get(aid_norm, 0.0) + except (TypeError, ValueError): + pass + elif active_paths is not None: + tt_actual = 0.0 + for ap in active_paths: + try: + fl = float(ap.get("flow", 0.0)) + except (TypeError, ValueError): + fl = 0.0 + path_tt = 0.0 + for aid in ap.get("arcs", []) or []: + try: + aid_norm = int(aid) + except (ValueError, TypeError): + aid_norm = aid + path_tt += arc_travel_time.get(aid_norm, 0.0) + tt_actual += fl * path_tt + + if tt_actual is not None: + true_obj = lam * line_cost_exact + (1.0 - lam) * tt_actual + record_violation( + violations, magnitudes, 8, + f"Objective consistency violated: reported objective_value={reported} " + f"differs from recomputed lambda*line_cost + (1-lambda)*passenger_travel_time" + f"={true_obj} (line_cost={line_cost_exact}, tt={tt_actual}, lambda={lam})", + reported, true_obj, 'eq', + ) + return + + # === Bracketed check (no flow data: e.g. Gurobi reference) === + tt_lb = _shortest_path_demand_sum(edges_data, od_pairs) + tt_ub = 0.0 + num_arcs = 2 * num_edges + for a in range(num_arcs): + tt_ub += arc_cap.get(a, 0.0) * arc_travel_time.get(a, 0.0) + + obj_lb = lam * line_cost_exact + (1.0 - lam) * tt_lb + obj_ub = lam * line_cost_exact + (1.0 - lam) * tt_ub + + record_violation( + violations, magnitudes, 8, + f"Objective below lower bound: reported objective_value={reported} < " + f"lb={obj_lb} = lambda*line_cost + (1-lambda)*sum_(s,t) d_st*shortest_path_tt " + f"(line_cost={line_cost_exact}, tt_lb={tt_lb}, lambda={lam})", + reported, obj_lb, 'ge', + ) + record_violation( + violations, magnitudes, 8, + f"Objective above upper bound: reported objective_value={reported} > " + f"ub={obj_ub} = lambda*line_cost + (1-lambda)*sum_a (cap_a*tau_a) " + f"(line_cost={line_cost_exact}, tt_ub={tt_ub}, lambda={lam})", + reported, obj_ub, 'le', + ) + + +def check_feasibility(instance, solution): + """Check all hard constraints of the LPP formulation.""" + violations = [] # list of (constraint_idx, message) + magnitudes = [] # list of violation magnitude dicts + + # ---- Handle degenerate solutions ---- + if solution.get("objective_value") is None or solution.get("status") in ("no_solution", "infeasible"): + # No actual solution to check -- return null feasibility + return { + "feasible": None, + "violated_constraints": [], + "violations": [f"No solution to check (status={solution.get('status', 'unknown')})"], + "violation_magnitudes": [], + } + + # ---- Extract instance data ---- + params = instance["global_parameters"] + F = params["frequency_upper_bound_F"] + edges_data = instance["network"]["edges"] + num_nodes = instance["network"]["num_nodes"] + num_edges = instance["network"]["num_edges"] + od_pairs = instance["od_matrix"] + modes = instance["modes"] + + # Build mode lookup by name + mode_by_name = {} + for m in modes: + mode_by_name[m["name"]] = m + + # Edge data lookup + edge_by_id = {} + for e in edges_data: + edge_by_id[e["id"]] = e + + # Edge capacity (Lambda_e) + edge_capacity = {} + for e in edges_data: + edge_capacity[e["id"]] = e["edge_capacity"] + + # ---- Extract solution data ---- + active_lines = solution.get("active_lines", []) + + # Build line data: each line has edges, frequency, mode info + lines = [] + for al in active_lines: + mode_name = al["mode"] + mode = mode_by_name[mode_name] + lines.append({ + "edges": al["edges"], + "frequency": al["frequency"], + "mode_name": mode_name, + "kappa": mode["vehicle_capacity"], + "x": 1, # line is active => x_l = 1 + }) + + # ===================================================================== + # Constraint (5): x_l in {0, 1} + # All active lines have x_l = 1 implicitly. Check frequency > 0 lines + # have valid binary x. Since solution only lists active lines, x_l=1 for + # all listed lines, which is trivially binary. + # ===================================================================== + for i, line in enumerate(lines): + x_val = line["x"] + record_violation(violations, magnitudes, 5, + f"Line {i}: x_l = {x_val} is not in {{0, 1}}", + x_val, round(x_val), 'eq') + + # ===================================================================== + # Constraint (6): f_l >= 0 for all l in L + # ===================================================================== + for i, line in enumerate(lines): + f_l = line["frequency"] + record_violation(violations, magnitudes, 6, + f"Line {i}: frequency f_l = {f_l} < 0", + -f_l, 0.0, 'le') # -f_l <= 0 means f_l >= 0 + + # ===================================================================== + # Constraint (4): f_l <= F * x_l for all l in L + # For active lines, x_l = 1, so f_l <= F. + # ===================================================================== + for i, line in enumerate(lines): + f_l = line["frequency"] + x_l = line["x"] + rhs = F * x_l + record_violation(violations, magnitudes, 4, + f"Line {i}: frequency f_l = {f_l:.6f} > F * x_l = {rhs:.6f}", + f_l, rhs, 'le') + + # ===================================================================== + # Constraint (3): f(L_e) <= Lambda_e for all e in E + # Total frequency of all lines using edge e must not exceed edge capacity. + # ===================================================================== + edge_freq_sum = defaultdict(float) + for line in lines: + for eid in line["edges"]: + edge_freq_sum[eid] += line["frequency"] + + for eid in range(num_edges): + Lambda_e = edge_capacity[eid] + freq_sum = edge_freq_sum.get(eid, 0.0) + record_violation(violations, magnitudes, 3, + f"Edge {eid}: total frequency {freq_sum:.6f} > edge capacity Lambda_e = {Lambda_e}", + freq_sum, Lambda_e, 'le') + + # ===================================================================== + # Constraints (1), (2), (7): Passenger flow feasibility + # + # (1) y(P_st) = d_st for all (s,t) in D + # (2) y(P_a) - sum_{l: e(a) in l} kappa_l f_l <= 0 for all a in A + # (7) y_p >= 0 + # + # Since the solution does not include individual path flows y_p, we check + # whether a feasible passenger routing EXISTS by solving an LP: + # Find y >= 0 such that flow conservation (1) and capacity (2) hold. + # + # We formulate this as a multi-commodity flow on the directed arc graph. + # Each OD pair (s,t) is a commodity with demand d_st. + # Arc capacities come from the lines: cap_a = sum_{l: e(a) in l} kappa_l f_l. + # ===================================================================== + + # Build directed arcs from undirected edges + # arc 2*eid: u->v, arc 2*eid+1: v->u + arc_cap = defaultdict(float) # arc_id -> capacity from lines + for line in lines: + kappa = line["kappa"] + f_l = line["frequency"] + for eid in line["edges"]: + arc_cap[2 * eid] += kappa * f_l + arc_cap[2 * eid + 1] += kappa * f_l + + # Build adjacency for directed arcs + arc_adj = defaultdict(list) # node -> list of (neighbor, arc_id) + arc_travel_time = {} + arc_endpoints = {} + for e in edges_data: + eid = e["id"] + u, v = e["endpoints"] + tt_e = e["traveling_time_seconds"] + fwd = 2 * eid + bwd = 2 * eid + 1 + arc_adj[u].append((v, fwd)) + arc_adj[v].append((u, bwd)) + arc_travel_time[fwd] = tt_e + arc_travel_time[bwd] = tt_e + arc_endpoints[fwd] = (u, v) + arc_endpoints[bwd] = (v, u) + + # ===================================================================== + # Fast path A (preferred): solution provides verification aggregates + # `od_served` and `arc_total_flow`. These are formulation-agnostic -- any + # solver (paper's or LLM-generated, path-based or arc-based MCF) can + # aggregate its own flows once at the end without committing to a + # particular formulation. Checker uses these directly to verify (1)(2), + # avoiding the LP slow path entirely. + # ===================================================================== + od_served = solution.get("od_served", None) + arc_total_flow_in = solution.get("arc_total_flow", None) + active_paths = solution.get("active_passenger_paths", None) + if (od_served is not None and arc_total_flow_in is not None + and not active_paths): + flow_per_od = defaultdict(float) + for entry in od_served: + flow_per_od[(entry["origin"], entry["destination"])] += entry["flow"] + # arc_total_flow is dict; keys may be str or int depending on serializer + flow_per_arc = {} + for aid, f in arc_total_flow_in.items(): + try: + aid_norm = int(aid) + except (ValueError, TypeError): + aid_norm = aid + flow_per_arc[aid_norm] = float(f) + + for od in od_pairs: + key = (od["origin"], od["destination"]) + d = od["demand"] + served = flow_per_od.get(key, 0.0) + record_violation(violations, magnitudes, 1, + f"OD ({od['origin']}, {od['destination']}): " + f"served {served:.6f} != demand {d}", + served, d, 'eq') + + for aid, total_flow in flow_per_arc.items(): + cap = arc_cap.get(aid, 0.0) + record_violation(violations, magnitudes, 2, + f"Arc {aid}: total flow {total_flow:.6f} " + f"> capacity {cap:.6f}", + total_flow, cap, 'le') + + # Constraint (8): objective consistency. + _check_objective_consistency( + violations, magnitudes, + solution, params, mode_by_name, edge_by_id, lines, + arc_travel_time, arc_cap, num_edges, od_pairs, + edges_data, arc_total_flow_in, active_paths, + ) + + return { + "feasible": len(violations) == 0, + "violated_constraints": violations, + "violations": [v[1] for v in violations], + "violation_magnitudes": magnitudes, + } + + # ===================================================================== + # Fast path B (fallback): solution has `active_passenger_paths` (path-based + # solvers). Aggregate per-OD and per-arc flows from path list, then verify + # (1)(2). Same constraints, just different input format. + # ===================================================================== + if active_paths: + # Constraint (1): per-OD demand met by sum of path flows + flow_per_od = defaultdict(float) + # Constraint (2): per-arc total flow <= capacity from lines + flow_per_arc = defaultdict(float) + valid_od_keys = { + (od["origin"], od["destination"]) for od in od_pairs + } + for ap in active_paths: + key = (ap["origin"], ap["destination"]) + try: + path_flow = float(ap["flow"]) + except (TypeError, ValueError): + path_flow = -1.0 + arcs = ap.get("arcs", []) + path_valid = key in valid_od_keys and path_flow >= -tol + current = ap["origin"] + if not isinstance(arcs, list) or (not arcs and key[0] != key[1]): + path_valid = False + else: + for raw_aid in arcs: + try: + aid = int(raw_aid) + except (TypeError, ValueError): + path_valid = False + break + endpoints = arc_endpoints.get(aid) + if endpoints is None or endpoints[0] != current: + path_valid = False + break + current = endpoints[1] + if current != ap["destination"]: + path_valid = False + if not path_valid: + record_violation( + violations, magnitudes, 7, + f"Passenger path {key} is not a valid directed path with " + f"nonnegative flow: arcs={arcs}, flow={ap.get('flow')}", + 0.0, 1.0, 'eq', + ) + continue + flow_per_od[key] += path_flow + for aid in arcs: + flow_per_arc[int(aid)] += path_flow + + for od in od_pairs: + key = (od["origin"], od["destination"]) + d = od["demand"] + served = flow_per_od.get(key, 0.0) + record_violation(violations, magnitudes, 1, + f"OD ({od['origin']}, {od['destination']}): " + f"sum y_p = {served:.6f} != demand {d}", + served, d, 'eq') + + for aid, total_flow in flow_per_arc.items(): + cap = arc_cap.get(aid, 0.0) + if total_flow > cap + tol: + record_violation(violations, magnitudes, 2, + f"Arc {aid}: total flow {total_flow:.6f} " + f"> capacity {cap:.6f}", + total_flow, cap, 'le') + + # Constraint (8): objective consistency. + _check_objective_consistency( + violations, magnitudes, + solution, params, mode_by_name, edge_by_id, lines, + arc_travel_time, arc_cap, num_edges, od_pairs, + edges_data, arc_total_flow_in, active_paths, + ) + + return { + "feasible": len(violations) == 0, + "violated_constraints": violations, + "violations": [v[1] for v in violations], + "violation_magnitudes": magnitudes, + } + + # No complete passenger certificate. The old fallback allocated + # |OD|*|arcs| variables (over 21 million on the stored large instances), + # could exhaust evaluator memory, and still used only a broad objective + # envelope. Fail closed and ask the solver to emit the already-supported + # path or aggregate witness; patched gurobi_code.py emits both. + record_violation( + violations, magnitudes, 1, + "Missing passenger-flow certificate: provide nonempty " + "active_passenger_paths, or both od_served and arc_total_flow", + 0.0, 1.0, 'eq', + ) + return { + "feasible": False, + "violated_constraints": sorted(set(c for c, _ in violations)), + "violations": [message for _, message in violations], + "violation_magnitudes": magnitudes, + } + + # Legacy unreachable implementation retained below for provenance. + # Slow path: solve multi-commodity flow LP via scipy linprog + try: + from scipy.optimize import linprog + from scipy.sparse import lil_matrix + _has_scipy = True + except ImportError: + _has_scipy = False + + if _has_scipy and od_pairs: + # Variables: y_{a,k} for each arc a and commodity k + # where k indexes OD pairs + num_od = len(od_pairs) + num_arcs = 2 * num_edges + + # Variable index: k * num_arcs + a + num_vars = num_od * num_arcs + + # Objective: minimize sum of flows (just find feasible) + c_obj = [0.0] * num_vars + + # Equality constraints: flow conservation for each (commodity, node) + # For commodity k with OD (s_k, t_k, d_k): + # sum_{a out of v} y_{a,k} - sum_{a into v} y_{a,k} = d_k if v=s_k + # = -d_k if v=t_k + # = 0 otherwise + + # Build incidence: for each node, list of (arc_id, +1 if outgoing, -1 if incoming) + node_arcs = defaultdict(list) # node -> list of (arc_id, sign) + for e in edges_data: + eid = e["id"] + u, v = e["endpoints"] + fwd = 2 * eid + bwd = 2 * eid + 1 + node_arcs[u].append((fwd, +1)) # u -> v: outgoing from u + node_arcs[v].append((fwd, -1)) # u -> v: incoming to v + node_arcs[v].append((bwd, +1)) # v -> u: outgoing from v + node_arcs[u].append((bwd, -1)) # v -> u: incoming to u + + # Equality constraints + eq_rows = [] + eq_rhs = [] + for k, od in enumerate(od_pairs): + s_k = od["origin"] + t_k = od["destination"] + d_k = od["demand"] + for v in range(num_nodes): + row = {} + for arc_id, sign in node_arcs[v]: + col = k * num_arcs + arc_id + row[col] = row.get(col, 0.0) + sign + if row: # only add if node has arcs + eq_rows.append(row) + if v == s_k: + eq_rhs.append(float(d_k)) + elif v == t_k: + eq_rhs.append(-float(d_k)) + else: + eq_rhs.append(0.0) + + # Inequality constraints: sum_k y_{a,k} <= cap_a for each arc a + ineq_rows = [] + ineq_rhs = [] + for a in range(num_arcs): + cap_a = arc_cap.get(a, 0.0) + row = {} + for k in range(num_od): + col = k * num_arcs + a + row[col] = 1.0 + if row: + ineq_rows.append(row) + ineq_rhs.append(cap_a) + + # Build sparse matrices + n_eq = len(eq_rows) + n_ineq = len(ineq_rows) + + A_eq = lil_matrix((n_eq, num_vars)) + b_eq = eq_rhs + for i, row in enumerate(eq_rows): + for col, val in row.items(): + A_eq[i, col] = val + + A_ub = lil_matrix((n_ineq, num_vars)) + b_ub = ineq_rhs + for i, row in enumerate(ineq_rows): + for col, val in row.items(): + A_ub[i, col] = val + + bounds = [(0.0, None)] * num_vars + + result = linprog( + c_obj, + A_ub=A_ub.tocsc(), b_ub=b_ub, + A_eq=A_eq.tocsc(), b_eq=b_eq, + bounds=bounds, + method='highs', + options={'presolve': True, 'time_limit': 300}, + ) + + if not result.success: + # Passenger flow is infeasible -- determine which constraints are violated. + # Check constraint (1): can demand be routed at all (ignoring capacity)? + # Check constraint (2): is there enough capacity? + + def bfs_reachable(src, adj, n): + visited = set() + queue = [src] + visited.add(src) + while queue: + u = queue.pop(0) + for v, _ in adj[u]: + if v not in visited: + visited.add(v) + queue.append(v) + return visited + + # Re-solve with relaxed capacity to see if it's a capacity issue + # vs connectivity issue + # Try uncapacitated version (only flow conservation) + result_uncp = linprog( + c_obj, + A_eq=A_eq.tocsc(), b_eq=b_eq, + bounds=bounds, + method='highs', + options={'presolve': True, 'time_limit': 60}, + ) + + if not result_uncp.success: + # Even without capacity, flow conservation fails + # This means the network is disconnected for some OD pair + # or the arc graph doesn't allow routing + for k, od in enumerate(od_pairs): + s_k = od["origin"] + t_k = od["destination"] + d_k = od["demand"] + # Check connectivity via arcs that have capacity > 0 + # (i.e., covered by at least one line) + covered_adj = defaultdict(list) + for e in edges_data: + eid = e["id"] + u, v = e["endpoints"] + # Only include arcs with capacity (from active lines) + if arc_cap.get(2 * eid, 0.0) > 0: + covered_adj[u].append((v, 2 * eid)) + if arc_cap.get(2 * eid + 1, 0.0) > 0: + covered_adj[v].append((u, 2 * eid + 1)) + + reachable = bfs_reachable(s_k, covered_adj, num_nodes) + if t_k not in reachable: + record_violation(violations, magnitudes, 1, + f"OD ({s_k},{t_k}): demand {d_k} cannot be routed; " + f"destination not reachable from origin via active lines", + 0.0, float(d_k), 'eq') + else: + # Uncapacitated is feasible but capacitated is not + # => capacity constraint (2) is violated + # Find which arcs are bottlenecks + y_uncp = result_uncp.x + for a in range(num_arcs): + cap_a = arc_cap.get(a, 0.0) + total_flow = sum(y_uncp[k * num_arcs + a] for k in range(num_od)) + if total_flow > cap_a + tol: + eid = a // 2 + e = edge_by_id[eid] + u, v = e["endpoints"] + direction = f"{u}->{v}" if a % 2 == 0 else f"{v}->{u}" + record_violation(violations, magnitudes, 2, + f"Arc {a} ({direction}): passenger flow {total_flow:.4f} " + f"exceeds line capacity {cap_a:.4f}", + total_flow, cap_a, 'le') + + # Also record constraint (1) as violated since demand can't be met + # with capacity limits + for k, od in enumerate(od_pairs): + s_k = od["origin"] + t_k = od["destination"] + d_k = od["demand"] + # Check how much demand could actually be routed + # by solving max-flow per commodity (simplified: use the LP result) + record_violation(violations, magnitudes, 1, + f"OD ({s_k},{t_k}): demand {d_k} may not be fully satisfiable " + f"due to insufficient arc capacity from active lines", + 0.0, float(d_k), 'eq') + # If result.success, constraints (1), (2), (7) are all satisfied + # (y >= 0 is enforced by bounds, flow conservation by A_eq, capacity by A_ub) + + elif not _has_scipy and od_pairs: + # Cannot check passenger flow constraints without scipy + # Skip with a warning -- do not record as violation + pass + + # ===================================================================== + # Constraint (8): objective consistency. See _check_objective_consistency. + # ===================================================================== + _check_objective_consistency( + violations, magnitudes, + solution, params, mode_by_name, edge_by_id, lines, + arc_travel_time, arc_cap, num_edges, od_pairs, + edges_data, arc_total_flow_in, active_paths, + ) + + # ===================================================================== + # Compile results + # ===================================================================== + violated_indices = sorted(set(c for c, _ in violations)) + violation_msgs = [msg for _, msg in violations] + + feasible = len(violated_indices) == 0 + + return { + "feasible": feasible, + "violated_constraints": violated_indices, + "violations": violation_msgs, + "violation_magnitudes": magnitudes, + } + + +def main(): + parser = argparse.ArgumentParser( + description="Feasibility checker for the Line Planning Problem (LPP)" + ) + parser.add_argument("--instance_path", type=str, required=True, + help="Path to the JSON file containing the data instance") + parser.add_argument("--solution_path", type=str, required=True, + help="Path to the JSON file containing the candidate solution") + parser.add_argument("--result_path", type=str, required=True, + help="Path to write the JSON file containing the feasibility result") + args = parser.parse_args() + + instance = load_json(args.instance_path) + solution = load_json(args.solution_path) + _frontieror_contract_result = _frontieror_validate_solution_contract(solution) + if _frontieror_contract_result is not None: + with open(args.result_path, "w") as _frontieror_result_handle: + json.dump( + _frontieror_contract_result, + _frontieror_result_handle, + indent=2, + ) + return + + result = check_feasibility(instance, solution) + + with open(args.result_path, 'w') as f: + json.dump(result, f, indent=2) + + if result["feasible"]: + print(f"FEASIBLE: No constraint violations found.") + else: + print(f"INFEASIBLE: {len(result['violated_constraints'])} constraint(s) violated: " + f"{result['violated_constraints']}") + for msg in result["violations"]: + print(f" - {msg}") + + +if __name__ == "__main__": + try: + main() + except Exception as _frontieror_unexpected_error: + if not _frontieror_write_unexpected_rejection( + _frontieror_unexpected_error + ): + raise diff --git a/borndorfer2007/gurobi_code.py b/borndorfer2007/gurobi_code.py new file mode 100644 index 0000000000000000000000000000000000000000..3cdf2e00f4a9234acadb20275a52ae3a790e8aeb --- /dev/null +++ b/borndorfer2007/gurobi_code.py @@ -0,0 +1,549 @@ +#!/usr/bin/env python3 +""" +Gurobi MIP implementation for the Line Planning Problem (LPP). + +Paper: "A Column-Generation Approach to Line Planning in Public Transport" +Authors: Borndörfer, Grötschel, Pfetsch (2007), Transportation Science 41(1), pp. 123-132. + +Model (LPP): + min lambda * (C^T x + c^T f) + (1 - lambda) * tau^T y + + subject to: + y(P_st) = d_st for all (s,t) in D (1) + y(P_a) - sum_{l: e(a) in l} kappa_l f_l <= 0 for all a in A (2) + f(L_e) <= Lambda_e for all e in E (3) + f <= F * x (4) + x_l in {0,1} for all l in L (5) + f_l >= 0 for all l in L (6) + y_p >= 0 for all p in P (7) + +Since the sets L (lines) and P (passenger paths) are exponentially large, +we enumerate lines with a time/count budget and use k-shortest paths for passengers. + +INFERRED ASSUMPTION: The paper uses column generation to handle the exponential +number of variables. For the direct MIP, we enumerate a bounded subset of lines +(capped at MAX_LINES to keep the model tractable) and k-shortest passenger paths. +This is necessary because full enumeration is intractable for dense networks. +**NOT SPECIFIED IN PAPER** for a direct MIP approach. +""" + +import argparse +import json +import sys +import time +import heapq +from collections import defaultdict +import os as _os, sys as _sys +# Walk up from this file's directory to find repo root (containing scripts/). +_repo = _os.path.dirname(_os.path.abspath(__file__)) +while _repo != _os.path.dirname(_repo) and not _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _repo = _os.path.dirname(_repo) +if _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _sys.path.insert(0, _repo) +try: + from scripts.utils.gurobi_log_helper import install_gurobi_logger +except ImportError: + def install_gurobi_logger(log_path): # no-op fallback when scripts/ unavailable + pass + + +try: + import gurobipy as gp + from gurobipy import GRB +except ImportError: + print("ERROR: gurobipy is required. Install Gurobi and its Python interface.") + sys.exit(1) + +# Maximum number of lines to enumerate before stopping +MAX_LINES = 50000 +# Maximum time (seconds) for line enumeration +MAX_ENUM_TIME = 30 + + +def load_instance(path): + with open(path, 'r') as f: + return json.load(f) + + +def build_directed_arcs(edges_data): + """ + Build directed arcs from undirected edges. + Each edge e = (u,v) produces two arcs: + arc a(e): u -> v (arc_id = 2*e_id) + arc a_bar(e): v -> u (arc_id = 2*e_id + 1) + """ + arcs = [] + arc_adj = defaultdict(list) + for e in edges_data: + eid = e["id"] + u, v = e["endpoints"] + tt = e["traveling_time_seconds"] + fwd_id = 2 * eid + arcs.append({"id": fwd_id, "from": u, "to": v, "edge_id": eid, "travel_time": tt}) + arc_adj[u].append((v, fwd_id)) + bwd_id = 2 * eid + 1 + arcs.append({"id": bwd_id, "from": v, "to": u, "edge_id": eid, "travel_time": tt}) + arc_adj[v].append((u, bwd_id)) + return arcs, arc_adj + + +def enumerate_lines_bounded(instance, edges_data, max_length, max_lines=MAX_LINES, + max_time=MAX_ENUM_TIME): + """ + Enumerate feasible lines for each mode with time and count bounds. + A line of mode i is a simple path in G_i between two terminals of T_i, + with at most max_length edges. + + Prioritizes shorter lines first (BFS-like by depth). + """ + lines = [] + modes = instance["modes"] + start_time = time.time() + + for mode_idx, mode in enumerate(modes): + terminals = set(mode["terminals"]) + mode_adj = defaultdict(list) + for eid in mode["edge_indices"]: + e = edges_data[eid] + u, v = e["endpoints"] + mode_adj[u].append((v, eid)) + mode_adj[v].append((u, eid)) + + terminal_list = sorted(terminals) + + # Enumerate using iterative deepening to prioritize shorter lines + for max_depth in range(1, max_length + 1): + if len(lines) >= max_lines or (time.time() - start_time) > max_time: + break + for s in terminal_list: + if len(lines) >= max_lines or (time.time() - start_time) > max_time: + break + # DFS with depth exactly up to max_depth (but only add lines at this depth + # that weren't found at shorter depths) + stack = [(s, frozenset([s]), [], [s])] + while stack: + if len(lines) >= max_lines or (time.time() - start_time) > max_time: + break + curr, visited, edge_path, node_path = stack.pop() + depth = len(edge_path) + + # Record line if at a different terminal and at exactly this depth level + if curr in terminals and curr != s and curr > s and depth == max_depth: + lines.append({ + "mode_idx": mode_idx, + "edges": list(edge_path), + "nodes": list(node_path), + }) + + if depth < max_depth: + for neighbor, eid in mode_adj[curr]: + if neighbor not in visited: + stack.append(( + neighbor, + visited | frozenset([neighbor]), + edge_path + [eid], + node_path + [neighbor], + )) + + # Deduplicate lines (same set of edges) + seen = set() + unique_lines = [] + for line in lines: + key = (line["mode_idx"], tuple(sorted(line["edges"]))) + if key not in seen: + seen.add(key) + unique_lines.append(line) + + # Post-enumeration edge coverage: ensure every edge has ≥1 line. + # The bounded enumeration may truncate before generating a line through + # some edges; the capacity constraint sum y - kappa*f ≤ 0 then has no f + # variable, forcing all paths through that edge to 0 → presolve INFEAS. + covered = set() + for line in unique_lines: + covered.update(line["edges"]) + uncovered_edges = [e for e in edges_data if e["id"] not in covered] + pre_count = len(unique_lines) + from collections import deque + for e in uncovered_edges: + eid = e["id"] + u, v = e["endpoints"] + for mode_idx, mode in enumerate(modes): + if eid not in mode["edge_indices"]: + continue + terminals_set = set(mode["terminals"]) + mode_adj = defaultdict(list) + for meid in mode["edge_indices"]: + me = edges_data[meid] + mu, mv = me["endpoints"] + mode_adj[mu].append((mv, meid)) + mode_adj[mv].append((mu, meid)) + + def bfs_terminal(start, exclude=None): + q = deque([(start, [start], [])]) + vis = {start} + while q: + n, npath, epath = q.popleft() + if n in terminals_set and n != exclude: + return npath, epath + for nb, meid in mode_adj[n]: + if nb not in vis: + vis.add(nb) + q.append((nb, npath + [nb], epath + [meid])) + return None, None + + up, ue = bfs_terminal(u) + if up is None: + continue + s = up[-1] + vp, ve = bfs_terminal(v, exclude=s) + if vp is None: + continue + line_edges = list(reversed(ue)) + [eid] + ve + line_nodes = list(reversed(up)) + vp + if len(line_edges) > max_length: + continue + key = (mode_idx, tuple(sorted(line_edges))) + if key in seen: + continue + seen.add(key) + unique_lines.append({ + "mode_idx": mode_idx, + "edges": line_edges, + "nodes": line_nodes, + }) + break # one line per edge is enough + if uncovered_edges: + added = len(unique_lines) - pre_count + print(f" Edge coverage: added {added} fallback lines for {len(uncovered_edges)} uncovered edges (total: {len(unique_lines)})") + + return unique_lines + + +def dijkstra(source, target, arc_adj, arcs, num_nodes, excluded_arcs=None): + """Dijkstra's shortest path. Returns (dist, arc_path) or None.""" + if excluded_arcs is None: + excluded_arcs = set() + dist = [float('inf')] * num_nodes + prev = [None] * num_nodes + dist[source] = 0.0 + pq = [(0.0, source)] + while pq: + d, u = heapq.heappop(pq) + if d > dist[u]: + continue + if u == target: + path_arcs = [] + node = target + while prev[node] is not None: + pn, aid = prev[node] + path_arcs.append(aid) + node = pn + path_arcs.reverse() + return dist[target], path_arcs + for v, aid in arc_adj[u]: + if aid in excluded_arcs: + continue + nd = d + arcs[aid]["travel_time"] + if nd < dist[v]: + dist[v] = nd + prev[v] = (u, aid) + heapq.heappush(pq, (nd, v)) + return None + + +def k_shortest_paths(source, target, arc_adj, arcs, num_nodes, k): + """Yen's k-shortest simple paths algorithm.""" + result = dijkstra(source, target, arc_adj, arcs, num_nodes) + if result is None: + return [] + _, first_path = result + A = [first_path] + B = [] + + for ki in range(1, k): + if not A: + break + last_path = A[-1] + last_nodes = [source] + for aid in last_path: + last_nodes.append(arcs[aid]["to"]) + + for i in range(len(last_path)): + spur_node = last_nodes[i] + root_path = last_path[:i] + + excluded = set() + for p in A: + if len(p) >= i and p[:i] == root_path and i < len(p): + excluded.add(p[i]) + + root_nodes = set(last_nodes[:i]) + excluded_arcs = set(excluded) + for aid_check in range(len(arcs)): + if arcs[aid_check]["from"] in root_nodes: + excluded_arcs.add(aid_check) + + spur_result = dijkstra(spur_node, target, arc_adj, arcs, num_nodes, excluded_arcs) + if spur_result is not None: + _, spur_path = spur_result + total_path = root_path + spur_path + total_cost = sum(arcs[aid]["travel_time"] for aid in total_path) + path_nodes = [source] + for aid in total_path: + path_nodes.append(arcs[aid]["to"]) + if len(path_nodes) == len(set(path_nodes)): + heapq.heappush(B, (total_cost, total_path)) + + if not B: + break + while B: + cost, candidate = heapq.heappop(B) + if candidate not in A: + A.append(candidate) + break + else: + break + return A + + +def main(): + parser = argparse.ArgumentParser(description="Gurobi MIP solver for Line Planning Problem") + parser.add_argument("--instance_path", type=str, required=True, help="Path to instance JSON") + parser.add_argument("--solution_path", type=str, required=True, help="Path for output solution JSON") + parser.add_argument("--time_limit", type=int, required=True, help="Solver time limit in seconds") + parser.add_argument("--log_path", type=str, default=None, help="Path to log incumbent solutions") + args = parser.parse_args() + install_gurobi_logger(args.log_path) + + total_start = time.time() + print("Loading instance...") + instance = load_instance(args.instance_path) + + lam = instance["global_parameters"]["lambda"] + max_line_length = instance["global_parameters"]["max_line_length_edges"] + F = instance["global_parameters"]["frequency_upper_bound_F"] + modes = instance["modes"] + edges_data = instance["network"]["edges"] + od_pairs = instance["od_matrix"] + num_nodes = instance["network"]["num_nodes"] + + arcs, arc_adj = build_directed_arcs(edges_data) + num_arcs = len(arcs) + + # Step 1: Enumerate lines (bounded) + print("Enumerating lines (bounded)...") + t0 = time.time() + lines = enumerate_lines_bounded(instance, edges_data, max_line_length) + print(f" Enumerated {len(lines)} lines in {time.time()-t0:.1f}s") + + if len(lines) == 0: + print("ERROR: No feasible lines found.") + solution = {"objective_value": None, "status": "infeasible", "error": "No lines enumerated"} + with open(args.solution_path, 'w') as f: + json.dump(solution, f, indent=2) + return + + # Step 2: Find passenger paths (k-shortest per OD pair) + print("Finding passenger paths...") + t0 = time.time() + # INFERRED ASSUMPTION: k=10 shortest paths per OD pair. **NOT SPECIFIED IN PAPER** + # for a direct MIP. The paper uses column generation for passenger paths. + K_PATHS = 10 + paths_by_od = {} + for od in od_pairs: + s, t = od["origin"], od["destination"] + paths_by_od[(s, t)] = k_shortest_paths(s, t, arc_adj, arcs, num_nodes, K_PATHS) + total_paths = sum(len(v) for v in paths_by_od.values()) + print(f" Found {total_paths} passenger paths in {time.time()-t0:.1f}s") + + # Step 3: Build Gurobi model + print("Building Gurobi model...") + + # Calculate remaining time for solver + elapsed = time.time() - total_start + solver_time = max(10, args.time_limit - int(elapsed)) + + model = gp.Model("LPP") + model.setParam("Threads", 1) + model.setParam("TimeLimit", solver_time) + model.setParam("OutputFlag", 1) + + # Decision variables + x = model.addVars(len(lines), vtype=GRB.BINARY, name="x") + f = model.addVars(len(lines), vtype=GRB.CONTINUOUS, lb=0.0, name="f") + + all_paths = [] + path_idx_by_od = {} + for od in od_pairs: + key = (od["origin"], od["destination"]) + path_idx_by_od[key] = [] + for path_arcs in paths_by_od.get(key, []): + idx = len(all_paths) + all_paths.append((key, path_arcs)) + path_idx_by_od[key].append(idx) + y = model.addVars(len(all_paths), vtype=GRB.CONTINUOUS, lb=0.0, name="y") + + # Objective: min lambda * (C^T x + c^T f) + (1 - lambda) * tau^T y + obj = gp.LinExpr() + for l_idx, line in enumerate(lines): + mode = modes[line["mode_idx"]] + C_l = mode["fixed_cost_per_line"] + c_l = sum(edges_data[eid]["operating_cost"] for eid in line["edges"]) + obj += lam * C_l * x[l_idx] + obj += lam * c_l * f[l_idx] + for p_idx, (od_key, path_arcs) in enumerate(all_paths): + tau_p = sum(arcs[aid]["travel_time"] for aid in path_arcs) + obj += (1 - lam) * tau_p * y[p_idx] + model.setObjective(obj, GRB.MINIMIZE) + + # Constraint (1): Flow conservation + for od in od_pairs: + key = (od["origin"], od["destination"]) + demand = od["demand"] + indices = path_idx_by_od.get(key, []) + if not indices: + print(f" WARNING: No paths for OD pair {key}, demand={demand}") + continue + model.addConstr( + gp.quicksum(y[i] for i in indices) == demand, + name=f"flow_{key[0]}_{key[1]}" + ) + + # Constraint (2): Capacity + arc_to_lines = defaultdict(list) + for l_idx, line in enumerate(lines): + kappa = modes[line["mode_idx"]]["vehicle_capacity"] + for eid in line["edges"]: + arc_to_lines[2 * eid].append((l_idx, kappa)) + arc_to_lines[2 * eid + 1].append((l_idx, kappa)) + + arc_to_paths = defaultdict(list) + for p_idx, (od_key, path_arcs) in enumerate(all_paths): + for aid in path_arcs: + arc_to_paths[aid].append(p_idx) + + for aid in set(arc_to_paths.keys()) | set(arc_to_lines.keys()): + path_ids = arc_to_paths.get(aid, []) + line_entries = arc_to_lines.get(aid, []) + if not path_ids and not line_entries: + continue + lhs = gp.LinExpr() + for p_idx in path_ids: + lhs += y[p_idx] + for l_idx, kappa in line_entries: + lhs -= kappa * f[l_idx] + model.addConstr(lhs <= 0, name=f"cap_a{aid}") + + # Constraint (3): Frequency bounds + edge_to_lines = defaultdict(list) + for l_idx, line in enumerate(lines): + for eid in line["edges"]: + edge_to_lines[eid].append(l_idx) + for e in edges_data: + eid = e["id"] + Lambda_e = e["edge_capacity"] + line_ids = edge_to_lines.get(eid, []) + if line_ids: + model.addConstr( + gp.quicksum(f[l_idx] for l_idx in line_ids) <= Lambda_e, + name=f"freq_e{eid}" + ) + + # Constraint (4): Linking f <= F*x + for l_idx in range(len(lines)): + model.addConstr(f[l_idx] <= F * x[l_idx], name=f"link_{l_idx}") + + print(f"Model: {model.NumVars} vars, {model.NumConstrs} constrs") + print(f"Solving with time limit {solver_time}s...") + model.optimize() + + # Extract solution + solution = {} + if model.SolCount > 0: + obj_val = model.ObjVal + solution["objective_value"] = obj_val + solution["status"] = "optimal" if model.Status == GRB.OPTIMAL else "time_limit" + solution["mip_gap"] = model.MIPGap if hasattr(model, 'MIPGap') else None + solution["solve_time_seconds"] = model.Runtime + + active_lines = [] + total_fixed_cost = 0.0 + total_operating_cost = 0.0 + for l_idx, line in enumerate(lines): + if x[l_idx].X > 0.5: + mode = modes[line["mode_idx"]] + active_lines.append({ + "line_index": l_idx, + "mode": mode["name"], + "nodes": line["nodes"], + "edges": line["edges"], + "frequency": f[l_idx].X, + }) + total_fixed_cost += mode["fixed_cost_per_line"] + total_operating_cost += ( + sum(edges_data[eid]["operating_cost"] for eid in line["edges"]) + * f[l_idx].X + ) + solution["active_lines"] = active_lines + solution["num_active_lines"] = len(active_lines) + + total_travel_time = 0.0 + num_active_paths = 0 + active_paths = [] + # Verification aggregates: per-OD served demand and per-arc total flow. + # These are formulation-agnostic stats (they don't expose the path-based + # decision variables), so any alternative solver can produce them by + # aggregating its own flows once at the end. The checker will prefer + # these over `active_passenger_paths` when both are present. + od_served_agg = defaultdict(float) + arc_total_flow = defaultdict(float) + for p_idx, (od_key, path_arcs) in enumerate(all_paths): + if y[p_idx].X > 1e-6: + tau_p = sum(arcs[aid]["travel_time"] for aid in path_arcs) + total_travel_time += tau_p * y[p_idx].X + num_active_paths += 1 + active_paths.append({ + "origin": od_key[0], + "destination": od_key[1], + "arcs": list(path_arcs), + "flow": y[p_idx].X, + }) + od_served_agg[(od_key[0], od_key[1])] += y[p_idx].X + for aid in path_arcs: + arc_total_flow[aid] += y[p_idx].X + + solution["active_passenger_paths"] = active_paths + solution["od_served"] = [ + {"origin": o, "destination": d, "flow": f} + for (o, d), f in od_served_agg.items() + ] + solution["arc_total_flow"] = {str(aid): f for aid, f in arc_total_flow.items()} + solution["total_travel_time"] = total_travel_time + solution["scaled_travel_time"] = (1 - lam) * total_travel_time + solution["total_fixed_cost"] = total_fixed_cost + solution["total_operating_cost"] = total_operating_cost + solution["scaled_line_cost"] = lam * (total_fixed_cost + total_operating_cost) + solution["num_active_passenger_paths"] = num_active_paths + + print(f"\n=== SOLUTION ===") + print(f"Objective value: {obj_val:.2f}") + print(f"Active lines: {len(active_lines)}") + print(f"Active passenger paths: {num_active_paths}") + print(f"Total travel time: {total_travel_time:.2f}") + print(f"Scaled travel time: {(1-lam)*total_travel_time:.2f}") + print(f"Total fixed cost: {total_fixed_cost:.2f}") + print(f"Total operating cost: {total_operating_cost:.2f}") + print(f"Scaled line cost: {lam*(total_fixed_cost+total_operating_cost):.2f}") + else: + solution["objective_value"] = None + solution["status"] = "infeasible" if model.Status == GRB.INFEASIBLE else "no_solution" + solution["solve_time_seconds"] = model.Runtime + print("No feasible solution found.") + + with open(args.solution_path, 'w') as outf: + json.dump(solution, outf, indent=2) + print(f"Solution written to {args.solution_path}") + + +if __name__ == "__main__": + main() diff --git a/borndorfer2007/gurobi_feasi_result/large_feasi_result_1.json b/borndorfer2007/gurobi_feasi_result/large_feasi_result_1.json new file mode 100644 index 0000000000000000000000000000000000000000..e305b82c3d6732211d5033415df1a105476e9cf1 --- /dev/null +++ b/borndorfer2007/gurobi_feasi_result/large_feasi_result_1.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3269392edcbb7720882e77caadb88b4a9cf2fda04cf482cebd23baace4a531fd +size 102 diff --git a/borndorfer2007/gurobi_feasi_result/large_feasi_result_2.json b/borndorfer2007/gurobi_feasi_result/large_feasi_result_2.json new file mode 100644 index 0000000000000000000000000000000000000000..e305b82c3d6732211d5033415df1a105476e9cf1 --- /dev/null +++ b/borndorfer2007/gurobi_feasi_result/large_feasi_result_2.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 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b/borndorfer2007/instance/tiny_instance.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ef0d55a3e5726cd01dfd24d07b4f8e1a6e49cee5a37fb324b52f563d289b70be +size 18357 diff --git a/borndorfer2007/instance/tiny_instance_2.json b/borndorfer2007/instance/tiny_instance_2.json new file mode 100644 index 0000000000000000000000000000000000000000..28c12964fe92ffea262fb1aa9431be4456489d28 --- /dev/null +++ b/borndorfer2007/instance/tiny_instance_2.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e18ba7da7398f6b57daffd614419b49b9207e47213b53fbb91db9f4148a8fc50 +size 18194 diff --git a/borndorfer2007/instance_schema.json b/borndorfer2007/instance_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..de9a6ad303ad78bd7a75180ca1c0c8ed22c434b2 --- /dev/null +++ b/borndorfer2007/instance_schema.json @@ -0,0 +1,60 @@ +{ + "global_parameters": { + "lambda": " Weight applied to total line cost in the objective, with (1 - lambda) applied to total passenger travel time.", + "fixed_cost_per_line": " Fixed cost incurred for establishing any transit line.", + "operating_cost_per_edge": " Cost per unit frequency for operating a line over one edge.", + "max_line_length_edges": " Maximum number of edges allowed in any single transit line.", + "frequency_upper_bound_F": " Global upper bound on the frequency assigned to any individual line.", + "traveling_times_unit": " Unit of measurement for all edge traveling times.", + "coordinates_unit": " Unit of measurement for node coordinates and edge lengths." + }, + "network": { + "num_nodes": " Total number of stations (nodes) in the transit network.", + "num_edges": " Total number of undirected edges connecting stations in the network.", + "num_od_nodes": " Number of stations that serve as origins or destinations for passenger demand.", + "area_km": " Side length of the square area containing the network.", + "nodes": [ + { + "id": " Unique identifier for this station.", + "x": " Horizontal coordinate of this station.", + "y": " Vertical coordinate of this station.", + "is_od_node": " Whether this station can be an origin or destination for passenger trips." + } + ], + "edges": [ + { + "id": " Unique identifier for this edge.", + "endpoints": " Pair of node identifiers connected by this edge.", + "traveling_time_seconds": " Time required to traverse this edge.", + "edge_capacity": " Maximum total frequency of all lines that may use this edge.", + "operating_cost": " Per-unit-frequency cost of operating a line over this edge.", + "length_km": " Physical length of this edge." + } + ] + }, + "modes": [ + { + "name": " Name of this transportation mode.", + "vehicle_capacity": " Number of passengers a single vehicle run of this mode can carry.", + "speed_kmh": " Travel speed of vehicles operating in this mode.", + "fixed_cost_per_line": " Fixed cost for establishing a line of this mode.", + "operating_cost_per_edge": " Per-unit-frequency cost for each edge traversed by a line of this mode.", + "terminals": " Node identifiers where lines of this mode may begin or end.", + "edge_indices": " Indices of edges available to lines of this mode.", + "num_edges": " Number of edges in the subnetwork for this mode.", + "num_terminals": " Number of terminal stations for this mode." + } + ], + "od_matrix": [ + { + "origin": " Node identifier of the trip origin.", + "destination": " Node identifier of the trip destination.", + "demand": " Number of passengers wishing to travel from the origin to the destination." + } + ], + "od_summary": { + "num_od_pairs": " Total number of origin-destination pairs with positive demand.", + "total_demand": " Sum of passenger demand across all origin-destination pairs.", + "num_od_nodes": " Number of distinct stations appearing as origins or destinations." + } +} diff --git a/borndorfer2007/mathematical_formulation.md b/borndorfer2007/mathematical_formulation.md new file mode 100644 index 0000000000000000000000000000000000000000..495849ed1fb4864197b5f768837840f1f5e41fc4 --- /dev/null +++ b/borndorfer2007/mathematical_formulation.md @@ -0,0 +1,68 @@ +# Original Formulation: Line Planning Problem (LPP) + +*Source: A Column-Generation Approach to Line Planning in Public Transport, Ralf Borndörfer, Martin Grötschel, Marc E. Pfetsch, 2007 (Transportation Science 41(1), pp. 123–132).* + +## Sets and Indices + +- $M$ — number of transportation *modes* (bus, tram, subway, etc.), indexed by $i = 1,\dots,M$. + +- $G = (V,E) = (V,\, E_1 \,\dot\cup\, \cdots \,\dot\cup\, E_M)$ — undirected multigraph representing the multimodal transportation network ($V$ nodes, $E$ edges partitioned by mode). + +- $G_i = (V, E_i)$ — subnetwork of $G$ corresponding to mode $i$. + +- $\mathcal{T}_1,\dots,\mathcal{T}_M \subseteq V$ — terminal sets; nodes where lines of each mode can start and end. + +- $\mathcal{L}$ — set of all feasible lines. A *line* of mode $i$ is a (simple) path in $G_i$ connecting two different terminals of $\mathcal{T}_i$. + +- $\mathcal{L}_e := \bigcup\{\, l \in \mathcal{L} : e \in l \,\}$ — set of lines that use edge $e \in E$. + +- $D := \{\, (s,t) \in V \times V : d_{st} > 0 \,\}$ — set of all OD-pairs. + +- $(V, A)$ — directed passenger route graph derived from $G = (V,E)$ by replacing each edge $e \in E$ with two antiparallel arcs $a(e)$ and $\bar a(e)$; $e(a) \in E$ denotes the undirected edge corresponding to $a \in A$. + +- $\mathcal{P}_{st}$ — set of all $(s,t)$-passenger paths (directed $s$–$t$ paths in $(V,A)$), for $(s,t) \in D$. + +- $\mathcal{P} := \bigcup\{\, p \in \mathcal{P}_{st} : (s,t) \in D \,\}$ — set of all passenger paths. + +- $\mathcal{P}_a := \bigcup\{\, p \in \mathcal{P} : a \in p \,\}$ — set of all passenger paths using arc $a \in A$. + +## Parameters + +- $d_{st} \in \mathbb{Q}_+$ — travel demand from $s$ to $t$ (entry of the not-necessarily-symmetric OD-matrix), for $(s,t) \in V \times V$. + +- $\mathbf{c}^i \in \mathbb{Q}_+^{E_i}$ — line operating costs on the edges of mode $i$; $c_l := \sum_{e \in l} c^i_e$ is the operating cost of line $l$ of mode $i$. + +- $C_1,\dots,C_M \in \mathbb{Q}_+$ — fixed cost for setting up a line of each mode; $C_l := C_i$ for a line $l$ of mode $i$. + +- $\kappa_1,\dots,\kappa_M \in \mathbb{Q}_+$ — vehicle capacity for each mode; $\kappa_l := \kappa_i$ for a line $l$ of mode $i$. + +- $\Lambda \in \mathbb{Q}_+^{E}$ — edge (frequency) capacities; $\Lambda_e$ bounds the total frequency of lines using $e \in E$. + +- $\boldsymbol{\tau} \in \mathbb{Q}_+^{A}$ — traveling time on arc $a \in A$; $\tau_p := \sum_{a \in p} \tau_a$ is the traveling time on path $p$. + +- $F$ — global upper bound on the frequency of any single line, assumed $F \ge \Lambda_e$ for all $e \in E$. + +## Decision Variables + +- $y_p \in \mathbb{R}_+$ — flow of passengers traveling from $s$ to $t$ on path $p \in \mathcal{P}_{st}$. + +- $f_l \in \mathbb{R}_+$ — frequency of line $l \in \mathcal{L}$. + +- $x_l \in \{0,1\}$ — decision variable for using line $l \in \mathcal{L}$. + +## Objective + +$$\text{(LPP)}\qquad +\min \ \boldsymbol{\tau}^{\mathsf T}\mathbf{y} + \mathbf{C}^{\mathsf T}\mathbf{x} + \mathbf{c}^{\mathsf T}\mathbf{f}$$ where $\boldsymbol{\tau}^{\mathsf T}\mathbf{y}$ is total passenger traveling time, $\mathbf{C}^{\mathsf T}\mathbf{x}$ is the fixed line set-up cost, and $\mathbf{c}^{\mathsf T}\mathbf{f}$ is the variable operating cost of lines at frequencies $\mathbf{f}$. + +## Constraints + +Using the notation $\mathbf{v}(I) := \sum_{i \in I} v_i$ for a vector $\mathbf{v}$ indexed by a set $I$: $$\begin{align} + \mathbf{y}(\mathcal{P}_{st}) &= d_{st} & &\forall\,(s,t) \in D \tag{1}\\[2pt] + \mathbf{y}(\mathcal{P}_a) - \sum_{l:\, e(a) \in l} \kappa_l f_l &\le 0 & &\forall\, a \in A \tag{2}\\[2pt] + \mathbf{f}(\mathcal{L}_e) &\le \Lambda_e & &\forall\, e \in E \tag{3}\\[2pt] + \mathbf{f} &\le F\mathbf{x} & & \tag{4}\\[2pt] + x_l &\in \{0,1\} & &\forall\, l \in \mathcal{L} \tag{5}\\[2pt] + f_l &\ge 0 & &\forall\, l \in \mathcal{L} \tag{6}\\[2pt] + y_p &\ge 0 & &\forall\, p \in \mathcal{P}. \tag{7} +\end{align}$$ Constraints (1) and (7) model a multicommodity flow problem routing the demand; (2) are the capacity constraints linking passenger paths with line capacity on each arc; (3) are the frequency (edge-capacity) constraints; (4) are the linking constraints forcing $f_l = 0$ whenever line $l$ is not used ($f_l \le F x_l$). diff --git a/borndorfer2007/problem_description.txt b/borndorfer2007/problem_description.txt new file mode 100644 index 0000000000000000000000000000000000000000..96908728116042d87c83f19ecfe2433e615f18b1 --- /dev/null +++ b/borndorfer2007/problem_description.txt @@ -0,0 +1,9 @@ +# Problem Description + +A public transit authority operates a multimodal transportation network consisting of a set of nodes connected by undirected edges. The network supports one or more transportation modes such as bus, tram, or subway, and each edge belongs to exactly one mode. For each mode, a designated subset of nodes serves as terminals where lines of that mode may begin and end. A line is a simple path (no repeated nodes) through the subnetwork of its mode, connecting two distinct terminals of that mode. Lines may be subject to a maximum length measured in number of edges. Each undirected edge in the network is associated with a traveling time, an operating cost per unit frequency, and an edge capacity that limits the total frequency of all lines using that edge. Each mode has a fixed cost incurred for establishing any line of that mode and a vehicle capacity specifying how many passengers a single run of a vehicle on that mode can carry. A global upper bound on the frequency of any individual line is also given, and this bound is at least as large as every edge capacity in the network. + +Passenger travel demand is specified by an origin-destination matrix: for each ordered pair of nodes with positive demand, the matrix gives the number of passengers wishing to travel from the origin to the destination within the planning horizon. The network's undirected edges induce a directed passenger route graph in which each undirected edge is replaced by two antiparallel directed arcs, each carrying the same traveling time as the original edge. A passenger path for a given origin-destination pair is any directed path from the origin to the destination in this directed graph, and the travel time of a passenger path is the sum of the arc travel times along it. + +The transit authority must simultaneously choose which lines to operate, at what frequency to run each chosen line, and how to route all passenger demand through the network. Specifically, the authority selects a subset of valid lines to activate, assigns a nonnegative frequency to each active line, and distributes the passengers of every origin-destination pair across directed paths connecting that pair. The frequency of a line that is not activated must be zero. For every origin-destination pair, the total passenger flow across all paths connecting that pair must equal the given demand. On every directed arc, the total passenger flow must not exceed the total transportation capacity provided by the lines covering the corresponding undirected edge, where each line contributes its mode's vehicle capacity multiplied by its frequency. On every undirected edge, the sum of the frequencies of all lines using that edge must not exceed the edge capacity. + +The goal is to minimize a weighted combination of two competing costs. The first component is the total passenger travel time, computed as the sum over all passenger paths of the flow on that path multiplied by the path's travel time. The second component is the total line cost, which itself consists of two parts: the sum of fixed costs over all activated lines, and the sum over all lines of the line's operating cost (the sum of per-edge operating costs along the line) multiplied by its frequency. A weighting parameter between zero and one controls the tradeoff: the quantity to minimize equals the weighting parameter times the total line cost, plus one minus the weighting parameter times the total passenger travel time. diff --git a/borndorfer2007/solution_logger.py b/borndorfer2007/solution_logger.py new file mode 100644 index 0000000000000000000000000000000000000000..5393fa935ce71df9da95cee04fe5199077cb8461 --- /dev/null +++ b/borndorfer2007/solution_logger.py @@ -0,0 +1,61 @@ +""" +Convergence logger for optimization algorithms. + +Records incumbent solutions with timestamps to a JSONL file. +This module is provided to LLM-generated programs — they only need to call +`log(objective_value)` whenever a better feasible solution is found. + +Usage in generated code: + from solution_logger import SolutionLogger + logger = SolutionLogger(log_path, sense="minimize") # or "maximize" + # ... inside algorithm loop: + logger.log(objective_value) +""" + +import json +import time + + +class SolutionLogger: + def __init__(self, log_path, sense="minimize"): + """ + Args: + log_path: Path to the JSONL output file. + sense: "minimize" or "maximize". + """ + self.log_path = log_path + self.sense = sense + self.start_time = time.time() + self.best_obj = None + self.min_interval = 0.1 # seconds, avoid excessive writes + + self._last_log_time = 0.0 + # Clear the file + with open(self.log_path, "w") as f: + pass + + def log(self, objective_value): + """Record a new incumbent if it improves on the best known.""" + if objective_value is None: + return + + # Check if this is an improvement + if self.best_obj is not None: + if self.sense == "minimize" and objective_value >= self.best_obj: + return + if self.sense == "maximize" and objective_value <= self.best_obj: + return + + elapsed = time.time() - self.start_time + + # Throttle writes + if self.best_obj is not None and elapsed - self._last_log_time < self.min_interval: + self.best_obj = objective_value + return + + self.best_obj = objective_value + self._last_log_time = elapsed + + with open(self.log_path, "a") as f: + f.write(json.dumps({"time": round(elapsed, 3), + "objective_value": objective_value}) + "\n") diff --git a/borndorfer2007/solution_schema.json b/borndorfer2007/solution_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..3ce24ca513546f9c66e0e8b9fd2fdf76651a2f18 --- /dev/null +++ b/borndorfer2007/solution_schema.json @@ -0,0 +1,20 @@ +{ + "objective_value": " Weighted combination of total line cost and total passenger travel time achieved by the solution.", + "active_lines": [ + { + "line_index": " Identifier of this line within the enumerated set of candidate lines.", + "mode": " Transportation mode operating this line.", + "nodes": " Ordered sequence of station identifiers visited by this line from one terminal to the other.", + "edges": " Ordered sequence of edge identifiers traversed by this line.", + "frequency": " Number of vehicle runs per planning period operated on this line." + } + ], + "active_passenger_paths": [ + { + "origin": " Origin station identifier of the OD pair carried by this path.", + "destination": " Destination station identifier of the OD pair carried by this path.", + "arcs": " Ordered sequence of directed arc identifiers traversed by this path from origin to destination.", + "flow": " Number of passengers routed along this path. The released certificate requires a non-empty complete path-flow witness so demand, capacity, and passenger travel cost can all be checked exactly." + } + ] +} diff --git a/bragin2022/feasibility_check.py b/bragin2022/feasibility_check.py new file mode 100644 index 0000000000000000000000000000000000000000..66ab67e250baf4ad58135e28ad6aef118f627e07 --- /dev/null +++ b/bragin2022/feasibility_check.py @@ -0,0 +1,376 @@ +#!/usr/bin/env python3 +""" +Feasibility checker for the Generalized Assignment Problem (GAP) +from Bragin & Tucker (2022). + +Constraints (numbered top-to-bottom from the formulation): + Constraint 1: sum_i x[i][j] = 1 for all j (each job assigned to exactly one machine) + Constraint 2: sum_j a[i][j] * x[i][j] <= b[i] for all i (machine capacity) + Constraint 3: x[i][j] in {0, 1} (binary integrality) + Constraint 4: reported objective_value matches sum_{i,j} c[i][j] * x[i][j] + (Tier C obj-consistency check — full recompute, since the + solution carries every variable the objective depends on) +""" + +import argparse +import json + +# FRONTIEROR_RELEASE_CONTRACT_V2 +_FRONTIEROR_REQUIRED_SOLUTION_FIELDS = ('assignments', 'objective_value') +_FRONTIEROR_NONEMPTY_SOLUTION_FIELDS = () +_FRONTIEROR_DERIVED_FIELD_RULES = () +_FRONTIEROR_CONDITIONAL_FIELD_RULES = () + + +def _frontieror_validate_solution_contract(solution): + """Return a structured rejection when the public solution contract is absent.""" + violations = [] + if not isinstance(solution, dict): + violations.append("solution must be a JSON object") + else: + missing = [ + field + for field in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS + if field not in solution or solution[field] is None + ] + if missing: + violations.append( + "missing required solution field(s): " + ", ".join(missing) + ) + + for discriminator, expected_value, fields in _FRONTIEROR_CONDITIONAL_FIELD_RULES: + if solution.get(discriminator) != expected_value: + continue + conditional_missing = [ + field + for field in fields + if field not in solution or solution[field] is None + ] + if conditional_missing: + violations.append( + "missing conditionally required solution field(s): " + + ", ".join(conditional_missing) + ) + + empty = [ + field + for field in _FRONTIEROR_NONEMPTY_SOLUTION_FIELDS + if field in solution and solution[field] in (None, [], {}) + ] + if empty: + violations.append( + "empty required decision witness field(s): " + ", ".join(empty) + ) + + for target, operation, source, source_key in _FRONTIEROR_DERIVED_FIELD_RULES: + try: + reported = float(solution[target]) + if operation == "length": + expected = float(len(solution[source])) + elif operation == "mapping_value": + expected = float(solution[source][source_key]) + else: + raise ValueError("unknown derived-field operation") + except (KeyError, TypeError, ValueError, OverflowError): + violations.append( + f"cannot derive {target} from decision witness field {source}" + ) + continue + if ( + reported != reported + or expected != expected + or reported in (float("inf"), float("-inf")) + or expected in (float("inf"), float("-inf")) + or abs(reported - expected) > 1e-6 + ): + violations.append( + f"{target}={reported} does not match the decision-derived " + f"value {expected}" + ) + + if "objective_value" in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS: + objective = solution.get("objective_value") + if ( + isinstance(objective, bool) + or not isinstance(objective, (int, float)) + or objective != objective + or objective in (float("inf"), float("-inf")) + ): + violations.append("objective_value must be a finite JSON number") + + if not violations: + return None + return { + "feasible": False, + "violated_constraints": ["solution_schema"], + "violations": violations, + "violation_magnitudes": [], + } + + +def _frontieror_write_unexpected_rejection(error): + """Turn malformed candidate/checker exceptions into a normal rejection.""" + import json as _frontieror_json + import sys as _frontieror_sys + + result_path = None + for index, argument in enumerate(_frontieror_sys.argv): + if argument == "--result_path" and index + 1 < len(_frontieror_sys.argv): + result_path = _frontieror_sys.argv[index + 1] + break + if argument.startswith("--result_path="): + result_path = argument.split("=", 1)[1] + break + if not result_path: + return False + try: + with open(result_path, "w") as result_handle: + _frontieror_json.dump( + { + "feasible": False, + "violated_constraints": ["malformed_solution"], + "violations": [ + "checker rejected malformed candidate: " + + type(error).__name__ + + ": " + + str(error)[:500] + ], + "violation_magnitudes": [], + }, + result_handle, + indent=2, + ) + except Exception: + return False + return True + + +TOL = 1e-5 +EPS = 1e-5 + + +def check_feasibility(instance, solution): + num_machines = instance["num_machines"] + num_jobs = instance["num_jobs"] + cost = instance["cost_matrix"] # c[i][j] + resource = instance["resource_matrix"] # a[i][j] + capacity = instance["capacities"] # b[i] + + assignments = solution.get("assignments", {}) + + violated_constraints = set() + violations = [] + violation_magnitudes = [] + + # Build assignment matrix from solution + # assignments maps str(job_index) -> machine_index + x = [[0] * num_jobs for _ in range(num_machines)] + assigned_jobs = set() + for job_str, machine in assignments.items(): + j = int(job_str) + i = int(machine) + if 0 <= i < num_machines and 0 <= j < num_jobs: + x[i][j] = 1 + assigned_jobs.add(j) + + # ------------------------------------------------------------------------- + # Constraint 1: sum_i x[i][j] = 1 for all j (assignment equality) + # ------------------------------------------------------------------------- + unassigned_jobs = [] + multi_assigned_jobs = [] + for j in range(num_jobs): + lhs = sum(x[i][j] for i in range(num_machines)) + rhs = 1.0 + violation_amount = abs(lhs - rhs) + if violation_amount > TOL: + violated_constraints.add(1) + normalizer = max(abs(rhs), EPS) + violation_magnitudes.append({ + "constraint": 1, + "lhs": float(lhs), + "rhs": rhs, + "raw_excess": violation_amount, + "normalizer": normalizer, + "ratio": violation_amount / normalizer + }) + if lhs == 0: + unassigned_jobs.append(j) + else: + multi_assigned_jobs.append(j) + + if unassigned_jobs: + violations.append( + f"Constraint 1 violated: jobs {unassigned_jobs} are not assigned to any machine" + ) + if multi_assigned_jobs: + violations.append( + f"Constraint 1 violated: jobs {multi_assigned_jobs} are assigned to multiple machines" + ) + + # ------------------------------------------------------------------------- + # Constraint 2: sum_j a[i][j] * x[i][j] <= b[i] for all i (capacity) + # ------------------------------------------------------------------------- + capacity_violated_machines = [] + for i in range(num_machines): + lhs = sum(resource[i][j] * x[i][j] for j in range(num_jobs)) + rhs = float(capacity[i]) + violation_amount = max(lhs - rhs, 0.0) + if violation_amount > TOL: + violated_constraints.add(2) + normalizer = max(abs(rhs), EPS) + violation_magnitudes.append({ + "constraint": 2, + "lhs": float(lhs), + "rhs": rhs, + "raw_excess": violation_amount, + "normalizer": normalizer, + "ratio": violation_amount / normalizer + }) + capacity_violated_machines.append( + f"machine {i} (usage {lhs} > capacity {rhs})" + ) + + if capacity_violated_machines: + violations.append( + f"Constraint 2 violated: capacity exceeded on {', '.join(capacity_violated_machines)}" + ) + + # ------------------------------------------------------------------------- + # Constraint 3: x[i][j] in {0, 1} (binary integrality) + # ------------------------------------------------------------------------- + non_binary_vars = [] + for job_str, machine in assignments.items(): + j = int(job_str) + i = int(machine) + # Check that machine index is valid + if i < 0 or i >= num_machines: + violated_constraints.add(3) + rhs_val = float(num_machines - 1) + normalizer = max(abs(rhs_val), EPS) + violation_amount = abs(i - max(0, min(i, num_machines - 1))) + violation_magnitudes.append({ + "constraint": 3, + "lhs": float(i), + "rhs": rhs_val, + "raw_excess": float(violation_amount), + "normalizer": normalizer, + "ratio": float(violation_amount) / normalizer + }) + non_binary_vars.append(f"x[{i}][{j}] has invalid machine index") + if j < 0 or j >= num_jobs: + violated_constraints.add(3) + rhs_val = float(num_jobs - 1) + normalizer = max(abs(rhs_val), EPS) + violation_amount = abs(j - max(0, min(j, num_jobs - 1))) + violation_magnitudes.append({ + "constraint": 3, + "lhs": float(j), + "rhs": rhs_val, + "raw_excess": float(violation_amount), + "normalizer": normalizer, + "ratio": float(violation_amount) / normalizer + }) + non_binary_vars.append(f"x[{i}][{j}] has invalid job index") + + if non_binary_vars: + violations.append( + f"Constraint 3 violated: {', '.join(non_binary_vars)}" + ) + + # ------------------------------------------------------------------------- + # Constraint 4: objective consistency (Tier C defense). + # Recompute true_obj = sum_{i,j} c[i][j] * x[i][j] from the solution + # variables and compare to the reported objective_value. The GAP solution + # carries every obj-determining variable (the full assignment dict), so a + # full recompute applies. Tolerance: max(1e-3 absolute, 1e-3 relative) + # plus a 0.5 integer-floor since cost coefficients are integers in this + # benchmark and any genuine LP/MIP-precision deviation stays well below + # half a unit, while LLM exploits typically lie by orders of magnitude. + # ------------------------------------------------------------------------- + reported_raw = solution.get("objective_value") + if reported_raw is not None: + try: + reported = float(reported_raw) + except (TypeError, ValueError): + reported = None + if reported is not None: + true_obj = float(sum( + cost[i][j] * x[i][j] + for i in range(num_machines) for j in range(num_jobs) + )) + abs_diff = abs(reported - true_obj) + tol = max(0.5, 1e-3, 1e-3 * abs(true_obj)) + if abs_diff > tol: + violated_constraints.add(4) + normalizer = max(abs(true_obj), EPS) + violation_magnitudes.append({ + "constraint": 4, + "lhs": float(reported), + "rhs": float(true_obj), + "raw_excess": float(abs_diff), + "normalizer": float(normalizer), + "ratio": float(abs_diff) / float(normalizer), + }) + violations.append( + f"Constraint 4 violated: reported objective_value={reported} " + f"differs from recomputed sum_(i,j) c[i][j]*x[i][j]={true_obj} " + f"(|diff|={abs_diff:.6g}, tol={tol:.6g})" + ) + + # ------------------------------------------------------------------------- + # Build result + # ------------------------------------------------------------------------- + feasible = len(violated_constraints) == 0 + return { + "feasible": feasible, + "violated_constraints": sorted(violated_constraints), + "violations": violations, + "violation_magnitudes": violation_magnitudes, + } + + +def main(): + parser = argparse.ArgumentParser(description="Check feasibility of a GAP solution") + parser.add_argument("--instance_path", type=str, required=True, + help="Path to the JSON instance file") + parser.add_argument("--solution_path", type=str, required=True, + help="Path to the JSON solution file") + parser.add_argument("--result_path", type=str, required=True, + help="Path to write the JSON feasibility result") + args = parser.parse_args() + + with open(args.instance_path, "r") as f: + instance = json.load(f) + with open(args.solution_path, "r") as f: + solution = json.load(f) + _frontieror_contract_result = _frontieror_validate_solution_contract(solution) + if _frontieror_contract_result is not None: + with open(args.result_path, "w") as _frontieror_result_handle: + json.dump( + _frontieror_contract_result, + _frontieror_result_handle, + indent=2, + ) + return + + result = check_feasibility(instance, solution) + + with open(args.result_path, "w") as f: + json.dump(result, f, indent=2) + + feasible = result["feasible"] + print(f"Feasibility: {feasible}") + if not feasible: + print(f"Violated constraints: {result['violated_constraints']}") + for v in result["violations"]: + print(f" - {v}") + + +if __name__ == "__main__": + try: + main() + except Exception as _frontieror_unexpected_error: + if not _frontieror_write_unexpected_rejection( + _frontieror_unexpected_error + ): + raise diff --git a/bragin2022/gurobi_code.py b/bragin2022/gurobi_code.py new file mode 100644 index 0000000000000000000000000000000000000000..5d6f82486da8aa29f1af0229cd66acdc32a0033f --- /dev/null +++ b/bragin2022/gurobi_code.py @@ -0,0 +1,120 @@ +#!/usr/bin/env python3 +""" +Gurobi implementation of the Generalized Assignment Problem (GAP) +from Bragin & Tucker (2022), "Surrogate 'Level-Based' Lagrangian Relaxation +for Mixed-Integer Linear Programming," Scientific Reports 12:22417. + +The GAP formulation: + min sum_i sum_j c[i][j] * x[i][j] + s.t. sum_i x[i][j] = 1 for all j (each job assigned to exactly one machine) + sum_j a[i][j] * x[i][j] <= b[i] for all i (machine capacity) + x[i][j] in {0, 1} +""" + +import argparse +import json +import gurobipy as gp +from gurobipy import GRB +import os as _os, sys as _sys +# Walk up from this file's directory to find repo root (containing scripts/). +_repo = _os.path.dirname(_os.path.abspath(__file__)) +while _repo != _os.path.dirname(_repo) and not _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _repo = _os.path.dirname(_repo) +if _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _sys.path.insert(0, _repo) +try: + from scripts.utils.gurobi_log_helper import install_gurobi_logger +except ImportError: + def install_gurobi_logger(log_path): # no-op fallback when scripts/ unavailable + pass +def main(): + parser = argparse.ArgumentParser(description="Solve GAP with Gurobi") + parser.add_argument("--instance_path", type=str, required=True, + help="Path to JSON instance file") + parser.add_argument("--solution_path", type=str, required=True, + help="Path to write solution JSON") + parser.add_argument("--time_limit", type=int, required=True, + help="Maximum solver runtime in seconds") + parser.add_argument("--log_path", type=str, default=None, help="Path to log incumbent solutions") + args = parser.parse_args() + install_gurobi_logger(args.log_path) + + # Load instance + with open(args.instance_path, "r") as f: + data = json.load(f) + + num_machines = data["num_machines"] # I + num_jobs = data["num_jobs"] # J + cost = data["cost_matrix"] # c[i][j], shape (I, J) + resource = data["resource_matrix"] # a[i][j], shape (I, J) + capacity = data["capacities"] # b[i], length I + + # Create model + model = gp.Model("GAP") + model.setParam("Threads", 1) + model.setParam("TimeLimit", args.time_limit) + + # Decision variables: x[i][j] binary + x = {} + for i in range(num_machines): + for j in range(num_jobs): + x[i, j] = model.addVar(vtype=GRB.BINARY, name=f"x_{i}_{j}") + + model.update() + + # Objective: minimize sum_i sum_j c[i][j] * x[i][j] + model.setObjective( + gp.quicksum(cost[i][j] * x[i, j] + for i in range(num_machines) + for j in range(num_jobs)), + GRB.MINIMIZE + ) + + # Constraint 1: Each job assigned to exactly one machine + for j in range(num_jobs): + model.addConstr( + gp.quicksum(x[i, j] for i in range(num_machines)) == 1, + name=f"assign_{j}" + ) + + # Constraint 2: Machine capacity + for i in range(num_machines): + model.addConstr( + gp.quicksum(resource[i][j] * x[i, j] for j in range(num_jobs)) <= capacity[i], + name=f"capacity_{i}" + ) + + # Solve + model.optimize() + + # Extract solution + solution = {} + if model.SolCount > 0: + objective_value = model.ObjVal + assignments = {} + for i in range(num_machines): + for j in range(num_jobs): + if x[i, j].X > 0.5: + assignments[str(j)] = i + solution["objective_value"] = objective_value + solution["assignments"] = assignments + solution["status"] = model.Status + solution["mip_gap"] = model.MIPGap if hasattr(model, "MIPGap") else None + else: + solution["objective_value"] = None + solution["status"] = model.Status + solution["assignments"] = {} + + # Write solution + with open(args.solution_path, "w") as f: + json.dump(solution, f, indent=2) + + print(f"Status: {model.Status}") + if model.SolCount > 0: + print(f"Objective value: {objective_value}") + else: + print("No feasible solution found.") + + +if __name__ == "__main__": + main() diff --git a/bragin2022/gurobi_feasi_result/large_feasi_result_1.json b/bragin2022/gurobi_feasi_result/large_feasi_result_1.json new file mode 100644 index 0000000000000000000000000000000000000000..e305b82c3d6732211d5033415df1a105476e9cf1 --- /dev/null +++ b/bragin2022/gurobi_feasi_result/large_feasi_result_1.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3269392edcbb7720882e77caadb88b4a9cf2fda04cf482cebd23baace4a531fd +size 102 diff --git a/bragin2022/gurobi_feasi_result/large_feasi_result_2.json b/bragin2022/gurobi_feasi_result/large_feasi_result_2.json new file mode 100644 index 0000000000000000000000000000000000000000..e305b82c3d6732211d5033415df1a105476e9cf1 --- /dev/null +++ b/bragin2022/gurobi_feasi_result/large_feasi_result_2.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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--- /dev/null +++ b/bragin2022/instance/large_instance_5.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:eeacf78f008c8cbd2ccbc8196d8165f2f89f7d29aa4b18394e4bd65f8d041663 +size 2533065 diff --git a/bragin2022/instance/tiny_instance.json b/bragin2022/instance/tiny_instance.json new file mode 100644 index 0000000000000000000000000000000000000000..3e9988ad06c4766b84e42bb62af9fe6c73d36ef0 --- /dev/null +++ b/bragin2022/instance/tiny_instance.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5f8ec9d42b085e93a7e292ecd8bda1902a5336c9237f4e1eb875c1eb5c69dfbf +size 10818 diff --git a/bragin2022/instance_schema.json b/bragin2022/instance_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..d638527d39b9bf5e504c77ae1cc476abb658cefa --- /dev/null +++ b/bragin2022/instance_schema.json @@ -0,0 +1,7 @@ +{ + "num_machines": " Number of machines available to process jobs.", + "num_jobs": " Number of jobs that must be assigned to machines.", + "cost_matrix": " Cost incurred when a specific job is assigned to a specific machine.", + "resource_matrix": " Amount of machine resource consumed when a specific job is assigned to a specific machine.", + "capacities": " Maximum total resource available on each machine." +} diff --git a/bragin2022/mathematical_formulation.md b/bragin2022/mathematical_formulation.md new file mode 100644 index 0000000000000000000000000000000000000000..58f27e0914818de1dbc7ffa3a0dcc3ec02258984 --- /dev/null +++ b/bragin2022/mathematical_formulation.md @@ -0,0 +1,38 @@ +# Original Formulation: Generalized Assignment Problem (GAP) + +*Source: Surrogate “Level-Based” Lagrangian Relaxation for Mixed-Integer Linear Programming, Mikhail A. Bragin & Emily L. Tucker, Scientific Reports 12:22417, 2022.* + +The MILP that the paper actually benchmarks in its “Generalized Assignment Problems” section is the classical Generalized Assignment Problem. The paper states (p. 8) that “large-scale instances of GAPs are considered (formulation is available in subsection 4.2 of Supplementary Information),” and the experiments (Table 5) report objective (feasible cost) values for this formulation on the OR-library / Yagiura instances of types D and E with 20, 40, and 80 machines and 1600 jobs. The general separable MILP of the main body (eqs. (1)–(2)) and the SLBLR Lagrangian-relaxation machinery (eqs. (3)–(22)) are solution methodology, not the problem definition, and are intentionally excluded below. + +## Sets and Indices + +- $I$ : set of machines, indexed by $i = 1,\dots,|I|$. + +- $J$ : set of jobs, indexed by $j = 1,\dots,|J|$. + +## Parameters + +- $c_{i,j}$ : cost of assigning job $j$ to machine $i$. + +- $a_{i,j}$ : amount of resource consumed when job $j$ is processed on machine $i$. + +- $b_{i}$ : resource capacity of machine $i$. + +## Decision Variables + +- $x_{i,j} \in \{0,1\}$ : equals $1$ if job $j$ is assigned to machine $i$, and $0$ otherwise, for all $i \in I,\ j \in J$. + +## Objective + +$$\begin{equation} + \min_{x}\ \sum_{i \in I} \sum_{j \in J} c_{i,j}\, x_{i,j} + \tag{1} +\end{equation}$$ + +## Constraints + +$$\begin{align} + \sum_{i \in I} x_{i,j} &= 1, && \forall\, j \in J, \tag{2}\\[2pt] + \sum_{j \in J} a_{i,j}\, x_{i,j} &\le b_{i}, && \forall\, i \in I, \tag{3}\\[2pt] + x_{i,j} &\in \{0,1\}, && \forall\, i \in I,\ \forall\, j \in J. \tag{4} +\end{align}$$ diff --git a/bragin2022/problem_description.txt b/bragin2022/problem_description.txt new file mode 100644 index 0000000000000000000000000000000000000000..406b20f678600aca12d2fbefc629b9497adfe2ca --- /dev/null +++ b/bragin2022/problem_description.txt @@ -0,0 +1,7 @@ +# Problem Description + +A finite set of jobs must each be assigned to exactly one machine drawn from a finite set of machines. Assigning a given job to a given machine incurs a known cost, specified for every (machine, job) pair. The total cost of an assignment is the sum, over all jobs, of the cost of the (machine, job) pair selected for that job. The goal is to choose an assignment that minimizes this total cost. + +Processing a given job on a given machine consumes a known amount of that machine's resource, specified for every (machine, job) pair. Each machine has a fixed resource capacity. For every machine, the total resource consumed by all the jobs assigned to that machine must not exceed the resource capacity of that machine. + +The decision for every (machine, job) pair is binary: it equals 1 if the job is assigned to that machine and 0 otherwise. Each job must be assigned to exactly one machine, so for every job exactly one (machine, job) pair takes the value 1. A machine may receive any number of jobs, provided the per-machine capacity requirement is satisfied. diff --git a/bragin2022/solution_logger.py b/bragin2022/solution_logger.py new file mode 100644 index 0000000000000000000000000000000000000000..5393fa935ce71df9da95cee04fe5199077cb8461 --- /dev/null +++ b/bragin2022/solution_logger.py @@ -0,0 +1,61 @@ +""" +Convergence logger for optimization algorithms. + +Records incumbent solutions with timestamps to a JSONL file. +This module is provided to LLM-generated programs — they only need to call +`log(objective_value)` whenever a better feasible solution is found. + +Usage in generated code: + from solution_logger import SolutionLogger + logger = SolutionLogger(log_path, sense="minimize") # or "maximize" + # ... inside algorithm loop: + logger.log(objective_value) +""" + +import json +import time + + +class SolutionLogger: + def __init__(self, log_path, sense="minimize"): + """ + Args: + log_path: Path to the JSONL output file. + sense: "minimize" or "maximize". + """ + self.log_path = log_path + self.sense = sense + self.start_time = time.time() + self.best_obj = None + self.min_interval = 0.1 # seconds, avoid excessive writes + + self._last_log_time = 0.0 + # Clear the file + with open(self.log_path, "w") as f: + pass + + def log(self, objective_value): + """Record a new incumbent if it improves on the best known.""" + if objective_value is None: + return + + # Check if this is an improvement + if self.best_obj is not None: + if self.sense == "minimize" and objective_value >= self.best_obj: + return + if self.sense == "maximize" and objective_value <= self.best_obj: + return + + elapsed = time.time() - self.start_time + + # Throttle writes + if self.best_obj is not None and elapsed - self._last_log_time < self.min_interval: + self.best_obj = objective_value + return + + self.best_obj = objective_value + self._last_log_time = elapsed + + with open(self.log_path, "a") as f: + f.write(json.dumps({"time": round(elapsed, 3), + "objective_value": objective_value}) + "\n") diff --git a/bragin2022/solution_schema.json b/bragin2022/solution_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..745df79a9abb1b183620b47e9a376cec56934f97 --- /dev/null +++ b/bragin2022/solution_schema.json @@ -0,0 +1,4 @@ +{ + "objective_value": " Total assignment cost across all jobs.", + "assignments": " Machine to which each job is assigned (0-indexed)." +} diff --git a/brandao2016/feasibility_check.py b/brandao2016/feasibility_check.py new file mode 100644 index 0000000000000000000000000000000000000000..88a23955bddc7870720d960f3cf57e376ed35f6d --- /dev/null +++ b/brandao2016/feasibility_check.py @@ -0,0 +1,406 @@ +#!/usr/bin/env python3 +""" +Feasibility checker for Bin Packing Problem (BPP) solutions based on +Brandao and Pedroso's General Arc-flow Formulation (Equations 17-22). + +Constraints numbered top-to-bottom from the formulation: + Constraint 1 (Eq 18): Flow conservation — each bin capacity <= W, num_bins consistent + Constraint 2 (Eq 19): Demand >= b_i for items NOT in J (b_i > 1) + Constraint 3 (Eq 20): Demand = b_i for items in J (b_i = 1) + Constraint 4 (Eq 21): Per-arc flow upper bound f_{uvi} <= b_i (per-bin item count <= b_i) + Constraint 5 (Eq 22): Non-negativity and integrality of flow variables + Constraint 6 (Eq 17): Objective consistency — reported objective_value must + equal the recomputed number of bins z = len(bins). +""" + +import argparse +import json +from collections import Counter + +# FRONTIEROR_RELEASE_CONTRACT_V2 +_FRONTIEROR_REQUIRED_SOLUTION_FIELDS = ('bins', 'num_bins', 'objective_value') +_FRONTIEROR_NONEMPTY_SOLUTION_FIELDS = () +_FRONTIEROR_DERIVED_FIELD_RULES = () +_FRONTIEROR_CONDITIONAL_FIELD_RULES = () + + +def _frontieror_validate_solution_contract(solution): + """Return a structured rejection when the public solution contract is absent.""" + violations = [] + if not isinstance(solution, dict): + violations.append("solution must be a JSON object") + else: + missing = [ + field + for field in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS + if field not in solution or solution[field] is None + ] + if missing: + violations.append( + "missing required solution field(s): " + ", ".join(missing) + ) + + for discriminator, expected_value, fields in _FRONTIEROR_CONDITIONAL_FIELD_RULES: + if solution.get(discriminator) != expected_value: + continue + conditional_missing = [ + field + for field in fields + if field not in solution or solution[field] is None + ] + if conditional_missing: + violations.append( + "missing conditionally required solution field(s): " + + ", ".join(conditional_missing) + ) + + empty = [ + field + for field in _FRONTIEROR_NONEMPTY_SOLUTION_FIELDS + if field in solution and solution[field] in (None, [], {}) + ] + if empty: + violations.append( + "empty required decision witness field(s): " + ", ".join(empty) + ) + + for target, operation, source, source_key in _FRONTIEROR_DERIVED_FIELD_RULES: + try: + reported = float(solution[target]) + if operation == "length": + expected = float(len(solution[source])) + elif operation == "mapping_value": + expected = float(solution[source][source_key]) + else: + raise ValueError("unknown derived-field operation") + except (KeyError, TypeError, ValueError, OverflowError): + violations.append( + f"cannot derive {target} from decision witness field {source}" + ) + continue + if ( + reported != reported + or expected != expected + or reported in (float("inf"), float("-inf")) + or expected in (float("inf"), float("-inf")) + or abs(reported - expected) > 1e-6 + ): + violations.append( + f"{target}={reported} does not match the decision-derived " + f"value {expected}" + ) + + if "objective_value" in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS: + objective = solution.get("objective_value") + if ( + isinstance(objective, bool) + or not isinstance(objective, (int, float)) + or objective != objective + or objective in (float("inf"), float("-inf")) + ): + violations.append("objective_value must be a finite JSON number") + + if not violations: + return None + return { + "feasible": False, + "violated_constraints": ["solution_schema"], + "violations": violations, + "violation_magnitudes": [], + } + + +def _frontieror_write_unexpected_rejection(error): + """Turn malformed candidate/checker exceptions into a normal rejection.""" + import json as _frontieror_json + import sys as _frontieror_sys + + result_path = None + for index, argument in enumerate(_frontieror_sys.argv): + if argument == "--result_path" and index + 1 < len(_frontieror_sys.argv): + result_path = _frontieror_sys.argv[index + 1] + break + if argument.startswith("--result_path="): + result_path = argument.split("=", 1)[1] + break + if not result_path: + return False + try: + with open(result_path, "w") as result_handle: + _frontieror_json.dump( + { + "feasible": False, + "violated_constraints": ["malformed_solution"], + "violations": [ + "checker rejected malformed candidate: " + + type(error).__name__ + + ": " + + str(error)[:500] + ], + "violation_magnitudes": [], + }, + result_handle, + indent=2, + ) + except Exception: + return False + return True + + + +def check_feasibility(instance, solution): + tol = 1e-5 + eps = 1e-5 + + W = instance["parameters"]["W"] + items_by_type = {} + for item in instance["items"]: + items_by_type[item["item_type"]] = { + "weight": item["weight"], + "demand": item["demand"], + } + + # J = {i : b_i = 1} + J = {t for t, info in items_by_type.items() if info["demand"] == 1} + + bins = solution["bins"] + num_bins = solution.get("num_bins", len(bins)) + + violated_constraints = set() + violations = [] + violation_magnitudes = [] + + # ========================================================================= + # Constraint 1 (Eq 18): Flow conservation + # ========================================================================= + if abs(num_bins - len(bins)) > tol: + violated_constraints.add(1) + lhs = float(len(bins)) + rhs = float(num_bins) + raw_excess = abs(lhs - rhs) + normalizer = max(abs(rhs), eps) + violations.append( + f"Stated num_bins ({num_bins}) does not match actual number of bins ({len(bins)})" + ) + violation_magnitudes.append({ + "constraint": 1, + "lhs": lhs, + "rhs": rhs, + "raw_excess": raw_excess, + "normalizer": normalizer, + "ratio": raw_excess / normalizer, + }) + + for b_idx, bin_items in enumerate(bins): + total_weight = 0.0 + for item_type in bin_items: + if item_type in items_by_type: + total_weight += items_by_type[item_type]["weight"] + lhs = total_weight + rhs = float(W) + violation_amount = lhs - rhs + if violation_amount > tol: + violated_constraints.add(1) + normalizer = max(abs(rhs), eps) + violations.append( + f"Bin {b_idx + 1} exceeds capacity: total weight {total_weight} > W={W}" + ) + violation_magnitudes.append({ + "constraint": 1, + "lhs": lhs, + "rhs": rhs, + "raw_excess": violation_amount, + "normalizer": normalizer, + "ratio": violation_amount / normalizer, + }) + + # ========================================================================= + # Count item usage across all bins + # ========================================================================= + usage = Counter() + for bin_items in bins: + for item_type in bin_items: + usage[item_type] += 1 + + # ========================================================================= + # Constraint 2 (Eq 19): For items NOT in J (b_i > 1), total usage >= b_i + # ========================================================================= + for item_type, info in items_by_type.items(): + if item_type in J: + continue + demand = info["demand"] + used = usage.get(item_type, 0) + lhs = float(used) + rhs = float(demand) + violation_amount = rhs - lhs + if violation_amount > tol: + violated_constraints.add(2) + normalizer = max(abs(rhs), eps) + violations.append( + f"Item type {item_type} (not in J): assigned {used} < demand {demand}" + ) + violation_magnitudes.append({ + "constraint": 2, + "lhs": lhs, + "rhs": rhs, + "raw_excess": violation_amount, + "normalizer": normalizer, + "ratio": violation_amount / normalizer, + }) + + # ========================================================================= + # Constraint 3 (Eq 20): For items in J (b_i = 1), total usage = b_i + # ========================================================================= + for item_type, info in items_by_type.items(): + if item_type not in J: + continue + demand = info["demand"] + used = usage.get(item_type, 0) + lhs = float(used) + rhs = float(demand) + violation_amount = abs(lhs - rhs) + if violation_amount > tol: + violated_constraints.add(3) + normalizer = max(abs(rhs), eps) + violations.append( + f"Item type {item_type} (in J): assigned {used} != demand {demand}" + ) + violation_magnitudes.append({ + "constraint": 3, + "lhs": lhs, + "rhs": rhs, + "raw_excess": violation_amount, + "normalizer": normalizer, + "ratio": violation_amount / normalizer, + }) + + # ========================================================================= + # Constraint 4 (Eq 21): f_{uvi} <= b_i for each item arc + # ========================================================================= + for b_idx, bin_items in enumerate(bins): + bin_counter = Counter(bin_items) + for item_type, count in bin_counter.items(): + if item_type not in items_by_type: + continue + demand = items_by_type[item_type]["demand"] + lhs = float(count) + rhs = float(demand) + violation_amount = lhs - rhs + if violation_amount > tol: + violated_constraints.add(4) + normalizer = max(abs(rhs), eps) + violations.append( + f"Bin {b_idx + 1}: item type {item_type} appears {count} times > demand {demand}" + ) + violation_magnitudes.append({ + "constraint": 4, + "lhs": lhs, + "rhs": rhs, + "raw_excess": violation_amount, + "normalizer": normalizer, + "ratio": violation_amount / normalizer, + }) + + # ========================================================================= + # Constraint 5 (Eq 22): Non-negativity and integrality + # ========================================================================= + for b_idx, bin_items in enumerate(bins): + for item_type in bin_items: + if item_type not in items_by_type: + violated_constraints.add(5) + violations.append( + f"Bin {b_idx + 1}: invalid item type {item_type} not in instance" + ) + violation_magnitudes.append({ + "constraint": 5, + "lhs": float(item_type), + "rhs": 0.0, + "raw_excess": 1.0, + "normalizer": 1.0, + "ratio": 1.0, + }) + + # ========================================================================= + # Constraint 6 (Eq 17): Objective consistency + # Reported objective_value must equal z = len(bins). The objective in + # Brandao & Pedroso's arc-flow formulation is simply the total number + # of bins used (an integer count), so we require equality up to 0.5 + # (any integer mismatch fires). + # ========================================================================= + reported_obj = solution.get("objective_value") + if reported_obj is not None: + try: + reported = float(reported_obj) + except (TypeError, ValueError): + reported = None + if reported is not None: + true_obj = float(len(bins)) + abs_diff = abs(reported - true_obj) + obj_tol = 0.5 # integer count: any mismatch >= 1 fires + if abs_diff > obj_tol: + violated_constraints.add(6) + normalizer = max(abs(true_obj), eps) + violations.append( + f"Objective consistency violated: reported objective_value=" + f"{reported} differs from recomputed z=len(bins)={true_obj} " + f"(|diff|={abs_diff:.3g}, tol={obj_tol})" + ) + violation_magnitudes.append({ + "constraint": 6, + "lhs": reported, + "rhs": true_obj, + "raw_excess": abs_diff, + "normalizer": normalizer, + "ratio": abs_diff / normalizer, + }) + + feasible = len(violated_constraints) == 0 + return { + "feasible": feasible, + "violated_constraints": sorted(violated_constraints), + "violations": violations, + "violation_magnitudes": violation_magnitudes, + } + + +def main(): + parser = argparse.ArgumentParser(description="Feasibility checker for BPP solutions") + parser.add_argument("--instance_path", type=str, required=True) + parser.add_argument("--solution_path", type=str, required=True) + parser.add_argument("--result_path", type=str, required=True) + args = parser.parse_args() + + with open(args.instance_path, "r") as f: + instance = json.load(f) + with open(args.solution_path, "r") as f: + solution = json.load(f) + _frontieror_contract_result = _frontieror_validate_solution_contract(solution) + if _frontieror_contract_result is not None: + with open(args.result_path, "w") as _frontieror_result_handle: + json.dump( + _frontieror_contract_result, + _frontieror_result_handle, + indent=2, + ) + return + + result = check_feasibility(instance, solution) + + with open(args.result_path, "w") as f: + json.dump(result, f, indent=2) + + print(f"Feasibility: {result['feasible']}") + if not result["feasible"]: + print(f"Violated constraints: {result['violated_constraints']}") + for v in result["violations"]: + print(f" - {v}") + + +if __name__ == "__main__": + try: + main() + except Exception as _frontieror_unexpected_error: + if not _frontieror_write_unexpected_rejection( + _frontieror_unexpected_error + ): + raise diff --git a/brandao2016/gurobi_code.py b/brandao2016/gurobi_code.py new file mode 100644 index 0000000000000000000000000000000000000000..908e3470df99e6a1b942730f77d408b5180960a9 --- /dev/null +++ b/brandao2016/gurobi_code.py @@ -0,0 +1,440 @@ +#!/usr/bin/env python3 +""" +Gurobi implementation of the General Arc-flow Formulation with Graph Compression +for Bin Packing and Related Problems. + +Based on: Brandao and Pedroso (2016) - "Bin Packing and Related Problems: + General Arc-flow Formulation with Graph Compression" + +This implements the arc-flow MIP formulation (Equations 17-22) over a compressed +graph built via Algorithm 1 (direct Step-3 construction + Step-4 compression). +""" + +import argparse +import json +import sys +from collections import defaultdict + +import gurobipy as gp +from gurobipy import GRB +import os as _os, sys as _sys +# Walk up from this file's directory to find repo root (containing scripts/). +_repo = _os.path.dirname(_os.path.abspath(__file__)) +while _repo != _os.path.dirname(_repo) and not _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _repo = _os.path.dirname(_repo) +if _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _sys.path.insert(0, _repo) +try: + from scripts.utils.gurobi_log_helper import install_gurobi_logger +except ImportError: + def install_gurobi_logger(log_path): # no-op fallback when scripts/ unavailable + pass + + +def load_instance(path): + """Load a BPP instance from JSON.""" + with open(path, "r") as f: + data = json.load(f) + items = [] + for item in data["items"]: + items.append({ + "type": item["item_type"], + "weight": item["weight"], + "demand": item["demand"], + }) + W = data["parameters"]["W"] + return items, W, data + + +def preprocess_items(items, W): + """ + Sort items in decreasing order by normalized weight (alpha_i = w_i / W). + Ties broken by decreasing weight (lexicographic in 1D). + Returns sorted items with original type mapping. + """ + # For 1D: alpha_i = w_i / W + items_sorted = sorted(items, key=lambda it: (-it["weight"] / W, -it["weight"])) + return items_sorted + + +def build_arc_flow_graph(items, W): + """ + Build the compressed arc-flow graph using Algorithm 1 (direct Step-3 construction). + + For 1D bin packing: + - States: (x, i, c) where x = space used, i = current item index, c = copies used + - Lifting: for each state, compute the highest position reachable + - Memoization on (lifted_x, i, c) + + Returns: (arcs, vertices, source_label, target_label) + where arcs is a list of (u, v, item_index) with item_index 0-based (0 = loss arc uses index -1 here, + we use item_index = -1 for loss arcs to distinguish from item 0). + """ + m = len(items) + weights = [it["weight"] for it in items] + demands = [it["demand"] for it in items] + + arcs = [] # list of (u, v, item_idx) where item_idx is 0-based, -1 for loss + vertices = set() + + # Memoization table: (x, i, c) -> node_label + dp = {} + + def highest_position(x, i, c): + """ + Compute the highest position (Step-3 lifting) for 1D case. + Solve: minimize W - sum_{j>=i} w_j * y_j + s.t. sum_{j>=i} w_j * y_j <= W - x + y_j <= b_j for j > i (or b_i - c for j == i) + y_j >= 0, integer + + This is equivalent to: W - maxFill, where maxFill is the max weight + we can pack from items i..m-1 into remaining capacity W - x. + + We solve this greedily / via a simple bounded knapsack approach. + + **INFERRED ASSUMPTION**: The paper says "solving one-dimensional knapsack + problems" for lifting. We use a DP-based bounded knapsack solver for exactness. + """ + remaining = W - x + if remaining <= 0: + return W + + # Simple DP for bounded knapsack to maximize total weight + # Items from index i to m-1, with adjusted bounds + sub_items = [] + for j in range(i, m): + bound = demands[j] - c if j == i else demands[j] + if bound > 0 and weights[j] <= remaining: + sub_items.append((weights[j], bound)) + + if not sub_items: + return W - 0 # can't pack anything more => highest pos = W + + # DP: max weight packable into capacity 'remaining'. + # Always use the exact bounded-knapsack DP (no greedy fallback). + dp_knap = [0] * (remaining + 1) + for (w, b) in sub_items: + if b == 1: + # 0-1 item: iterate backwards + for cap in range(remaining, w - 1, -1): + dp_knap[cap] = max(dp_knap[cap], dp_knap[cap - w] + w) + else: + # Bounded item: binary decomposition + k = 1 + left = b + while left > 0: + take = min(k, left) + tw = take * w + for cap in range(remaining, tw - 1, -1): + dp_knap[cap] = max(dp_knap[cap], dp_knap[cap - tw] + tw) + left -= take + k *= 2 + max_fill = dp_knap[remaining] + + return W - max_fill + + def build(x, i, c): + """ + Recursive graph construction (Algorithm 1). + Returns the node label for state (x, i, c). + """ + # Step 1: Lift state + x = highest_position(x, i, c) + + key = (x, i, c) + if key in dp: + return dp[key] + + # Initialize node label to W (target) + u = W + + up_x = None + # Option 1: Skip current item (move to next item type) + if i < m - 1: + up_x = build(x, i + 1, 0) + u = up_x + + # Option 2: Use one more copy of item i + if c < demands[i] and x + weights[i] <= W: + v = build(x + weights[i], i, c + 1) + u = min(u, v - weights[i]) + # Add item arc + arcs.append((u, v, i)) + vertices.add(u) + vertices.add(v) + # Add loss arc between levels if needed + if i < m - 1 and up_x is not None and u != up_x: + arcs.append((u, up_x, -1)) # loss arc + vertices.add(up_x) + + dp[key] = u + return u + + # Build graph starting from initial state + sys.setrecursionlimit(100000) + source = build(0, 0, 0) + target = W + + vertices.add(source) + vertices.add(target) + + # Add loss arcs from all internal nodes to target + for v in list(vertices): + if v != source and v != target: + arcs.append((v, target, -1)) + + return arcs, vertices, source, target + + +def apply_step4_compression(arcs, vertices, source, target, items): + """ + Step-4 compression: relabel nodes using longest path from source. + + psi(v) = 0 if v = source + psi(v) = max over incoming arcs (u,v,i) of {psi(u) + w_i} otherwise + where w_i = 0 for loss arcs. + """ + weights = [it["weight"] for it in items] + + # Build adjacency list for incoming edges + incoming = defaultdict(list) # node -> list of (from_node, item_idx) + all_nodes = set() + for (u, v, idx) in arcs: + incoming[v].append((u, idx)) + all_nodes.add(u) + all_nodes.add(v) + + # Build adjacency list for outgoing edges (for topological sort) + outgoing = defaultdict(list) + in_degree = defaultdict(int) + for node in all_nodes: + in_degree[node] = 0 + for (u, v, idx) in arcs: + outgoing[u].append(v) + in_degree[v] += 1 + + # Topological sort (Kahn's algorithm) + from collections import deque + queue = deque() + for node in all_nodes: + if in_degree[node] == 0: + queue.append(node) + + topo_order = [] + while queue: + node = queue.popleft() + topo_order.append(node) + for neighbor in outgoing[node]: + in_degree[neighbor] -= 1 + if in_degree[neighbor] == 0: + queue.append(neighbor) + + # Compute psi (longest path from source) via topological order. + psi = {} + psi[source] = 0 + for node in topo_order: + if node not in psi: + psi[node] = 0 + for (u, idx) in incoming[node]: + if u in psi: + w = 0 if idx == -1 else weights[idx] + psi[node] = max(psi[node], psi[u] + w) + + # Relabel: merge nodes with same psi value + new_arcs = set() + for (u, v, idx) in arcs: + nu = psi.get(u, u) + nv = psi.get(v, v) + if nu != nv: # skip self-loops + new_arcs.add((nu, nv, idx)) + + new_source = psi.get(source, 0) + new_target = psi.get(target, target) + new_vertices = set() + for (u, v, idx) in new_arcs: + new_vertices.add(u) + new_vertices.add(v) + new_vertices.add(new_source) + new_vertices.add(new_target) + + return list(new_arcs), new_vertices, new_source, new_target + + +def solve_with_gurobi(arcs, vertices, source, target, items, W, time_limit): + """ + Build and solve the arc-flow MIP formulation (Equations 17-22). + """ + m_items = len(items) + demands = [it["demand"] for it in items] + + # J = {i : b_i = 1} (0-indexed) + J = set(i for i in range(m_items) if demands[i] == 1) + + model = gp.Model("ArcFlowBPP") + + # Solver parameters as specified in the paper (Section 6) + model.setParam("TimeLimit", time_limit) + model.setParam("Threads", 1) + model.setParam("Presolve", 1) + model.setParam("Method", 2) + model.setParam("MIPFocus", 1) + model.setParam("Heuristics", 1.0) + model.setParam("MIPGap", 0) + model.setParam("MIPGapAbs", 1 - 1e-5) + + # Decision variables + # z: number of bins + z = model.addVar(vtype=GRB.INTEGER, name="z", lb=0) + + # f_{uvi}: flow on arc (u, v, i) + f = {} + for idx, (u, v, item_idx) in enumerate(arcs): + ub = GRB.INFINITY + if item_idx >= 0: + ub = demands[item_idx] + f[idx] = model.addVar( + vtype=GRB.INTEGER, name=f"f_{idx}", lb=0, ub=ub + ) + + model.update() + + # Objective (17): minimize z + model.setObjective(z, GRB.MINIMIZE) + + # Constraint (18): Flow conservation + # For each node k: sum(inflow) - sum(outflow) = -z (source), z (target), 0 (other) + node_in = defaultdict(list) # node -> list of arc indices (incoming) + node_out = defaultdict(list) # node -> list of arc indices (outgoing) + for idx, (u, v, item_idx) in enumerate(arcs): + node_out[u].append(idx) + node_in[v].append(idx) + + for node in vertices: + inflow = gp.quicksum(f[idx] for idx in node_in.get(node, [])) + outflow = gp.quicksum(f[idx] for idx in node_out.get(node, [])) + if node == source: + model.addConstr(inflow - outflow == -z, name=f"flow_source") + elif node == target: + model.addConstr(inflow - outflow == z, name=f"flow_target") + else: + model.addConstr(inflow - outflow == 0, name=f"flow_{node}") + + # Constraints (19) and (20): Demand satisfaction + # Group arcs by item type + item_arcs = defaultdict(list) # item_idx -> list of arc indices + for idx, (u, v, item_idx) in enumerate(arcs): + if item_idx >= 0: + item_arcs[item_idx].append(idx) + + for i in range(m_items): + total_flow = gp.quicksum(f[idx] for idx in item_arcs.get(i, [])) + if i in J: + # Constraint (20): equality + model.addConstr(total_flow == demands[i], name=f"demand_eq_{i}") + else: + # Constraint (19): inequality (>=) + model.addConstr(total_flow >= demands[i], name=f"demand_geq_{i}") + + # Solve + model.optimize() + + # Extract solution + obj_val = None + bin_assignments = [] + + if model.SolCount > 0: + obj_val = model.ObjVal + + # Flow decomposition to recover bin assignments + flow_values = {} + for idx, (u, v, item_idx) in enumerate(arcs): + val = round(f[idx].X) + if val > 0: + flow_values[idx] = val + + # Decompose flow into paths from source to target + # Build residual adjacency + residual_out = defaultdict(list) + for idx in flow_values: + u, v, item_idx = arcs[idx] + residual_out[u].append((v, item_idx, idx)) + + total_bins = round(z.X) + for _ in range(total_bins): + path_items = [] + current = source + while current != target: + found = False + for (v, item_idx, arc_idx) in residual_out[current]: + if flow_values.get(arc_idx, 0) > 0: + flow_values[arc_idx] -= 1 + if flow_values[arc_idx] == 0: + del flow_values[arc_idx] + if item_idx >= 0: + path_items.append(items[item_idx]["type"]) + current = v + found = True + break + if not found: + break + # Append every path so len(bin_assignments) == round(z.X); + # paths consisting only of loss arcs yield empty bins. + bin_assignments.append(path_items) + + return obj_val, bin_assignments, model.Status + + +def main(): + parser = argparse.ArgumentParser( + description="Arc-flow formulation for Bin Packing (Gurobi solver)" + ) + parser.add_argument("--instance_path", type=str, required=True, + help="Path to the JSON instance file") + parser.add_argument("--solution_path", type=str, required=True, + help="Path for the output solution JSON file") + parser.add_argument("--time_limit", type=int, required=True, + help="Maximum solver runtime in seconds") + parser.add_argument("--log_path", type=str, default=None, help="Path to log incumbent solutions") + args = parser.parse_args() + install_gurobi_logger(args.log_path) + + # Load instance + items, W, instance_data = load_instance(args.instance_path) + + # Preprocess: sort items + items_sorted = preprocess_items(items, W) + + # Build compressed arc-flow graph + arcs, vertices, source, target = build_arc_flow_graph(items_sorted, W) + + # Apply Step-4 compression + arcs, vertices, source, target = apply_step4_compression( + arcs, vertices, source, target, items_sorted + ) + + # Solve MIP + obj_val, bin_assignments, status = solve_with_gurobi( + arcs, vertices, source, target, items_sorted, W, args.time_limit + ) + + # Build solution output + solution = { + "objective_value": obj_val, + "status": "optimal" if status == GRB.OPTIMAL else + "time_limit" if status == GRB.TIME_LIMIT else + "feasible" if obj_val is not None else "infeasible", + "num_bins": int(round(obj_val)) if obj_val is not None else None, + "bins": bin_assignments, + } + + with open(args.solution_path, "w") as f: + json.dump(solution, f, indent=2) + + print(f"Solution written to {args.solution_path}") + if obj_val is not None: + print(f"Objective value (bins used): {obj_val}") + + +if __name__ == "__main__": + main() diff --git a/brandao2016/gurobi_feasi_result/large_feasi_result_1.json b/brandao2016/gurobi_feasi_result/large_feasi_result_1.json new file mode 100644 index 0000000000000000000000000000000000000000..e305b82c3d6732211d5033415df1a105476e9cf1 --- /dev/null +++ b/brandao2016/gurobi_feasi_result/large_feasi_result_1.json @@ 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a/brandao2016/instance/tiny_instance.json b/brandao2016/instance/tiny_instance.json new file mode 100644 index 0000000000000000000000000000000000000000..9a29ba548a10550029447e6e72ba6bc80a0cea1b --- /dev/null +++ b/brandao2016/instance/tiny_instance.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:709511fd5c03a481962ef364d81505b82df6c1a7272d142026a9e79dfa0eaff6 +size 3223 diff --git a/brandao2016/instance_schema.json b/brandao2016/instance_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..505413d00e5e111240509472e6d257904d332221 --- /dev/null +++ b/brandao2016/instance_schema.json @@ -0,0 +1,14 @@ +{ + "parameters": { + "n": " Total number of items to be packed across all bins.", + "m": " Number of distinct item types.", + "W": " Maximum weight capacity of each bin." + }, + "items": [ + { + "item_type": " Unique identifier for this item type, starting from 1.", + "weight": " Weight of one copy of this item type.", + "demand": " Number of copies of this item type that must be packed." + } + ] +} diff --git a/brandao2016/mathematical_formulation.md b/brandao2016/mathematical_formulation.md new file mode 100644 index 0000000000000000000000000000000000000000..d0418ed6b39e179e7eb96a3c4fbffa1392a9b442 --- /dev/null +++ b/brandao2016/mathematical_formulation.md @@ -0,0 +1,58 @@ +# Original Formulation: Bin Packing / Cutting Stock via General Arc-flow (AF) + +*Source: Bin Packing and Related Problems: General Arc-flow Formulation with Graph Compression, Filipe Brandão and João Pedro Pedroso, 2013.* + +## Sets and Indices + +- $m$ : number of different item types (weights), indexed $i = 1, \ldots, m$. + +- $V$ : set of vertices of the directed acyclic graph $G = (V, A)$; includes the source vertex $\textsc{s}$ and the target vertex $\textsc{t}$. + +- $A$ : set of arcs. Each arc is a triple $(u, v, i)$ where $u, v \in V$ and $i$ identifies the item type contributing to the arc; arcs with $i = 0$ are the *loss* arcs (representing unused capacity). + +- $J \subseteq \{1, \ldots, m\}$ : subset of items whose demands must be satisfied *exactly* (for efficiency the authors set $J = \{ i = 1, \ldots, m \mid b_i = 1 \}$). + +## Parameters + +- $b_i$ : demand of items of type $i$, for $i = 1, \ldots, m$. + +- $w_i$ : weight (size) of item type $i$. + +- $W$ : bin capacity. + +- $\textsc{s}, \textsc{t}$ : source and target vertices of $G$. + +*The graph $G = (V, A)$ is constructed beforehand so that every path from $\textsc{s}$ to $\textsc{t}$ corresponds to a valid packing pattern for a single bin; $V$ and $A$ are therefore inputs to the MILP below.* + +## Decision Variables + +- $f_{uvi}$ : amount of flow along arc $(u, v, i) \in A$ (non-negative integer). + +- $z$ : number of bins required (total flow from $\textsc{s}$ to $\textsc{t}$). + +## Objective + +$$\begin{align} +\text{minimize} \quad & z \tag{17} +\end{align}$$ + +## Constraints + +$$\begin{align} +\sum_{(u,v,i) \in A:\, v = k} f_{uvi} + \;-\; \sum_{(v,r,i) \in A:\, v = k} f_{vri} + &= + \begin{cases} + -z & \text{if } k = \textsc{s}, \\ + \phantom{-}z & \text{if } k = \textsc{t}, \\ + \phantom{-}0 & \text{for } k \in V \setminus \{\textsc{s}, \textsc{t}\}, + \end{cases} \tag{18} \\[4pt] +\sum_{(u,v,j) \in A:\, j = i} f_{uvj} &\geq b_i, + && i \in \{1, \ldots, m\} \setminus J, \tag{19} \\[4pt] +\sum_{(u,v,j) \in A:\, j = i} f_{uvj} &= b_i, + && i \in J, \tag{20} \\[4pt] +f_{uvi} &\leq b_i, + && \forall (u,v,i) \in A, \text{ if } i \neq 0, \tag{21} \\[4pt] +f_{uvi} &\geq 0, \text{ integer}, + && \forall (u,v,i) \in A. \tag{22} +\end{align}$$ diff --git a/brandao2016/problem_description.txt b/brandao2016/problem_description.txt new file mode 100644 index 0000000000000000000000000000000000000000..58218e690ced2637e409cf0d12fd5f27a0a2f926 --- /dev/null +++ b/brandao2016/problem_description.txt @@ -0,0 +1,7 @@ +# Problem Description + +A facility must pack a collection of items into identical bins of fixed capacity, using as few bins as possible. There are m distinct item types, where each item type has a known integer weight and a known integer demand specifying how many copies of that type must be packed. The bin capacity is a single integer W, and every item weight is strictly positive and does not exceed W. + +The planner decides how many bins to use and how to allocate items to those bins. The total weight of items placed in any single bin must not exceed the capacity W, and for every item type the total number of copies packed across all bins must be at least that type's demand. + +The objective is to minimize the total number of bins used. diff --git a/brandao2016/solution_logger.py b/brandao2016/solution_logger.py new file mode 100644 index 0000000000000000000000000000000000000000..5393fa935ce71df9da95cee04fe5199077cb8461 --- /dev/null +++ b/brandao2016/solution_logger.py @@ -0,0 +1,61 @@ +""" +Convergence logger for optimization algorithms. + +Records incumbent solutions with timestamps to a JSONL file. +This module is provided to LLM-generated programs — they only need to call +`log(objective_value)` whenever a better feasible solution is found. + +Usage in generated code: + from solution_logger import SolutionLogger + logger = SolutionLogger(log_path, sense="minimize") # or "maximize" + # ... inside algorithm loop: + logger.log(objective_value) +""" + +import json +import time + + +class SolutionLogger: + def __init__(self, log_path, sense="minimize"): + """ + Args: + log_path: Path to the JSONL output file. + sense: "minimize" or "maximize". + """ + self.log_path = log_path + self.sense = sense + self.start_time = time.time() + self.best_obj = None + self.min_interval = 0.1 # seconds, avoid excessive writes + + self._last_log_time = 0.0 + # Clear the file + with open(self.log_path, "w") as f: + pass + + def log(self, objective_value): + """Record a new incumbent if it improves on the best known.""" + if objective_value is None: + return + + # Check if this is an improvement + if self.best_obj is not None: + if self.sense == "minimize" and objective_value >= self.best_obj: + return + if self.sense == "maximize" and objective_value <= self.best_obj: + return + + elapsed = time.time() - self.start_time + + # Throttle writes + if self.best_obj is not None and elapsed - self._last_log_time < self.min_interval: + self.best_obj = objective_value + return + + self.best_obj = objective_value + self._last_log_time = elapsed + + with open(self.log_path, "a") as f: + f.write(json.dumps({"time": round(elapsed, 3), + "objective_value": objective_value}) + "\n") diff --git a/brandao2016/solution_schema.json b/brandao2016/solution_schema.json new file mode 100644 index 0000000000000000000000000000000000000000..1eda629b61863f557bba261c0e9934d1a1df70a9 --- /dev/null +++ b/brandao2016/solution_schema.json @@ -0,0 +1,5 @@ +{ + "objective_value": " Total number of bins used in the packing.", + "num_bins": " Total number of bins used in the packing.", + "bins": " Item types assigned to each bin, where each inner list contains the item_type identifiers of all items packed into that bin." +} diff --git a/bront2009/feasibility_check.py b/bront2009/feasibility_check.py new file mode 100644 index 0000000000000000000000000000000000000000..71448a0170dd6613f64cafb8e0a49874bd75a545 --- /dev/null +++ b/bront2009/feasibility_check.py @@ -0,0 +1,486 @@ +""" +Feasibility Checker for CDLP (Choice-Based Deterministic Linear Programming) +============================================================================= +Paper: Bront, Mendez-Diaz, Vulcano (2009) +"A Column Generation Algorithm for Choice-Based Network Revenue Management" +Operations Research 57(3):769-784 + +Checks a candidate CDLP solution against the three hard constraints plus an +objective-consistency check (Tier C defence against self-reported-objective +exploits): + Constraint 1 (Capacity): sum_S lambda * Q_i(S) * t(S) <= c_i for each leg i + Constraint 2 (Time): sum_S t(S) <= T + Constraint 3 (Non-negativity): t(S) >= 0 for all S + Constraint 4 (Obj consistency): + reported objective_value must equal sum_S lambda * R(S) * t(S) + within a small tolerance. Because the solution lists every active + column (offer_set, time_allocated) the objective can be fully + recomputed from the solution + instance data, so a tight equality + check is appropriate. +""" + +import argparse +import json +import numpy as np + +# FRONTIEROR_RELEASE_CONTRACT_V2 +_FRONTIEROR_REQUIRED_SOLUTION_FIELDS = ('active_columns', 'objective_value') +_FRONTIEROR_NONEMPTY_SOLUTION_FIELDS = () +_FRONTIEROR_DERIVED_FIELD_RULES = () +_FRONTIEROR_CONDITIONAL_FIELD_RULES = () + + +def _frontieror_validate_solution_contract(solution): + """Return a structured rejection when the public solution contract is absent.""" + violations = [] + if not isinstance(solution, dict): + violations.append("solution must be a JSON object") + else: + missing = [ + field + for field in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS + if field not in solution or solution[field] is None + ] + if missing: + violations.append( + "missing required solution field(s): " + ", ".join(missing) + ) + + for discriminator, expected_value, fields in _FRONTIEROR_CONDITIONAL_FIELD_RULES: + if solution.get(discriminator) != expected_value: + continue + conditional_missing = [ + field + for field in fields + if field not in solution or solution[field] is None + ] + if conditional_missing: + violations.append( + "missing conditionally required solution field(s): " + + ", ".join(conditional_missing) + ) + + empty = [ + field + for field in _FRONTIEROR_NONEMPTY_SOLUTION_FIELDS + if field in solution and solution[field] in (None, [], {}) + ] + if empty: + violations.append( + "empty required decision witness field(s): " + ", ".join(empty) + ) + + for target, operation, source, source_key in _FRONTIEROR_DERIVED_FIELD_RULES: + try: + reported = float(solution[target]) + if operation == "length": + expected = float(len(solution[source])) + elif operation == "mapping_value": + expected = float(solution[source][source_key]) + else: + raise ValueError("unknown derived-field operation") + except (KeyError, TypeError, ValueError, OverflowError): + violations.append( + f"cannot derive {target} from decision witness field {source}" + ) + continue + if ( + reported != reported + or expected != expected + or reported in (float("inf"), float("-inf")) + or expected in (float("inf"), float("-inf")) + or abs(reported - expected) > 1e-6 + ): + violations.append( + f"{target}={reported} does not match the decision-derived " + f"value {expected}" + ) + + if "objective_value" in _FRONTIEROR_REQUIRED_SOLUTION_FIELDS: + objective = solution.get("objective_value") + if ( + isinstance(objective, bool) + or not isinstance(objective, (int, float)) + or objective != objective + or objective in (float("inf"), float("-inf")) + ): + violations.append("objective_value must be a finite JSON number") + + if not violations: + return None + return { + "feasible": False, + "violated_constraints": ["solution_schema"], + "violations": violations, + "violation_magnitudes": [], + } + + +def _frontieror_write_unexpected_rejection(error): + """Turn malformed candidate/checker exceptions into a normal rejection.""" + import json as _frontieror_json + import sys as _frontieror_sys + + result_path = None + for index, argument in enumerate(_frontieror_sys.argv): + if argument == "--result_path" and index + 1 < len(_frontieror_sys.argv): + result_path = _frontieror_sys.argv[index + 1] + break + if argument.startswith("--result_path="): + result_path = argument.split("=", 1)[1] + break + if not result_path: + return False + try: + with open(result_path, "w") as result_handle: + _frontieror_json.dump( + { + "feasible": False, + "violated_constraints": ["malformed_solution"], + "violations": [ + "checker rejected malformed candidate: " + + type(error).__name__ + + ": " + + str(error)[:500] + ], + "violation_magnitudes": [], + }, + result_handle, + indent=2, + ) + except Exception: + return False + return True + + + +def load_json(path): + with open(path, 'r') as f: + return json.load(f) + + +def build_problem_data(data): + """Extract problem parameters from instance JSON.""" + n = len(data["products"]) + m = len(data["network"]["legs"]) + L = len(data["segments"]) + T = data["booking_horizon"]["T"] + lam = data["lambda"] + + r = np.array([p["fare"] for p in data["products"]], dtype=float) + + A = np.zeros((m, n), dtype=float) + for j, prod in enumerate(data["products"]): + for leg_id in prod["legs_used"]: + A[leg_id - 1, j] = 1.0 + + c = np.array([leg["capacity"] for leg in data["network"]["legs"]], dtype=float) + + segments = [] + for seg in data["segments"]: + seg_info = { + "lambda_l": seg["lambda_l"], + "consideration_set": [pid - 1 for pid in seg["consideration_set"]], + "v": {}, + "v0": seg["no_purchase_preference"] + } + for idx, pid in enumerate(seg["consideration_set"]): + seg_info["v"][pid - 1] = seg["preference_vector"][idx] + segments.append(seg_info) + + p_l = np.array([seg["lambda_l"] / lam for seg in segments]) + + return { + "n": n, "m": m, "L": L, "T": T, "lam": lam, + "r": r, "A": A, "c": c, + "segments": segments, "p_l": p_l + } + + +def compute_choice_probs(S_set, prob_data): + """Compute P_j(S) for all products j using MNL with overlapping segments.""" + segments = prob_data["segments"] + p_l = prob_data["p_l"] + n = prob_data["n"] + P = np.zeros(n) + for j in S_set: + for l_idx, seg in enumerate(segments): + if j in seg["v"]: + denom = seg["v0"] + for h in S_set: + if h in seg["v"]: + denom += seg["v"][h] + P[j] += p_l[l_idx] * seg["v"][j] / denom + return P + + +def compute_R_and_Q(S_set, prob_data): + """Compute R(S) and Q(S) for an offer set S.""" + r = prob_data["r"] + A = prob_data["A"] + P = compute_choice_probs(S_set, prob_data) + R_S = sum(r[j] * P[j] for j in S_set) + Q_S = A @ P + return R_S, Q_S + + +def extract_columns_and_times(solution): + """ + Extract offer sets and their time allocations from a candidate solution. + Returns list of (S_set_0indexed, t_value) tuples, or None if no primal + variables are present. + """ + if "active_columns" not in solution: + return None + + columns = [] + for col in solution["active_columns"]: + # offer_set is 1-indexed in the solution JSON + S_set = set(pid - 1 for pid in col["offer_set"]) + t_val = col["time_allocated"] + columns.append((S_set, t_val)) + return columns + + +def check_feasibility(instance, solution): + """ + Check all hard constraints of the CDLP formulation plus objective + consistency. + + Constraints: + 1: Capacity -- sum_S lambda * Q_i(S) * t(S) <= c_i for each leg i + 2: Time -- sum_S t(S) <= T + 3: Non-negativity -- t(S) >= 0 for all S + 4: Objective consistency -- reported objective_value == sum_S lambda * R(S) * t(S) + """ + tol = 1e-5 + eps = 1e-5 + + prob_data = build_problem_data(instance) + m = prob_data["m"] + T = prob_data["T"] + lam = prob_data["lam"] + c = prob_data["c"] + + columns = extract_columns_and_times(solution) + + violated_constraints = set() + violations = [] + violation_magnitudes = [] + + if columns is None: + # Solution has no primal t(S) variables (e.g., simulation-based DCOMP). + # Constraints 1-3 cannot be evaluated, but a reported objective_value + # with no supporting columns is still an obj-consistency violation + # (recomputed obj is 0 in this case). + reported_obj = solution.get("objective_value") + try: + reported = float(reported_obj) if reported_obj is not None else None + except (TypeError, ValueError): + reported = None + if reported is not None: + true_obj = 0.0 + abs_diff = abs(reported - true_obj) + obj_tol = max(1e-3, 1e-3 * abs(true_obj)) + if abs_diff > obj_tol: + violated_constraints.add(4) + violations.append( + f"Objective consistency violated: reported objective_value=" + f"{reported} but no active_columns present so recomputed " + f"sum_S lambda*R(S)*t(S) = 0.0 " + f"(|diff|={abs_diff:.6g}, tol={obj_tol:.6g})" + ) + normalizer = max(abs(true_obj), eps) + violation_magnitudes.append({ + "constraint": 4, + "lhs": float(reported), + "rhs": float(true_obj), + "raw_excess": float(abs_diff), + "normalizer": float(normalizer), + "ratio": float(abs_diff / normalizer) + }) + if not violated_constraints: + return { + "feasible": True, + "violated_constraints": [], + "violations": [ + "No primal t(S) variables in solution; CDLP constraints not evaluated" + ], + "violation_magnitudes": [] + } + return { + "feasible": False, + "violated_constraints": sorted(violated_constraints), + "violations": violations, + "violation_magnitudes": violation_magnitudes + } + + # ------------------------------------------------------------------ + # Constraint 1: Capacity constraint (one per leg) + # sum_S lambda * Q_i(S) * t(S) <= c_i for i = 1, ..., m + # ------------------------------------------------------------------ + # Compute R(S) and Q(S) for every active column (R(S) reused by constraint 4). + R_per_col = [] + capacity_usage = np.zeros(m) + for S_set, t_val in columns: + R_S, Q_S = compute_R_and_Q(S_set, prob_data) + R_per_col.append(R_S) + capacity_usage += lam * Q_S * t_val + + for i in range(m): + lhs = capacity_usage[i] + rhs = c[i] + violation_amount = lhs - rhs # positive means violated (LHS > RHS) + if violation_amount > tol: + violated_constraints.add(1) + leg_info = instance["network"]["legs"][i] + violations.append( + f"Capacity constraint violated on leg {leg_info['leg_id']} " + f"({leg_info['origin']}->{leg_info['destination']}): " + f"usage {lhs:.6f} > capacity {rhs:.6f}" + ) + normalizer = max(abs(rhs), eps) + violation_magnitudes.append({ + "constraint": 1, + "lhs": float(lhs), + "rhs": float(rhs), + "raw_excess": float(violation_amount), + "normalizer": float(normalizer), + "ratio": float(violation_amount / normalizer) + }) + + # ------------------------------------------------------------------ + # Constraint 2: Time constraint + # sum_S t(S) <= T + # ------------------------------------------------------------------ + total_time = sum(t_val for _, t_val in columns) + lhs = total_time + rhs = float(T) + violation_amount = lhs - rhs + if violation_amount > tol: + violated_constraints.add(2) + violations.append( + f"Time constraint violated: total time allocated {lhs:.6f} > T = {rhs:.6f}" + ) + normalizer = max(abs(rhs), eps) + violation_magnitudes.append({ + "constraint": 2, + "lhs": float(lhs), + "rhs": float(rhs), + "raw_excess": float(violation_amount), + "normalizer": float(normalizer), + "ratio": float(violation_amount / normalizer) + }) + + # ------------------------------------------------------------------ + # Constraint 3: Non-negativity + # t(S) >= 0 for all S + # ------------------------------------------------------------------ + for idx, (S_set, t_val) in enumerate(columns): + lhs = 0.0 # RHS of t(S) >= 0 rewritten: 0 <= t(S), so check 0 - t(S) + rhs_val = 0.0 + # For a >= constraint: violation_amount = RHS - LHS = 0 - t_val + violation_amount = rhs_val - t_val # positive means t_val < 0 + if violation_amount > tol: + violated_constraints.add(3) + offer_set_1idx = sorted(j + 1 for j in S_set) + violations.append( + f"Non-negativity violated for offer set {offer_set_1idx}: " + f"t(S) = {t_val:.6f} < 0" + ) + normalizer = max(abs(rhs_val), eps) + violation_magnitudes.append({ + "constraint": 3, + "lhs": float(t_val), + "rhs": float(rhs_val), + "raw_excess": float(violation_amount), + "normalizer": float(normalizer), + "ratio": float(violation_amount / normalizer) + }) + + # ------------------------------------------------------------------ + # Constraint 4: Objective consistency (Tier C defence) + # reported objective_value == sum_S lambda * R(S) * t(S) + # Full recompute is exact: every variable that determines the obj + # (the active columns and their time allocations) is present in the + # solution. Tolerance: 0.1% relative, with a 1e-3 absolute floor. + # ------------------------------------------------------------------ + reported_obj = solution.get("objective_value") + try: + reported = float(reported_obj) if reported_obj is not None else None + except (TypeError, ValueError): + reported = None + if reported is not None: + true_obj = float(sum(lam * R_per_col[i] * columns[i][1] for i in range(len(columns)))) + abs_diff = abs(reported - true_obj) + obj_tol = max(1e-3, 1e-3 * abs(true_obj)) + if abs_diff > obj_tol: + violated_constraints.add(4) + violations.append( + f"Objective consistency violated: reported objective_value=" + f"{reported} differs from recomputed sum_S lambda*R(S)*t(S)=" + f"{true_obj} (|diff|={abs_diff:.6g}, tol={obj_tol:.6g})" + ) + normalizer = max(abs(true_obj), eps) + violation_magnitudes.append({ + "constraint": 4, + "lhs": float(reported), + "rhs": float(true_obj), + "raw_excess": float(abs_diff), + "normalizer": float(normalizer), + "ratio": float(abs_diff / normalizer) + }) + + feasible = len(violated_constraints) == 0 + return { + "feasible": feasible, + "violated_constraints": sorted(violated_constraints), + "violations": violations, + "violation_magnitudes": violation_magnitudes + } + + +def main(): + parser = argparse.ArgumentParser( + description="Feasibility checker for CDLP (Bront et al. 2009)") + parser.add_argument("--instance_path", type=str, required=True, + help="Path to the JSON file containing the data instance") + parser.add_argument("--solution_path", type=str, required=True, + help="Path to the JSON file containing the candidate solution") + parser.add_argument("--result_path", type=str, required=True, + help="Path to write the JSON file containing the feasibility result") + args = parser.parse_args() + + instance = load_json(args.instance_path) + solution = load_json(args.solution_path) + _frontieror_contract_result = _frontieror_validate_solution_contract(solution) + if _frontieror_contract_result is not None: + with open(args.result_path, "w") as _frontieror_result_handle: + json.dump( + _frontieror_contract_result, + _frontieror_result_handle, + indent=2, + ) + return + + result = check_feasibility(instance, solution) + + with open(args.result_path, 'w') as f: + json.dump(result, f, indent=2) + + status = "FEASIBLE" if result["feasible"] else "INFEASIBLE" + print(f"Feasibility: {status}") + if result["violated_constraints"]: + print(f"Violated constraints: {result['violated_constraints']}") + for v in result["violations"]: + print(f" - {v}") + + +if __name__ == "__main__": + try: + main() + except Exception as _frontieror_unexpected_error: + if not _frontieror_write_unexpected_rejection( + _frontieror_unexpected_error + ): + raise diff --git a/bront2009/gurobi_code.py b/bront2009/gurobi_code.py new file mode 100644 index 0000000000000000000000000000000000000000..10fc5c72cc162ea825974ea1ad2c68e455be8622 --- /dev/null +++ b/bront2009/gurobi_code.py @@ -0,0 +1,524 @@ +""" +CDLP (Choice-Based Deterministic Linear Programming) via Column Generation +============================================================================ +Paper: Bront, Mendez-Diaz, Vulcano (2009) +"A Column Generation Algorithm for Choice-Based Network Revenue Management" +Operations Research 57(3):769-784 + +This program solves the CDLP formulation (Equation (3) in the paper) using +column generation (Section 4). The column generation subproblem (Equation (6)) +is solved first by a greedy heuristic (Section 4.2.2), and if that fails, +by an exact MIP reformulation (Section 4.2.1). + +Output: optimal CDLP objective value and the primal/dual solutions. +""" + +import argparse +import json +import time +import numpy as np +import gurobipy as gp +from gurobipy import GRB +import os as _os, sys as _sys +# Walk up from this file's directory to find repo root (containing scripts/). +_repo = _os.path.dirname(_os.path.abspath(__file__)) +while _repo != _os.path.dirname(_repo) and not _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _repo = _os.path.dirname(_repo) +if _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')): + _sys.path.insert(0, _repo) +try: + from scripts.utils.gurobi_log_helper import install_gurobi_logger +except ImportError: + def install_gurobi_logger(log_path): # no-op fallback when scripts/ unavailable + pass + + +def load_instance(path): + """Load problem instance from JSON file.""" + with open(path, 'r') as f: + data = json.load(f) + return data + + +def build_problem_data(data): + """ + Extract and precompute all problem parameters from the instance JSON. + Returns a dict with all needed arrays/values. + """ + n = len(data["products"]) # number of products + m = len(data["network"]["legs"]) # number of legs (resources) + L = len(data["segments"]) # number of segments + T = data["booking_horizon"]["T"] + lam = data["lambda"] # overall arrival probability per period + + # Product revenues (0-indexed) + r = np.array([p["fare"] for p in data["products"]], dtype=float) + + # Incidence matrix A: m x n, A[i][j] = 1 if leg i is used by product j + A = np.zeros((m, n), dtype=float) + for j, prod in enumerate(data["products"]): + for leg_id in prod["legs_used"]: + leg_idx = leg_id - 1 # convert 1-indexed to 0-indexed + A[leg_idx, j] = 1.0 + + # Capacities + c = np.array([leg["capacity"] for leg in data["network"]["legs"]], dtype=float) + + # Segment data + segments = [] + for seg in data["segments"]: + seg_info = { + "lambda_l": seg["lambda_l"], + "consideration_set": [pid - 1 for pid in seg["consideration_set"]], # 0-indexed + "v": {}, # preference weights: product_0idx -> weight + "v0": seg["no_purchase_preference"] + } + for idx, pid in enumerate(seg["consideration_set"]): + seg_info["v"][pid - 1] = seg["preference_vector"][idx] + segments.append(seg_info) + + # Compute p_l = lambda_l / lambda + p_l = np.array([seg["lambda_l"] / lam for seg in segments]) + + return { + "n": n, "m": m, "L": L, "T": T, "lam": lam, + "r": r, "A": A, "c": c, + "segments": segments, "p_l": p_l + } + + +def compute_choice_probs(S_set, prob_data): + """ + Compute P_j(S) for all j in S, using the MNL model with overlapping segments. + S_set: set of 0-indexed product indices + Returns: dict {j: P_j(S)} for j in S_set + """ + segments = prob_data["segments"] + p_l = prob_data["p_l"] + n = prob_data["n"] + + P = {} + for j in range(n): + if j not in S_set: + P[j] = 0.0 + continue + total = 0.0 + for l_idx, seg in enumerate(segments): + if j in seg["v"]: + # Compute denominator for this segment + denom = seg["v0"] + for h in S_set: + if h in seg["v"]: + denom += seg["v"][h] + P_lj = seg["v"][j] / denom + total += p_l[l_idx] * P_lj + P[j] = total + return P + + +def compute_R_and_Q(S_set, prob_data): + """ + Compute R(S) = sum_{j in S} r_j * P_j(S) (expected revenue) + and Q(S) = A * P(S) (capacity consumption vector) + """ + r = prob_data["r"] + A = prob_data["A"] + n = prob_data["n"] + + P = compute_choice_probs(S_set, prob_data) + + R_S = sum(r[j] * P[j] for j in S_set) + + P_vec = np.array([P.get(j, 0.0) for j in range(n)]) + Q_S = A @ P_vec + + return R_S, Q_S + + +def greedy_heuristic(pi, sigma, prob_data): + """ + Greedy heuristic for the column generation subproblem (Section 4.2.2). + Returns (S_set, reduced_cost) where S_set is the set of products to offer, + and reduced_cost is the subproblem objective value. + """ + n = prob_data["n"] + r = prob_data["r"] + A = prob_data["A"] + segments = prob_data["segments"] + lam = prob_data["lam"] + + # Step 1: For all products j such that r_j - A_j^T pi <= 0, set y_j = 0 + w = np.array([r[j] - A[:, j] @ pi for j in range(n)]) + S_prime = set() + for j in range(n): + if w[j] > 0: + S_prime.add(j) + + if not S_prime: + return set(), -sigma + + # Step 3: Compute j1* = argmax over S' of sum_l (r_j - A_j^T pi) * v_lj / (v_lj + v_l0) + best_val = -np.inf + best_j = None + for j in S_prime: + val = 0.0 + for seg in segments: + if j in seg["v"]: + vlj = seg["v"][j] + val += w[j] * vlj / (vlj + seg["v0"]) + if val > best_val: + best_val = val + best_j = j + + S = {best_j} + S_prime.discard(best_j) + + # Helper: compute Value(S) = subproblem objective without -sigma + def compute_value(S_set): + val = 0.0 + for j in S_set: + for l_idx, seg in enumerate(segments): + if j in seg["v"]: + denom = seg["v0"] + for h in S_set: + if h in seg["v"]: + denom += seg["v"][h] + val += w[j] * seg["lambda_l"] * seg["v"][j] / denom + return val + + # Step 4: Repeat adding products + changed = True + while changed and S_prime: + changed = False + current_val = compute_value(S) + + # Find best product to add from S' + # Compute for each j in S': the objective of S union {j} + best_new_val = -np.inf + best_new_j = None + for j in S_prime: + # Compute objective: sum_l lambda_l * (sum_{i in C_l cap (S union {j})} w_i * v_li) / (sum_{i in C_l cap (S union {j})} v_li + v_l0) + candidate = S | {j} + new_val = 0.0 + for l_idx, seg in enumerate(segments): + num = 0.0 + denom = seg["v0"] + for h in candidate: + if h in seg["v"]: + num += w[h] * seg["v"][h] + denom += seg["v"][h] + new_val += seg["lambda_l"] * num / denom + if new_val > best_new_val: + best_new_val = new_val + best_new_j = j + + # Following paper step 4(a)-(b): use Value(S union {j*}) + if best_new_j is not None: + candidate_val = compute_value(S | {best_new_j}) + if candidate_val > current_val: + S.add(best_new_j) + S_prime.discard(best_new_j) + changed = True + + reduced_cost = compute_value(S) - sigma + return S, reduced_cost + + +def exact_mip_subproblem(pi, sigma, prob_data, time_limit=300): + """ + Exact MIP reformulation for the column generation subproblem (Section 4.2.1). + Returns (S_set, reduced_cost). + """ + n = prob_data["n"] + r = prob_data["r"] + A = prob_data["A"] + segments = prob_data["segments"] + L = prob_data["L"] + + w = np.array([r[j] - A[:, j] @ pi for j in range(n)]) + + # Compute K >= 1/v_min + all_v = [] + for seg in segments: + all_v.append(seg["v0"]) + for v_val in seg["v"].values(): + if v_val > 0: + all_v.append(v_val) + v_min = min(all_v) + K = 1.0 / v_min + 1.0 # add margin + + model = gp.Model("subproblem_mip") + model.setParam("Threads", 1) + model.setParam("OutputFlag", 0) + model.setParam("TimeLimit", time_limit) + + # Variables + y = model.addVars(n, vtype=GRB.BINARY, name="y") + x = model.addVars(L, lb=0.0, name="x") + + # z[l,j] = x_l * y_j (linearized) + z = {} + for l_idx, seg in enumerate(segments): + for j in seg["consideration_set"]: + z[l_idx, j] = model.addVar(lb=0.0, name=f"z_{l_idx}_{j}") + + model.update() + + # Objective: max sum_l sum_{j in C_l} lambda_l * (r_j - A_j^T pi) * v_lj * z_lj + obj = gp.LinExpr() + for l_idx, seg in enumerate(segments): + for j in seg["consideration_set"]: + coeff = seg["lambda_l"] * w[j] * seg["v"][j] + obj += coeff * z[l_idx, j] + model.setObjective(obj, GRB.MAXIMIZE) + + # Constraints + for l_idx, seg in enumerate(segments): + # x_l * v_l0 + sum_{i in C_l} v_li * z_li = 1 + constr = seg["v0"] * x[l_idx] + for j in seg["consideration_set"]: + constr += seg["v"][j] * z[l_idx, j] + model.addConstr(constr == 1.0, name=f"norm_{l_idx}") + + for j in seg["consideration_set"]: + # x_l - z_lj <= K - K * y_j + model.addConstr(x[l_idx] - z[l_idx, j] <= K - K * y[j], + name=f"lin1_{l_idx}_{j}") + # z_lj <= x_l + model.addConstr(z[l_idx, j] <= x[l_idx], + name=f"lin2_{l_idx}_{j}") + # z_lj <= K * y_j + model.addConstr(z[l_idx, j] <= K * y[j], + name=f"lin3_{l_idx}_{j}") + + model.optimize() + + if model.status in [GRB.OPTIMAL, GRB.SUBOPTIMAL, GRB.TIME_LIMIT]: + if model.SolCount > 0: + S_set = set() + for j in range(n): + if y[j].X > 0.5: + S_set.add(j) + obj_val = model.ObjVal + return S_set, obj_val - sigma + else: + return set(), -sigma + else: + return set(), -sigma + + +def solve_cdlp_column_generation(prob_data, time_limit): + """ + Solve the CDLP via column generation (Section 4 of the paper). + Returns the optimal objective value and dual prices. + """ + n = prob_data["n"] + m = prob_data["m"] + T = prob_data["T"] + lam = prob_data["lam"] + c = prob_data["c"] + segments = prob_data["segments"] + + start_time = time.time() + + # A.2. Initialization: single column containing one product per segment + # Pick the first product of each segment (0-indexed, in labeling order) + init_products = set() + for seg in segments: + first_product = min(seg["consideration_set"]) + init_products.add(first_product) + + columns = [frozenset(init_products)] # list of frozensets + column_set = {columns[0]} # for duplicate checking + + # Precompute R(S) and Q(S) for each column + R_vals = [] + Q_vals = [] + R_S, Q_S = compute_R_and_Q(init_products, prob_data) + R_vals.append(R_S) + Q_vals.append(Q_S) + + iteration = 0 + best_obj = 0.0 + best_pi = np.zeros(m) + best_sigma = 0.0 + best_t_vals = {} + + while True: + elapsed = time.time() - start_time + if elapsed > time_limit: + break + + iteration += 1 + k = len(columns) + + # Solve reduced LP (Equation (4)) + master = gp.Model("CDLP_master") + master.setParam("Threads", 1) + master.setParam("OutputFlag", 0) + remaining_time = max(1, time_limit - (time.time() - start_time)) + master.setParam("TimeLimit", remaining_time) + + t_vars = master.addVars(k, lb=0.0, name="t") + master.update() + + # Objective: max sum_S lambda * R(S) * t(S) + obj = gp.LinExpr() + for idx in range(k): + obj += lam * R_vals[idx] * t_vars[idx] + master.setObjective(obj, GRB.MAXIMIZE) + + # Capacity constraints: sum_S lambda * Q_i(S) * t(S) <= c_i + cap_constrs = [] + for i in range(m): + constr = gp.LinExpr() + for idx in range(k): + constr += lam * Q_vals[idx][i] * t_vars[idx] + cap_constrs.append(master.addConstr(constr <= c[i], name=f"cap_{i}")) + + # Time constraint: sum_S t(S) <= T + time_constr_expr = gp.LinExpr() + for idx in range(k): + time_constr_expr += t_vars[idx] + time_constr = master.addConstr(time_constr_expr <= T, name="time") + + master.optimize() + + if master.status != GRB.OPTIMAL: + break + + best_obj = master.ObjVal + + # Get dual prices + pi = np.array([cap_constrs[i].Pi for i in range(m)]) + sigma = time_constr.Pi + + best_pi = pi.copy() + best_sigma = sigma + best_t_vals = {} + for idx in range(k): + if t_vars[idx].X > 1e-8: + best_t_vals[idx] = t_vars[idx].X + + master.dispose() + + # Check time + elapsed = time.time() - start_time + if elapsed > time_limit: + break + + # Solve column generation subproblem + # First try greedy heuristic + S_greedy, rc_greedy = greedy_heuristic(pi, sigma, prob_data) + + if rc_greedy > 1e-8 and len(S_greedy) > 0: + new_col = frozenset(S_greedy) + if new_col not in column_set: + columns.append(new_col) + column_set.add(new_col) + R_S, Q_S = compute_R_and_Q(S_greedy, prob_data) + R_vals.append(R_S) + Q_vals.append(Q_S) + continue + + # If greedy fails, try exact MIP + elapsed = time.time() - start_time + remaining = max(1, time_limit - elapsed) + S_exact, rc_exact = exact_mip_subproblem(pi, sigma, prob_data, + time_limit=remaining) + + if rc_exact > 1e-8 and len(S_exact) > 0: + new_col = frozenset(S_exact) + if new_col not in column_set: + columns.append(new_col) + column_set.add(new_col) + R_S, Q_S = compute_R_and_Q(S_exact, prob_data) + R_vals.append(R_S) + Q_vals.append(Q_S) + continue + + # No entering column found -> optimal + break + + # Build solution details + solution_columns = [] + for idx, t_val in best_t_vals.items(): + solution_columns.append({ + "offer_set": sorted([j + 1 for j in columns[idx]]), # 1-indexed + "time_allocated": t_val + }) + + return { + "objective_value": best_obj, + "dual_prices_pi": best_pi.tolist(), + "dual_price_sigma": best_sigma, + "num_iterations": iteration, + "num_columns_generated": len(columns), + "active_columns": solution_columns + } + + +def main(): + parser = argparse.ArgumentParser( + description="Solve CDLP via Column Generation (Bront et al. 2009)") + parser.add_argument("--instance_path", type=str, required=True, + help="Path to the JSON instance file") + parser.add_argument("--solution_path", type=str, required=True, + help="Path for the output solution JSON file") + parser.add_argument("--time_limit", type=int, required=True, + help="Maximum solver runtime in seconds") + parser.add_argument("--log_path", type=str, default=None, help="Path to log incumbent solutions") + args = parser.parse_args() + install_gurobi_logger(args.log_path) + + # Load instance + data = load_instance(args.instance_path) + + # Build problem data structures + prob_data = build_problem_data(data) + + print(f"Problem: {data.get('description', 'N/A')}") + print(f" Products: {prob_data['n']}, Legs: {prob_data['m']}, " + f"Segments: {prob_data['L']}, T: {prob_data['T']}") + print(f" Capacities: {prob_data['c'].tolist()}") + print(f" Lambda: {prob_data['lam']}") + print(f" Time limit: {args.time_limit}s") + + # Solve CDLP via column generation + start = time.time() + result = solve_cdlp_column_generation(prob_data, args.time_limit) + elapsed = time.time() - start + + print(f"\nResults:") + print(f" CDLP Objective Value: {result['objective_value']:.4f}") + print(f" Dual prices (pi): {result['dual_prices_pi']}") + print(f" Dual price (sigma): {result['dual_price_sigma']:.4f}") + print(f" Column generation iterations: {result['num_iterations']}") + print(f" Total columns generated: {result['num_columns_generated']}") + print(f" Elapsed time: {elapsed:.2f}s") + print(f"\n Active offer sets:") + for col in result["active_columns"]: + print(f" S = {col['offer_set']}, t(S) = {col['time_allocated']:.4f}") + + # Save solution + solution = { + "objective_value": result["objective_value"], + "instance_id": data.get("instance_id", "unknown"), + "solver": "Gurobi (column generation)", + "method": "CDLP", + "elapsed_time_seconds": elapsed, + "dual_prices_pi": result["dual_prices_pi"], + "dual_price_sigma": result["dual_price_sigma"], + "num_iterations": result["num_iterations"], + "num_columns_generated": result["num_columns_generated"], + "active_columns": result["active_columns"] + } + + with open(args.solution_path, 'w') as f: + json.dump(solution, f, indent=2) + + print(f"\nSolution saved to {args.solution_path}") + + +if __name__ 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b/bront2009/instance_schema.json @@ -0,0 +1,39 @@ +{ + "network": { + "num_cities": " Number of cities in the airline network.", + "city_names": " Name of each city in the network.", + "num_legs": " Number of flight legs in the network.", + "legs": [ + { + "leg_id": " Unique identifier for this flight leg.", + "origin": " City where this flight leg departs.", + "destination": " City where this flight leg arrives.", + "capacity": " Number of available seats on this flight leg." + } + ] + }, + "products": [ + { + "product_id": " Unique identifier for this product.", + "origin_destination": " Route of the itinerary, with intermediate stops separated by arrows.", + "class": " Fare class of this product.", + "fare": " Revenue earned per unit sold of this product.", + "legs_used": " Leg IDs of the flight legs consumed by one unit of this product." + } + ], + "segments": [ + { + "segment_id": " Unique identifier for this customer segment.", + "lambda_l": " Arrival rate of customers in this segment per period.", + "consideration_set": " Product IDs that customers in this segment would consider purchasing.", + "preference_vector": " Preference weight of each product in the consideration set, in the same order.", + "no_purchase_preference": " Preference weight for the no-purchase option for customers in this segment.", + "description": " Brief label describing the segment's market and price sensitivity." + } + ], + "booking_horizon": { + "T": " Number of discrete time periods in the booking horizon." + }, + "alpha": " Capacity scarcity factor used to scale leg capacities.", + "lambda": " Overall probability that a customer arrives in any single period." +} diff --git a/bront2009/mathematical_formulation.md b/bront2009/mathematical_formulation.md new file mode 100644 index 0000000000000000000000000000000000000000..3b0691384d4fdad625ed04af245791d06eb2e43b --- /dev/null +++ b/bront2009/mathematical_formulation.md @@ -0,0 +1,53 @@ +# Original Formulation: Choice-Based Deterministic Linear Program (CDLP) + +*Source: A Column Generation Algorithm for Choice-Based Network Revenue Management, Bront, Méndez-Díaz, and Vulcano, 2009.* + +## Sets and Parameters + +- $N = \{1,\dots,n\}$: set of products (itinerary and fare-class combinations). + +- $m$: number of resources (flight legs), indexed by $i = 1,\dots,m$. + +- $L$: number of customer segments, indexed by $l = 1,\dots,L$. + +- $T$: length of the booking horizon (number of discrete time periods). + +- $S \subseteq N$: an offer set (a subset of products made available to arriving customers). + +- $C_l \subseteq N$: consideration set of segment $l$ (overlap across segments is allowed). + +- $c = (c_1,\dots,c_m)^{\top}$: initial capacity vector of the resources. + +- $A = [a_{ij}] \in \{0,1\}^{m \times n}$: resource-product incidence matrix; $A_j$ denotes the $j$-th column. + +- $r_j$: revenue collected from selling one unit of product $j$. + +- $\lambda$: probability that a customer arrives in a given time period; $p_l$ is the conditional probability of segment $l$ given an arrival, with $\sum_l p_l = 1$; $\lambda_l = \lambda p_l$. + +- $v_{lj} \geq 0$ for $j \in C_l$: preference weight of segment $l$ for product $j$, with $v_{l0} > 0$ the no-purchase weight. + +- Under the MNL choice model, the probability that a segment-$l$ arrival chooses $j \in S$ is $P_{lj}(S) = v_{lj} / \bigl(\sum_{h \in C_l \cap S} v_{lh} + v_{l0}\bigr)$, and the aggregate purchase probability of product $j$ under $S$ is $P_j(S) = \sum_{l=1}^{L} p_l P_{lj}(S)$. + +- Expected per-period revenue from $S$: $R(S) = \sum_{j \in S} r_j P_j(S)$. + +- Resource consumption vector from $S$: $Q(S) = A\, P(S)$ where $P(S) = (P_1(S),\dots,P_n(S))^{\top}$. + +## Decision Variables + +- $t(S) \geq 0$ for every $S \subseteq N$: (continuous) number of time periods during which offer set $S$ is made available. + +## Objective + +$$\begin{equation} +V^{\mathrm{CDLP}} \;=\; \max \; \sum_{S \subseteq N} \lambda\, R(S)\, t(S) \tag{3} +\end{equation}$$ + +## Constraints + +$$\begin{align} +\sum_{S \subseteq N} \lambda\, Q(S)\, t(S) & \;\leq\; c, \tag{3a} \\ +\sum_{S \subseteq N} t(S) & \;\leq\; T, \tag{3b} \\ +t(S) & \;\geq\; 0, \qquad \forall S \subseteq N. \tag{3c} +\end{align}$$ + +The formulation has one variable $t(S)$ for each of the $2^{n}-1$ nonempty subsets $S \subseteq N$, i.e. an exponential family of variables; the paper solves it via column generation. diff --git a/bront2009/problem_description.txt b/bront2009/problem_description.txt new file mode 100644 index 0000000000000000000000000000000000000000..9feabba1b47ffa2e42e479aaf427c6f4eaef3660 --- /dev/null +++ b/bront2009/problem_description.txt @@ -0,0 +1,9 @@ +# Problem Description + +An airline operates a network of flight legs connecting several cities. Each leg has a fixed seat capacity. Products sold to customers are itinerary-and-fare-class combinations, where each product is defined by an origin-destination path (possibly spanning multiple legs), a fare class, and a fare (revenue per unit sold). Each product consumes one seat on every leg along its route; for each combination of a leg and a product, it is known whether or not that product uses that leg. + +Customers arrive one at a time over a discrete booking horizon spanning a given number of time periods. In each period, at most one customer arrives. The probability of an arrival in any period is given by a global arrival rate. Customers belong to one of several market segments. Each segment has a known arrival rate equal to the global arrival rate multiplied by the segment's probability share, where the segment probabilities sum to one. Each segment is characterized by a consideration set, which is a subset of the products that customers in that segment would consider purchasing. Consideration sets of different segments may overlap, meaning the same product can appear in the consideration sets of multiple segments. Each segment also has a positive preference weight for every product in its consideration set and a strictly positive no-purchase preference weight. When a customer from a given segment is presented with a set of available products (an offer set), the customer's probability of choosing a particular product in the intersection of the offer set and the segment's consideration set equals that product's preference weight divided by the sum of the preference weights of all products in that intersection plus the no-purchase preference weight. A product outside the intersection is never chosen. The aggregate probability that an arriving customer (whose segment is unknown to the firm) purchases a given product in the offer set equals the sum over all segments of the segment probability times the segment-level choice probability for that product. + +The airline's task is to determine, for every possible nonempty subset of products that could be offered, the total amount of time (measured in number of periods) during which that offer set is made available over the booking horizon. The time allocated to each offer set must be zero or positive. The expected revenue generated when a particular offer set is shown to an arriving customer equals the sum, over all products in that set, of the product's fare times the aggregate purchase probability for that product under that set. The expected consumption of capacity on each leg when an offer set is shown equals the sum of the aggregate purchase probabilities for all products in that set which use that leg. + +The total expected capacity consumption on each leg, summed across all offer sets weighted by the arrival rate and the time allocated to each set, must not exceed the leg's initial seat capacity. The total time allocated across all offer sets must not exceed the length of the booking horizon. The goal is to choose the time allocations for each possible offer set so as to maximize total expected revenue, computed as the sum over all offer sets of the arrival rate times the expected revenue of the set times the time allocated to that set. diff --git a/bront2009/solution_logger.py b/bront2009/solution_logger.py new file mode 100644 index 0000000000000000000000000000000000000000..5393fa935ce71df9da95cee04fe5199077cb8461 --- /dev/null +++ b/bront2009/solution_logger.py @@ -0,0 +1,61 @@ +""" +Convergence logger for optimization algorithms. + +Records incumbent solutions with timestamps to a JSONL file. +This module is provided to LLM-generated programs — they only need to call +`log(objective_value)` whenever a better feasible solution is found. + +Usage in generated code: + from solution_logger import SolutionLogger + logger = SolutionLogger(log_path, sense="minimize") # or "maximize" + # ... inside algorithm loop: + logger.log(objective_value) +""" + +import json +import time + + +class SolutionLogger: + def __init__(self, log_path, sense="minimize"): + """ + Args: + log_path: Path to the JSONL output file. + sense: "minimize" or "maximize". + """ + self.log_path = log_path + self.sense = sense + self.start_time = time.time() + self.best_obj = None + self.min_interval = 0.1 # seconds, avoid excessive writes + + self._last_log_time = 0.0 + # Clear the file + with open(self.log_path, "w") as f: + pass + + def log(self, objective_value): + """Record a new incumbent if it improves on the best known.""" + if objective_value is None: + return + + # Check if this is an improvement + if self.best_obj is not None: + if self.sense == "minimize" and objective_value >= self.best_obj: + return + if self.sense == "maximize" and objective_value <= self.best_obj: + return + + elapsed = time.time() - self.start_time + + # Throttle writes + if self.best_obj is not None and elapsed - self._last_log_time < self.min_interval: + self.best_obj = objective_value + return + + self.best_obj = objective_value + self._last_log_time = elapsed + + with open(self.log_path, "a") as f: + f.write(json.dumps({"time": round(elapsed, 3), + "objective_value": objective_value}) + "\n")