Buckets:
| { | |
| "claims": { | |
| "claim_1_algorithm2_static_anytime": { | |
| "evidence": "{\"ccv_violations\": 0, \"max_ccv_ratio\": 0.0003455980784292409, \"max_regret_ratio\": 0.12500000000000003, \"regret_violations\": 0, \"runs\": 12, \"timesteps\": 36000}", | |
| "verdict": "verified on deterministic adversarial linear instances" | |
| }, | |
| "claim_2_time_varying_lyapunov": { | |
| "evidence": "{\"checked_timesteps\": 72864, \"lambda_schedule\": \"lambda_t = [4 sqrt(t) sqrt(log t + 1)(log(log t + 1)+1)]^-1, with sqrt(1+P_t) factor for dynamic\", \"max_phi_drop\": 2.220446049250313e-16, \"max_queue_identity_error\": 2.220446049250313e-16}", | |
| "verdict": "verified as the active certificate mechanism" | |
| }, | |
| "claim_3_algorithm3_dynamic_path_length": { | |
| "evidence": "{\"ccv_violations\": 0, \"max_ccv_ratio\": 0.00022566089078199686, \"max_regret_ratio\": 0.125, \"path_max\": 345.66263168177215, \"path_min\": 20.607211739138727, \"regret_violations\": 0, \"runs\": 9, \"timesteps\": 36864}", | |
| "verdict": "verified on deterministic adversarial linear instances with unknown running path" | |
| }, | |
| "claim_4_algorithm4_optimistic_prediction_error": { | |
| "evidence": "{\"ccv\": [1.0532111494750591, 1.113738351871047, 1.514804744353717, 2.2191744573811336, 4.149731183927458, 7.927859182897209], \"noise_grid\": [0.0, 0.02, 0.05, 0.1, 0.2, 0.4], \"regret\": [168.02170750372014, 168.0249656032684, 168.03001262960242, 168.181358590713, 169.23065715623474, 172.08709979803857], \"sqrt_E_f\": [0.0, 15.397895845129963, 24.302265683684016, 34.35728306987152, 48.52207412643605, 68.87414228714518], \"sqrt_E_g\": [0.30987406485845387, 15.37845515001666, 24.345919668961763, 34.35570841497768, 48.67969956350066, 68.85716876632047]}", | |
| "verdict": "partially verified: theorem statement checked in paper and qualitative prediction-error scaling reproduced in a proxy optimistic OCO/COCO simulation" | |
| } | |
| }, | |
| "configuration": { | |
| "dim": 16, | |
| "dynamic_horizon": 4096, | |
| "horizon": 3000, | |
| "outdir": "outputs/local_verification" | |
| }, | |
| "outputs": { | |
| "all_time_certificates_csv": "outputs/local_verification/all_time_certificates.csv", | |
| "optimistic_proxy_csv": "outputs/local_verification/optimistic_proxy.csv", | |
| "run_summaries_csv": "outputs/local_verification/run_summaries.csv" | |
| }, | |
| "paper": { | |
| "arxiv": "2510.22579", | |
| "openreview": "https://openreview.net/forum?id=7FG30Wld9Q", | |
| "title": "Optimal Anytime Algorithms for Online Convex Optimization with Adversarial Constraints" | |
| }, | |
| "summary_sha256": "56c22b25a5958a8e68b246fcad52b38c925fedfa114d279e3e67a7faa4c21cef" | |
| } |
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