{"id":"W2615441104","doi":"","title":"Modeling an Augmented Lagrangian for Improved Blackbox Constrained Optimization","year":2014,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Booth University College","funders":"","keywords":"Augmented Lagrangian method; Mathematical optimization; Computer science; Heuristics; Benchmark (surveying); Bottleneck; Context (archaeology); Lagrangian relaxation; Optimization problem; Sensitivity (control systems); Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001988509,0.0009377412,0.001212192,0.000697677,0.0003872822,0.001437117,0.001191027,0.00148629,0.003986501],"category_scores_gemma":[0.004286799,0.0005801893,0.0009383198,0.0005626882,0.001305391,0.001662703,0.001747784,0.0015177,0.000661415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008378161,"about_ca_system_score_gemma":0.001388892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001991954,"about_ca_topic_score_gemma":0.002145362,"domain_scores_codex":[0.9994193,0.0002796172,0.00002126032,0.00008622205,0.0001380633,0.00005559826],"domain_scores_gemma":[0.9984666,0.0009892627,0.0001745039,0.0001324336,0.0001726924,0.00006453731],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001887941,0.0000183069,0.0001355549,0.00003908556,0.0000114137,0.00002717469,0.00002188384,0.9626239,0.0005445207,0.0295195,0.0004798725,0.006559827],"study_design_scores_gemma":[0.000002330921,0.000006218166,0.000009696381,0.00000401359,0.000001284527,0.000002453364,0.000001535631,0.9947976,0.00009383086,0.004797258,0.0002821025,0.0000016959],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004038625,0.00009302343,0.993347,0.0001448754,0.00002283739,0.00001918322,0.00002906248,0.0001419479,0.002163426],"genre_scores_gemma":[0.458475,0.0003622101,0.5323027,0.0003281138,0.00008039615,0.000374384,0.000225155,0.0003381355,0.007513912],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003986501,"threshold_uncertainty_score":0.01333618,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01061383224355807,"score_gpt":0.2432445580746803,"score_spread":0.2326307258311223,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}