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Clear old FrontierOR files 901-1800 of 5583

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  1. byeon2022/gurobi_solution_log/large_log_3.jsonl +0 -1
  2. byeon2022/gurobi_solution_log/large_log_4.jsonl +0 -4
  3. byeon2022/gurobi_solution_log/large_log_5.jsonl +0 -4
  4. byeon2022/gurobi_solution_log/tiny_log.jsonl +0 -1
  5. byeon2022/instance/large_instance_1.json +0 -870
  6. byeon2022/instance/large_instance_11.json.pre_scaleup_20260519 +0 -622
  7. byeon2022/instance/large_instance_2.json +0 -880
  8. byeon2022/instance/large_instance_21.json.pre_compfix_20260518 +0 -632
  9. byeon2022/instance/large_instance_21.json.pre_scaleup_20260519 +0 -633
  10. byeon2022/instance/large_instance_3.json +0 -875
  11. byeon2022/instance/large_instance_31.json.pre_scaleup_20260519 +0 -627
  12. byeon2022/instance/large_instance_4.json +0 -874
  13. byeon2022/instance/large_instance_41.json.pre_compfix_20260518 +0 -626
  14. byeon2022/instance/large_instance_41.json.pre_scaleup_20260519 +0 -627
  15. byeon2022/instance/large_instance_5.json +0 -871
  16. byeon2022/instance/large_instance_51.json.pre_compfix_20260518 +0 -623
  17. byeon2022/instance/large_instance_51.json.pre_scaleup_20260519 +0 -624
  18. byeon2022/instance/tiny_instance.json +0 -382
  19. byeon2022/instance_schema.json +0 -115
  20. byeon2022/mathematical_formulation.md +0 -47
  21. byeon2022/problem_description.txt +0 -19
  22. byeon2022/solution_logger.py +0 -61
  23. byeon2022/solution_schema.json +0 -23
  24. caprara1999/feasibility_check.py +0 -166
  25. caprara1999/gurobi_code.py +0 -143
  26. caprara1999/gurobi_feasi_result/large_feasi_result_1.json +0 -6
  27. caprara1999/gurobi_feasi_result/large_feasi_result_2.json +0 -6
  28. caprara1999/gurobi_feasi_result/large_feasi_result_3.json +0 -6
  29. caprara1999/gurobi_feasi_result/large_feasi_result_4.json +0 -6
  30. caprara1999/gurobi_feasi_result/large_feasi_result_5.json +0 -6
  31. caprara1999/gurobi_feasi_result/tiny_feasi_result.json +0 -6
  32. caprara1999/gurobi_solution/large_solution_1.json +0 -305
  33. caprara1999/gurobi_solution/large_solution_2.json +0 -305
  34. caprara1999/gurobi_solution/large_solution_3.json +0 -305
  35. caprara1999/gurobi_solution/large_solution_4.json +0 -305
  36. caprara1999/gurobi_solution/large_solution_5.json +0 -305
  37. caprara1999/gurobi_solution/tiny_solution.json +0 -25
  38. caprara1999/gurobi_solution_log/large_log_1.jsonl +0 -1
  39. caprara1999/gurobi_solution_log/large_log_2.jsonl +0 -1
  40. caprara1999/gurobi_solution_log/large_log_3.jsonl +0 -1
  41. caprara1999/gurobi_solution_log/large_log_4.jsonl +0 -1
  42. caprara1999/gurobi_solution_log/large_log_5.jsonl +0 -1
  43. caprara1999/gurobi_solution_log/tiny_log.jsonl +0 -1
  44. caprara1999/instance/large_instance_1.json +0 -0
  45. caprara1999/instance/large_instance_2.json +0 -0
  46. caprara1999/instance/large_instance_3.json +0 -0
  47. caprara1999/instance/large_instance_4.json +0 -0
  48. caprara1999/instance/large_instance_5.json +0 -0
  49. caprara1999/instance/tiny_instance.json +0 -469
  50. caprara1999/instance_schema.json +0 -7
byeon2022/gurobi_solution_log/large_log_3.jsonl DELETED
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byeon2022/gurobi_solution_log/large_log_4.jsonl DELETED
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byeon2022/gurobi_solution_log/large_log_5.jsonl DELETED
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byeon2022/gurobi_solution_log/tiny_log.jsonl DELETED
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- "description": " [fix 2026-05-18 unified: (1) compressor ratio_lb\u21921.0/ub\u2192max(orig,3.0) to remove topology cycle constraints, (2) source pressure_ub_squared \u00d73, (3) all pipeline weymouth_factor \u00d70.1 to give 10\u00d7 flow capacity at same pressure differential \u2014 IIS analysis showed combined infeasibility from compressor LB cycle + tight source pressure + Weymouth W too large for actual gas demand] [scale-up 2026-05-19 FINAL: time_periods \u2192 24]"
871
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- "control_ratio_ub": null
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- },
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- {
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- "id": 4,
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- "from_junction": 1,
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- "to_junction": 0,
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- "type": "compressor",
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- "weymouth_factor": null,
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- "compression_ratio_lb": 1.0,
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- "compression_ratio_ub": 3.0,
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- "control_ratio_lb": null,
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- "control_ratio_ub": null
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- }
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- ]
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- },
623
- "description": " [fix 2026-05-18 unified: (1) compressor ratio_lb\u21921.0/ub\u2192max(orig,3.0) to remove topology cycle constraints, (2) source pressure_ub_squared \u00d73, (3) all pipeline weymouth_factor \u00d70.1 to give 10\u00d7 flow capacity at same pressure differential \u2014 IIS analysis showed combined infeasibility from compressor LB cycle + tight source pressure + Weymouth W too large for actual gas demand]"
624
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
byeon2022/instance/tiny_instance.json DELETED
@@ -1,382 +0,0 @@
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- {
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- "instance_id": "instance_1",
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- "scale": "small",
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- "problem_type": "UCGNA_BSOCP",
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- {
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- "voltage_angle_ub": 1.570796
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- }
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- ],
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- "lines": [
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- {
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- {
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- {
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- {
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- "angle_diff_limit": 1.047198
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- ]
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- },
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- "generators": {
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- "num_generators": 4,
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- {
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- "id": 0,
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- "max_power": 114.7327,
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- "ramp_down": 49.1562,
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- "ramp_up": 49.1562,
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- "min_down_time": 1,
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- "initial_status": 1,
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- "initial_gen": 34.2365,
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- "initial_inactive_periods": 0,
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- "startup_cost_params": [
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- "price": 20.0,
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- "max_amount": 26.1983
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- {
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- "id": 1,
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- "price": 31.286,
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- "max_amount": 62.0732
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- ],
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- "heat_rate_coefficients": {
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- "H_u0": 4.712178
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- {
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- "gas_junction": null,
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- "min_power": 20.0226,
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- "max_power": 96.2476,
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- "startup_cost_params": [
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- ]
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- "num_bids": 2,
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- "bids": [
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- {
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- },
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- {
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- "price": 31.6389,
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- ],
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- },
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- {
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- "max_power": 112.6776,
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- "initial_status": 1,
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- "startup_cost_params": [
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- ],
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- "bids": [
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- {
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- },
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- {
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- "id": 1,
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- "max_amount": 40.3701
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- {
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- ],
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- "heat_rate_coefficients": null,
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- },
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- "gas_network": {
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- "num_connections": 3,
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- "num_compressors": 1,
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- "num_pricing_zones": 1,
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- "max_gas_price_mmBtu": 200.0,
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- "min_gas_price_mmBtu": 0.0,
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- "pricing_zones": [
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- {
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- }
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- "junctions": [
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- "id": 0,
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- "pressure_ub_squared": 93.4578,
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- ],
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- {
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- "interval_ub": 164.3707,
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- "slope": 1.967
310
- },
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- {
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- "interval_lb": 164.3707,
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- "interval_ub": 328.7414,
315
- "slope": 2.9964
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- }
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- ]
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- },
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- {
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- "pressure_ub_squared": 93.6722,
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- "demand_shedding_cost": 671.7289,
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- "gas_demand_profile": [
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- ],
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- },
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- {
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- "pressure_ub_squared": 158.8427,
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- "gas_demand_profile": [
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- ],
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- "supply_intervals": []
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- }
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- ],
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- "connections": [
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- {
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- "id": 0,
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- "from_junction": 0,
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- "type": "pipeline",
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- "control_ratio_ub": null
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- {
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- "control_ratio_ub": null
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- },
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- {
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- "id": 2,
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372
- "to_junction": 0,
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- "compression_ratio_ub": 1.8539,
377
- "control_ratio_lb": null,
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- "control_ratio_ub": null
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- }
380
- ]
381
- }
382
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
byeon2022/instance_schema.json DELETED
@@ -1,115 +0,0 @@
1
- {
2
- "instance_id": "<str> Unique identifier for this problem instance.",
3
- "scale": "<str> Size category of the instance (small, medium, or large).",
4
- "problem_type": "<str> Type of optimization problem formulation.",
5
- "stress_parameters": {
6
- "eta_p": "<float> Multiplier applied to base electricity demand across all buses.",
7
- "eta_g": "<float> Multiplier applied to base gas demand across all junctions."
8
- },
9
- "time_periods": "<int> Number of time periods in the planning horizon.",
10
- "electricity_network": {
11
- "num_buses": "<int> Number of buses in the electricity transmission grid.",
12
- "num_lines": "<int> Number of transmission lines connecting buses.",
13
- "buses": [
14
- {
15
- "id": "<int> Unique identifier for this bus.",
16
- "demand_profile": "<list[float], length time_periods> Electricity demand at this bus in each time period (MW).",
17
- "voltage_angle_lb": "<float> Lower bound on the voltage phase angle at this bus (radians).",
18
- "voltage_angle_ub": "<float> Upper bound on the voltage phase angle at this bus (radians)."
19
- }
20
- ],
21
- "lines": [
22
- {
23
- "id": "<int> Unique identifier for this transmission line.",
24
- "from_bus": "<int> Bus at one endpoint of the line.",
25
- "to_bus": "<int> Bus at the other endpoint of the line.",
26
- "susceptance": "<float> Electrical susceptance of the line relating power flow to voltage angle difference (MW/rad).",
27
- "thermal_limit": "<float> Maximum allowable power flow on the line (MW).",
28
- "angle_diff_limit": "<float> Maximum allowable voltage angle difference between the two endpoint buses (radians)."
29
- }
30
- ]
31
- },
32
- "generators": {
33
- "num_generators": "<int> Total number of generators in the system.",
34
- "num_gfpp": "<int> Number of gas-fired power plants among the generators.",
35
- "generators": [
36
- {
37
- "id": "<int> Unique identifier for this generator.",
38
- "bus": "<int> Bus where this generator is located.",
39
- "is_gfpp": "<bool> Whether this generator is a gas-fired power plant that consumes natural gas.",
40
- "gas_junction": "<int or null> Gas network junction from which this GFPP draws fuel, or null for non-GFPP generators.",
41
- "min_power": "<float> Minimum real power output when the generator is on (MW).",
42
- "max_power": "<float> Maximum real power output (MW).",
43
- "ramp_down": "<float> Maximum decrease in power output between consecutive time periods (MW).",
44
- "ramp_up": "<float> Maximum increase in power output between consecutive time periods (MW).",
45
- "no_load_cost": "<float> Fixed operating cost incurred each period the generator is on ($).",
46
- "min_up_time": "<int> Minimum number of consecutive periods the generator must remain on after starting up.",
47
- "min_down_time": "<int> Minimum number of consecutive periods the generator must remain off after shutting down.",
48
- "initial_status": "<int> On/off status of the generator at the start of the horizon (1 = on, 0 = off).",
49
- "initial_gen": "<float> Power output of the generator in the pre-horizon period (MW).",
50
- "initial_active_periods": "<int> Number of consecutive periods the generator has been on at the start of the horizon.",
51
- "initial_inactive_periods": "<int> Number of consecutive periods the generator has been off at the start of the horizon.",
52
- "startup_cost_params": "<list[list[float]], length min_up_time> Startup cost tiers, each a pair of lookback window length (periods) and associated startup cost ($).",
53
- "num_bids": "<int> Number of supply bid segments for this generator.",
54
- "bids": [
55
- {
56
- "id": "<int> Unique identifier for this bid segment.",
57
- "price": "<float> Marginal price of electricity offered in this bid segment ($/MWh).",
58
- "max_amount": "<float> Maximum generation quantity available in this bid segment (MW)."
59
- }
60
- ],
61
- "heat_rate_coefficients": {
62
- "H_u2": "<float or null> Quadratic coefficient of the heat rate curve relating power output to gas consumption (mmBtu/MW^2h).",
63
- "H_u1": "<float or null> Linear coefficient of the heat rate curve (mmBtu/MWh).",
64
- "H_u0": "<float or null> Constant coefficient of the heat rate curve (mmBtu/h)."
65
- },
66
- "max_gas_price_fraction": "<float or null> Maximum fraction of the global gas price upper bound at which this GFPP's bids remain profitable, or null for non-GFPP generators."
67
- }
68
- ]
69
- },
70
- "gas_network": {
71
- "num_junctions": "<int> Number of junctions in the gas transmission network.",
72
- "num_connections": "<int> Total number of connections (pipelines and compressors) in the gas network.",
73
- "num_compressors": "<int> Number of compressor connections in the gas network.",
74
- "num_pricing_zones": "<int> Number of gas pricing zones partitioning the junctions.",
75
- "max_gas_price_mmBtu": "<float> Global upper bound on the gas price ($/mmBtu).",
76
- "min_gas_price_mmBtu": "<float> Global lower bound on the gas price ($/mmBtu).",
77
- "pricing_zones": [
78
- {
79
- "id": "<int> Unique identifier for this pricing zone.",
80
- "junctions": "<list[int]> Gas junctions belonging to this pricing zone."
81
- }
82
- ],
83
- "junctions": [
84
- {
85
- "id": "<int> Unique identifier for this gas junction.",
86
- "is_source": "<bool> Whether this junction is a gas supply source.",
87
- "pressure_lb_squared": "<float> Lower bound on the squared pressure at this junction.",
88
- "pressure_ub_squared": "<float> Upper bound on the squared pressure at this junction.",
89
- "demand_shedding_cost": "<float> Penalty cost per unit of unmet gas demand at this junction ($/unit).",
90
- "gas_demand_profile": "<list[float], length time_periods> Exogenous gas demand at this junction in each time period.",
91
- "supply_intervals": [
92
- {
93
- "id": "<int> Unique identifier for this supply interval.",
94
- "interval_lb": "<float> Lower bound on gas production in this interval.",
95
- "interval_ub": "<float> Upper bound on gas production in this interval.",
96
- "slope": "<float> Marginal cost of gas supply within this interval ($/unit)."
97
- }
98
- ]
99
- }
100
- ],
101
- "connections": [
102
- {
103
- "id": "<int> Unique identifier for this gas network connection.",
104
- "from_junction": "<int> Junction at the upstream end of the connection.",
105
- "to_junction": "<int> Junction at the downstream end of the connection.",
106
- "type": "<str> Type of connection (pipeline or compressor).",
107
- "weymouth_factor": "<float or null> Resistance factor relating pressure drop to squared gas flow for pipelines, or null for compressors.",
108
- "compression_ratio_lb": "<float or null> Lower bound on the pressure compression ratio for compressors, or null for pipelines.",
109
- "compression_ratio_ub": "<float or null> Upper bound on the pressure compression ratio for compressors, or null for pipelines.",
110
- "control_ratio_lb": "<float or null> Lower bound on the control valve ratio, or null if not applicable.",
111
- "control_ratio_ub": "<float or null> Upper bound on the control valve ratio, or null if not applicable."
112
- }
113
- ]
114
- }
115
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
byeon2022/mathematical_formulation.md DELETED
@@ -1,47 +0,0 @@
1
- # Original Formulation: Bilevel Second-Order Cone Program (BSOCP)
2
-
3
- *Source: Benders Subproblem Decomposition for Bilevel Problems with Convex Follower, Byeon and Van Hentenryck, 2022.*
4
-
5
- ## Sets and Parameters
6
-
7
- - $n_x, n_y$: dimensions of the leader and follower variable vectors.
8
-
9
- - $m_x, m_y$: numbers of leader and follower constraints.
10
-
11
- - $\mathcal{I} \subseteq \{1,\dots,n_x\}$: index set of integer leader variables.
12
-
13
- - $\mathcal{J} \subseteq \mathcal{I}$: indices $i$ such that column $i$ of $A$ is nonzero (leader variables that appear in the follower problem).
14
-
15
- - $c_x \in \mathbb{R}^{n_x}$, $c_y \in \mathbb{R}^{n_y}$: leader objective coefficients.
16
-
17
- - $G_x \in \mathbb{R}^{m_x \times n_x}$, $G_y \in \mathbb{R}^{m_x \times n_y}$, $h \in \mathbb{R}^{m_x}$: leader constraint data.
18
-
19
- - $A \in \mathbb{R}^{m_y \times n_x}$, $B \in \mathbb{R}^{m_y \times n_y}$, $b \in \mathbb{R}^{m_y}$, $d \in \mathbb{R}^{n_y}$: follower constraint and objective data.
20
-
21
- - $\underline{x}_i, \overline{x}_i$: lower and upper bounds on $x_i$ (finite for $i \in \mathcal{J}$).
22
-
23
- - $\mathcal{K}_x, \mathcal{K}_y$: Cartesian products of second-order cones and nonnegative orthants (the ambient cones for $x$ and $y$).
24
-
25
- ## Decision Variables
26
-
27
- - $x \in \mathbb{R}^{n_x}$: leader (upper-level) decision variables.
28
-
29
- - $y \in \mathbb{R}^{n_y}$: follower (lower-level) decision variables.
30
-
31
- ## Objective
32
-
33
- $$\begin{equation}
34
- \min_{x,\,y} \quad c_x^{\top} x + c_y^{\top} y \tag{1a}
35
- \end{equation}$$
36
-
37
- ## Constraints (Bilevel)
38
-
39
- $$\begin{align}
40
- G_x x + G_y y & \;\geq\; h, \tag{1b} \\[2pt]
41
- x & \;\in\; \mathcal{X} \;:=\; \Bigl\{ x \in \mathcal{K}_x \;:\;
42
- x_i \in [\underline{x}_i,\overline{x}_i] \cap \mathbb{Z},\;\forall i \in \mathcal{I} \Bigr\}, \tag{1c} \\[2pt]
43
- y & \;\in\; \arg\min_{y' \in \mathcal{K}_y}
44
- \Bigl\{\, d^{\top} y' \;:\; A x + B y' \geq b \,\Bigr\}. \tag{1d}
45
- \end{align}$$
46
-
47
- Constraint (1d) enforces that $y$ is an optimal response of the follower to the leader decision $x$ (optimistic bilevel); the problem is an MISOCP-follower bilevel program. Under Assumption 2(b) integer bounded $x_i$ for $i \in \mathcal{J}$ may be encoded as binary without loss of generality.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
byeon2022/problem_description.txt DELETED
@@ -1,19 +0,0 @@
1
- # Problem Description
2
-
3
- This problem concerns the Unit Commitment with Gas Network Awareness (UCGNA), a bilevel optimization problem that coordinates interdependent electricity and natural gas systems over a finite planning horizon. The electricity transmission grid is an undirected graph of buses connected by transmission lines, and the natural gas transmission system is a directed graph of junctions connected by pipelines, compressors, and control valves. A set of time periods spans the planning horizon. Some generators are conventional, while others are gas-fired power plants (GFPPs) that consume natural gas from the gas network at specified junctions. Gas junctions are partitioned into pricing zones, each of which has a single zonal gas price.
4
-
5
- The input data for the electricity network specifies, for each bus, the electricity demand profile over all time periods, and lower and upper bounds on the voltage angle. For each transmission line, the data provides the susceptance, the thermal flow limit, and the maximum allowable voltage angle difference between its two endpoint buses. For each generator, the data specifies which bus it is located at, whether it is a GFPP (and if so, which gas junction it draws fuel from), its minimum and maximum real power output, its ramp-up and ramp-down rates, its no-load cost, its minimum up-time and minimum down-time, its initial on/off status, its initial power output, the number of time periods it has been active or inactive at the start of the horizon, and a list of startup cost tiers each defined by a lookback window in periods and an associated cost. Each generator submits a set of supply bids, where each bid has a price and a maximum generation amount. Each GFPP additionally has heat-rate curve coefficients (a quadratic, linear, and constant term) relating power output to gas consumption, a maximum allowable fraction of the gas price at which its bids remain profitable, and a per-bid profitability threshold representing the maximum gas price at which that particular bid remains profitable.
6
-
7
- The input data for the gas network specifies, for each junction, whether it is a gas source, the lower and upper bounds on squared pressure, a cost of shedding gas demand, and a gas demand profile over all time periods. Source junctions provide a set of non-overlapping gas supply intervals, each defined by lower and upper production limits and a marginal supply cost (slope), with slopes ordered from cheapest to most expensive across successive intervals. For each pipeline connection, the data provides a Weymouth resistance factor relating pressure drop to the square of gas flow. For each compressor, the data provides lower and upper bounds on the compression ratio. For each control valve, the data provides lower and upper bounds on the control ratio. A global maximum and minimum gas price per unit of energy (mmBtu) are given.
8
-
9
- The problem involves two levels of decision-making. At the upper level (the leader), the system operator must decide, for each generator and each time period, whether the generator is on or off, whether it starts up or shuts down, which supply bids are selected, the startup cost incurred, and, for each GFPP, the maximum allowable gas price at its scheduled output level. At the lower level (the follower), given the commitment decisions, the system must determine the hourly dispatch: the real power output of each generator decomposed into bid amounts, the power flow on each transmission line, the voltage angle at each bus, the gas supply at each source junction decomposed into supply-interval amounts, the gas flow on each connection, the squared pressure at each junction, the satisfied and shed gas demand at each junction, and the total gas consumed by GFPPs at each junction. The gas zonal prices, which emerge as equilibrium prices from the dispatch-level gas-balance conditions, also arise from the lower-level solution.
10
-
11
- The leader's choices must satisfy several rules. Each generator's initial on/off status is fixed for the first period and must remain unchanged for any remaining periods dictated by the initial active or inactive duration. The startup cost in each period must be at least as large as the applicable startup cost tier, determined by comparing the current on status against the on/off history over the lookback window. The startup cost must also be nonnegative. The minimum up-time rule requires that the sum of startup indicators over any window of consecutive periods equal in length to the minimum up-time must not exceed the on/off indicator of the last period in that window. The minimum down-time rule requires that the sum of startup indicators over any window of consecutive periods equal in length to the minimum down-time must not exceed one minus the on/off indicator of the period immediately before that window. The startup and shutdown indicators relate to the on/off indicators by the rule that the startup indicator minus the shutdown indicator equals the change in on/off status from the previous period to the current period. A bid of a GFPP can only be selected if that generator is on. For GFPPs, the generation from each bid is bounded above by the bid's maximum amount times the bid selection indicator. GFPP bids are activated sequentially: the next bid can be selected only if the current bid is fully utilized. For each GFPP in each period, the maximum allowable gas price is determined by the per-bid profitability thresholds and the bid selection pattern: for each bid except the last, the bid's profitability threshold is multiplied by the difference between that bid's selection indicator and the next bid's selection indicator, and the last bid's threshold is multiplied by its own selection indicator, with the sum of these products giving the maximum allowable gas price. For each committed GFPP at a junction in a given pricing zone, this maximum allowable gas price must be at least as large as the zonal gas price when the generator is on (that is, at least the product of the zonal gas price and the on/off indicator). This nonlinear relationship is represented exactly through linear inequalities that introduce auxiliary quantities and use the global gas price bounds.
12
-
13
- The follower's dispatch must satisfy the following rules. At each bus and each time period, the total power generated by all generators at that bus minus the bus demand equals the net outgoing power flow (outgoing minus incoming flows on lines incident to that bus). Each generator's total output equals the sum of its bid amounts. Each bid's generation amount is bounded between zero and the bid's maximum capacity. Each generator's output is bounded between its minimum and maximum power times the on/off indicator. For ramp-up, the increase in output from one period to the next is limited by the ramp-up rate times the prior on/off indicator plus the maximum power times the startup indicator. For ramp-down, the decrease in output is limited by the ramp-down rate times the current on/off indicator plus the minimum power times the shutdown indicator. The initial output in the pre-horizon period is fixed. Power flow on each line equals the line susceptance times the difference in voltage angles between the from-bus and to-bus. Each flow is bounded in absolute value by the line's thermal limit. The voltage angle difference across each line is bounded in absolute value by the line's angle difference limit.
14
-
15
- For the gas network, at each junction and each time period, the gas supply minus the satisfied demand minus the total GFPP gas consumption equals the net outgoing gas flow. At source junctions the total supply equals the sum of supply-interval amounts, and each interval amount is bounded by the interval's capacity. At non-source junctions the supply is zero. The satisfied demand plus the shed demand equals the exogenous gas demand, and shed demand cannot exceed the gas demand. Gas flows on all connections are nonnegative. For pipelines, the Weymouth equation is enforced as a convex relaxation: the squared pressure at the from-junction minus the squared pressure at the to-junction is at least the Weymouth factor times the square of the gas flow. For compressors, the squared pressure at the to-junction divided by the squared pressure at the from-junction lies between the square of the lower compression ratio and the square of the upper compression ratio (equivalently, the to-junction squared pressure is bounded between these ratio-squared values times the from-junction squared pressure). For control valves, the to-junction squared pressure is similarly bounded between the square of the lower control ratio and the square of the upper control ratio times the from-junction squared pressure. Squared pressures at all junctions are bounded within their specified lower and upper limits. The total gas consumed by GFPPs at each junction that is also a bus in the electricity network is at least the sum, over all GFPPs located at that junction, of the quadratic heat-rate function of that generator's power output: the quadratic coefficient times the square of the output plus the linear coefficient times the output plus the constant coefficient times the on/off indicator. This is also enforced as a convex relaxation.
16
-
17
- The two-level structure requires that the follower's dispatch is optimal for the follower's own cost given the leader's commitment decisions. When multiple equally good dispatch solutions exist, the one most favorable to the leader is chosen. The follower's cost is a weighted sum of electricity dispatch costs and gas dispatch costs, where the weight on electricity costs is a parameter between zero and one and the weight on gas costs is one minus that parameter. The electricity dispatch cost component sums over all time periods, generators, and bids the bid price times the bid generation amount. The gas dispatch cost component sums over all time periods and junctions the demand-shedding cost times the shed demand, plus, at source junctions, the supply-interval slope times the interval supply amount.
18
-
19
- The leader's goal is to minimize the total system cost, which is a weighted combination using the same weighting parameter. The electricity component sums over all time periods and generators the no-load cost times the on/off indicator plus the startup cost, plus the sum of bid prices times bid generation amounts. The gas component is the same as the follower's gas cost. The leader is additionally subject to the requirement that the gas zonal prices (from the dispatch-level solution) together with the commitment decisions satisfy the bid-validity rules described above, ensuring robustness of the commitment against volatile gas prices.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
byeon2022/solution_logger.py DELETED
@@ -1,61 +0,0 @@
1
- """
2
- Convergence logger for optimization algorithms.
3
-
4
- Records incumbent solutions with timestamps to a JSONL file.
5
- This module is provided to LLM-generated programs — they only need to call
6
- `log(objective_value)` whenever a better feasible solution is found.
7
-
8
- Usage in generated code:
9
- from solution_logger import SolutionLogger
10
- logger = SolutionLogger(log_path, sense="minimize") # or "maximize"
11
- # ... inside algorithm loop:
12
- logger.log(objective_value)
13
- """
14
-
15
- import json
16
- import time
17
-
18
-
19
- class SolutionLogger:
20
- def __init__(self, log_path, sense="minimize"):
21
- """
22
- Args:
23
- log_path: Path to the JSONL output file.
24
- sense: "minimize" or "maximize".
25
- """
26
- self.log_path = log_path
27
- self.sense = sense
28
- self.start_time = time.time()
29
- self.best_obj = None
30
- self.min_interval = 0.1 # seconds, avoid excessive writes
31
-
32
- self._last_log_time = 0.0
33
- # Clear the file
34
- with open(self.log_path, "w") as f:
35
- pass
36
-
37
- def log(self, objective_value):
38
- """Record a new incumbent if it improves on the best known."""
39
- if objective_value is None:
40
- return
41
-
42
- # Check if this is an improvement
43
- if self.best_obj is not None:
44
- if self.sense == "minimize" and objective_value >= self.best_obj:
45
- return
46
- if self.sense == "maximize" and objective_value <= self.best_obj:
47
- return
48
-
49
- elapsed = time.time() - self.start_time
50
-
51
- # Throttle writes
52
- if self.best_obj is not None and elapsed - self._last_log_time < self.min_interval:
53
- self.best_obj = objective_value
54
- return
55
-
56
- self.best_obj = objective_value
57
- self._last_log_time = elapsed
58
-
59
- with open(self.log_path, "a") as f:
60
- f.write(json.dumps({"time": round(elapsed, 3),
61
- "objective_value": objective_value}) + "\n")
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
byeon2022/solution_schema.json DELETED
@@ -1,23 +0,0 @@
1
- {
2
- "objective_value": "<float> Total weighted system cost combining electricity commitment-plus-dispatch cost and gas dispatch cost.",
3
- "primary_variables": {
4
- "o_{generator}_{period}": "<float> Whether the generator is on (1) or off (0) in each period, with period 0 being the fixed pre-horizon state.",
5
- "vp_{generator}_{period}": "<float> Whether the generator starts up at the beginning of each period.",
6
- "vm_{generator}_{period}": "<float> Whether the generator shuts down at the beginning of each period.",
7
- "w_{generator}_{bid}_{period}": "<float> Whether the given supply bid of the generator is selected in each period.",
8
- "r_{generator}_{period}": "<float> Startup cost incurred by the generator in each period.",
9
- "phimax_{generator}_{period}": "<float> Maximum allowable gas price at which the committed gas-fired power plant's scheduled output remains profitable in each period.",
10
- "p_{generator}_{period}": "<float> Real power output of the generator in each period, with period 0 being the fixed pre-horizon output.",
11
- "se_{generator}_{bid}_{period}": "<float> Real power output supplied by the given bid of the generator in each period.",
12
- "f_{line}_{period}": "<float> Real power flow on the transmission line from its from-bus to its to-bus in each period.",
13
- "theta_{bus}_{period}": "<float> Voltage angle at the bus in each period.",
14
- "sg_{junction}_{period}": "<float> Total gas supply provided at the junction in each period.",
15
- "sgs_{junction}_{supply_interval}_{period}": "<float> Gas supply provided from the given supply interval of the source junction in each period.",
16
- "pisq_{junction}_{period}": "<float> Squared pressure at the gas junction in each period.",
17
- "phig_{connection}_{period}": "<float> Gas flow on the gas-network connection from its from-junction to its to-junction in each period.",
18
- "lg_{junction}_{period}": "<float> Satisfied gas demand at the junction in each period.",
19
- "qg_{junction}_{period}": "<float> Shed gas demand at the junction in each period.",
20
- "gamma_{junction}_{period}": "<float> Total gas consumed by gas-fired power plants at the junction in each period.",
21
- "psi_{pricing_zone}_{period}": "<float> Zonal gas price for the pricing zone in each period."
22
- }
23
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
caprara1999/feasibility_check.py DELETED
@@ -1,166 +0,0 @@
1
- #!/usr/bin/env python3
2
- """
3
- Feasibility checker for the Quadratic Knapsack Problem (QKP).
4
-
5
- Checks hard constraints from Caprara, Pisinger, and Toth (1999):
6
- Constraint 1: sum_{j in N} w_j x_j <= c (capacity)
7
- Constraint 2: x_j in {0, 1} for all j in N (binary / integrality)
8
- Constraint 3: objective consistency
9
- reported objective_value must equal recomputed
10
- sum_{i in N} sum_{j in N} p_{ij} x_i x_j
11
- within a small tolerance (Tier C defence against
12
- self-reported-objective exploits). All variables determining
13
- the objective (the selected_items vector x) are present in
14
- the solution, so a full recompute is exact.
15
- """
16
-
17
- import argparse
18
- import json
19
-
20
-
21
- def check_feasibility(instance, solution):
22
- tol = 1e-5
23
- eps = 1e-5
24
-
25
- n = instance["n"]
26
- weights = instance["weights"]
27
- capacity = instance["capacity"]
28
- profit_matrix = instance["profit_matrix"]
29
-
30
- # Parse selected_items: could be a binary vector [0,1,0,...] or a list of indices [3,4,7,...]
31
- raw_items = solution["selected_items"]
32
- if len(raw_items) == n and all(v in (0, 1, 0.0, 1.0) for v in raw_items):
33
- # Binary vector format
34
- x = [float(v) for v in raw_items]
35
- else:
36
- # List of selected indices format
37
- x = [0.0] * n
38
- for idx in raw_items:
39
- x[idx] = 1.0
40
-
41
- violated_constraints = set()
42
- violations = []
43
- violation_magnitudes = []
44
-
45
- # ---- Constraint 1: capacity constraint ----
46
- # sum_{j in N} w_j x_j <= c
47
- lhs_cap = sum(weights[j] * x[j] for j in range(n))
48
- rhs_cap = float(capacity)
49
- violation_amount_cap = max(lhs_cap - rhs_cap, 0.0)
50
-
51
- if violation_amount_cap > tol:
52
- violated_constraints.add(1)
53
- violations.append(
54
- f"Capacity constraint violated: total weight {lhs_cap} exceeds capacity {rhs_cap}"
55
- )
56
- normalizer = max(abs(rhs_cap), eps)
57
- violation_magnitudes.append({
58
- "constraint": 1,
59
- "lhs": lhs_cap,
60
- "rhs": rhs_cap,
61
- "raw_excess": violation_amount_cap,
62
- "normalizer": normalizer,
63
- "ratio": violation_amount_cap / normalizer,
64
- })
65
-
66
- # ---- Constraint 2: binary / integrality constraint ----
67
- # x_j in {0, 1} for all j in N
68
- for j in range(n):
69
- diff = min(abs(x[j] - 0.0), abs(x[j] - 1.0))
70
- if diff > tol:
71
- violated_constraints.add(2)
72
- rhs_val = round(x[j]) # nearest integer
73
- violation_amount = diff
74
- violations.append(
75
- f"Integrality violated for item {j}: x[{j}] = {x[j]} is not binary"
76
- )
77
- normalizer = max(abs(rhs_val), eps)
78
- violation_magnitudes.append({
79
- "constraint": 2,
80
- "lhs": x[j],
81
- "rhs": float(rhs_val),
82
- "raw_excess": violation_amount,
83
- "normalizer": normalizer,
84
- "ratio": violation_amount / normalizer,
85
- })
86
-
87
- # ---- Constraint 3: objective consistency (Tier C) ----
88
- # reported objective_value == sum_i sum_j p_{ij} x_i x_j
89
- # Full recompute is exact: selected_items contains every variable that
90
- # determines the objective. Tolerance: 0.1% relative with a 0.5 absolute
91
- # floor, since profit_matrix entries are integers and any honest mismatch
92
- # is at least 1.
93
- reported_obj = solution.get("objective_value")
94
- try:
95
- reported = float(reported_obj) if reported_obj is not None else None
96
- except (TypeError, ValueError):
97
- reported = None
98
- if reported is not None:
99
- # Iterate only over the selected items for efficiency (n can be 300+).
100
- selected = [j for j in range(n) if x[j] > 0.5]
101
- true_obj = 0.0
102
- for i in selected:
103
- row = profit_matrix[i]
104
- for j in selected:
105
- true_obj += row[j]
106
- true_obj = float(true_obj)
107
- abs_diff = abs(reported - true_obj)
108
- obj_tol = max(0.5, 1e-3 * abs(true_obj))
109
- if abs_diff > obj_tol:
110
- violated_constraints.add(3)
111
- violations.append(
112
- f"Objective consistency violated: reported objective_value="
113
- f"{reported} differs from recomputed sum_i sum_j p_ij x_i x_j="
114
- f"{true_obj} (|diff|={abs_diff:.6g}, tol={obj_tol:.6g})"
115
- )
116
- normalizer = max(abs(true_obj), eps)
117
- violation_magnitudes.append({
118
- "constraint": 3,
119
- "lhs": float(reported),
120
- "rhs": float(true_obj),
121
- "raw_excess": float(abs_diff),
122
- "normalizer": float(normalizer),
123
- "ratio": float(abs_diff / normalizer),
124
- })
125
-
126
- feasible = len(violated_constraints) == 0
127
-
128
- return {
129
- "feasible": feasible,
130
- "violated_constraints": sorted(violated_constraints),
131
- "violations": violations,
132
- "violation_magnitudes": violation_magnitudes,
133
- }
134
-
135
-
136
- def main():
137
- parser = argparse.ArgumentParser(
138
- description="Feasibility checker for QKP (Caprara et al. 1999)"
139
- )
140
- parser.add_argument("--instance_path", type=str, required=True,
141
- help="Path to instance JSON file")
142
- parser.add_argument("--solution_path", type=str, required=True,
143
- help="Path to solution JSON file")
144
- parser.add_argument("--result_path", type=str, required=True,
145
- help="Path to write feasibility result JSON file")
146
- args = parser.parse_args()
147
-
148
- with open(args.instance_path, "r") as f:
149
- instance = json.load(f)
150
-
151
- with open(args.solution_path, "r") as f:
152
- solution = json.load(f)
153
-
154
- result = check_feasibility(instance, solution)
155
-
156
- with open(args.result_path, "w") as f:
157
- json.dump(result, f, indent=2)
158
-
159
- print(f"Feasible: {result['feasible']}")
160
- if not result["feasible"]:
161
- for v in result["violations"]:
162
- print(f" - {v}")
163
-
164
-
165
- if __name__ == "__main__":
166
- main()
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
caprara1999/gurobi_code.py DELETED
@@ -1,143 +0,0 @@
1
- """
2
- Quadratic Knapsack Problem (QKP) solver using Gurobi.
3
-
4
- Implements the linearized ILP formulation from:
5
- Caprara, Pisinger, and Toth (1999),
6
- "Exact Solution of the Quadratic Knapsack Problem"
7
-
8
- Linearized ILP:
9
- maximize sum_{j in N} sum_{i in N\{j}} p_{ij} y_{ij} + sum_{j in N} q_j x_j
10
- subject to:
11
- sum_{j in N} w_j x_j <= c
12
- sum_{i in N\{j}} w_i y_{ij} <= (c - w_j) x_j, for all j in N
13
- 0 <= y_{ij} <= x_j, for all i,j in N, j != i
14
- y_{ij} = y_{ji}, for all i,j in N, j > i
15
- x_j, y_{ij} in {0,1}
16
- """
17
-
18
- import argparse
19
- import json
20
- import gurobipy as gp
21
- from gurobipy import GRB
22
- import os as _os, sys as _sys
23
- # Walk up from this file's directory to find repo root (containing scripts/).
24
- _repo = _os.path.dirname(_os.path.abspath(__file__))
25
- while _repo != _os.path.dirname(_repo) and not _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')):
26
- _repo = _os.path.dirname(_repo)
27
- if _os.path.isdir(_os.path.join(_repo, 'scripts', 'utils')):
28
- _sys.path.insert(0, _repo)
29
- try:
30
- from scripts.utils.gurobi_log_helper import install_gurobi_logger
31
- except ImportError:
32
- def install_gurobi_logger(log_path): # no-op fallback when scripts/ unavailable
33
- pass
34
-
35
-
36
- def solve_qkp(instance_path: str, solution_path: str, time_limit: float) -> None:
37
- # Load instance
38
- with open(instance_path, "r") as f:
39
- data = json.load(f)
40
-
41
- n = data["n"]
42
- capacity = data["capacity"]
43
- weights = data["weights"]
44
- P = data["profit_matrix"]
45
-
46
- N = range(n)
47
-
48
- # Diagonal entries are the individual item profits q_j
49
- q = [P[j][j] for j in N]
50
-
51
- # Build model
52
- model = gp.Model("QKP")
53
- model.setParam("Threads", 1)
54
- model.setParam("TimeLimit", time_limit)
55
-
56
- # Decision variables
57
- x = model.addVars(N, vtype=GRB.BINARY, name="x")
58
-
59
- # y_{ij} for i != j
60
- y = {}
61
- for i in N:
62
- for j in N:
63
- if i != j:
64
- y[i, j] = model.addVar(vtype=GRB.BINARY, name=f"y_{i}_{j}")
65
-
66
- model.update()
67
-
68
- # Objective (2): sum_{j} sum_{i != j} p_{ij} y_{ij} + sum_{j} q_j x_j
69
- obj = gp.quicksum(P[i][j] * y[i, j] for i in N for j in N if i != j)
70
- obj += gp.quicksum(q[j] * x[j] for j in N)
71
- model.setObjective(obj, GRB.MAXIMIZE)
72
-
73
- # Constraint (3): capacity constraint
74
- model.addConstr(
75
- gp.quicksum(weights[j] * x[j] for j in N) <= capacity,
76
- name="capacity"
77
- )
78
-
79
- # Constraint (4): surrogate knapsack constraints for each j
80
- for j in N:
81
- model.addConstr(
82
- gp.quicksum(weights[i] * y[i, j] for i in N if i != j)
83
- <= (capacity - weights[j]) * x[j],
84
- name=f"surrogate_{j}"
85
- )
86
-
87
- # Constraint (5): y_{ij} <= x_j
88
- for i in N:
89
- for j in N:
90
- if i != j:
91
- model.addConstr(y[i, j] <= x[j], name=f"link_{i}_{j}")
92
-
93
- # Constraint (6): symmetry y_{ij} = y_{ji} for j > i
94
- for i in N:
95
- for j in N:
96
- if j > i:
97
- model.addConstr(y[i, j] == y[j, i], name=f"sym_{i}_{j}")
98
-
99
- # Solve
100
- model.optimize()
101
-
102
- # Extract solution
103
- if model.SolCount > 0:
104
- objective_value = model.ObjVal
105
- selected_items = [int(x[j].X > 0.5) for j in N]
106
- else:
107
- objective_value = None
108
- selected_items = [0] * n
109
-
110
- # Write solution
111
- solution = {
112
- "objective_value": objective_value,
113
- "selected_items": selected_items,
114
- }
115
-
116
- with open(solution_path, "w") as f:
117
- json.dump(solution, f, indent=2)
118
-
119
-
120
- def main():
121
- parser = argparse.ArgumentParser(
122
- description="Solve QKP using Gurobi (Caprara et al. 1999 linearization)"
123
- )
124
- parser.add_argument(
125
- "--instance_path", type=str, required=True,
126
- help="Path to instance JSON file"
127
- )
128
- parser.add_argument(
129
- "--solution_path", type=str, required=True,
130
- help="Path to write solution JSON file"
131
- )
132
- parser.add_argument(
133
- "--time_limit", type=int, default=300,
134
- help="Gurobi time limit in seconds (default: 300)"
135
- )
136
- parser.add_argument("--log_path", type=str, default=None, help="Path to log incumbent solutions")
137
- args = parser.parse_args()
138
- install_gurobi_logger(args.log_path)
139
- solve_qkp(args.instance_path, args.solution_path, args.time_limit)
140
-
141
-
142
- if __name__ == "__main__":
143
- main()
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
caprara1999/gurobi_feasi_result/large_feasi_result_1.json DELETED
@@ -1,6 +0,0 @@
1
- {
2
- "feasible": true,
3
- "violated_constraints": [],
4
- "violations": [],
5
- "violation_magnitudes": []
6
- }
 
 
 
 
 
 
 
caprara1999/gurobi_feasi_result/large_feasi_result_2.json DELETED
@@ -1,6 +0,0 @@
1
- {
2
- "feasible": true,
3
- "violated_constraints": [],
4
- "violations": [],
5
- "violation_magnitudes": []
6
- }
 
 
 
 
 
 
 
caprara1999/gurobi_feasi_result/large_feasi_result_3.json DELETED
@@ -1,6 +0,0 @@
1
- {
2
- "feasible": true,
3
- "violated_constraints": [],
4
- "violations": [],
5
- "violation_magnitudes": []
6
- }
 
 
 
 
 
 
 
caprara1999/gurobi_feasi_result/large_feasi_result_4.json DELETED
@@ -1,6 +0,0 @@
1
- {
2
- "feasible": true,
3
- "violated_constraints": [],
4
- "violations": [],
5
- "violation_magnitudes": []
6
- }
 
 
 
 
 
 
 
caprara1999/gurobi_feasi_result/large_feasi_result_5.json DELETED
@@ -1,6 +0,0 @@
1
- {
2
- "feasible": true,
3
- "violated_constraints": [],
4
- "violations": [],
5
- "violation_magnitudes": []
6
- }
 
 
 
 
 
 
 
caprara1999/gurobi_feasi_result/tiny_feasi_result.json DELETED
@@ -1,6 +0,0 @@
1
- {
2
- "feasible": true,
3
- "violated_constraints": [],
4
- "violations": [],
5
- "violation_magnitudes": []
6
- }
 
 
 
 
 
 
 
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