{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000804123,0.0003921445,0.0003777035,0.0004378464,0.0004020896,0.0003560607,0.0009622298,0.0002499468,0.000010138],"category_scores_gemma":[0.0004299792,0.000421238,0.0001545509,0.0006615381,0.00007826227,0.00146017,0.0001868588,0.0002360986,0.000003670905],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003360362,"about_ca_system_score_gemma":0.0001793598,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004817498,"about_ca_topic_score_gemma":0.0003056871,"domain_scores_codex":[0.9974467,0.0001766164,0.0005451277,0.0008312962,0.0002459979,0.0007542706],"domain_scores_gemma":[0.9976255,0.000136037,0.0002389352,0.001088595,0.0005235662,0.0003873604],"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":[0.00003919684,0.000144887,0.00003082575,0.00001146826,0.00002214442,0.000001443824,0.0001818952,0.9372113,0.003225401,0.03036345,0.00002878198,0.02873925],"study_design_scores_gemma":[0.001469155,0.000261312,0.00003587128,0.00002062577,0.00001807503,0.00002542967,0.00005243049,0.992153,0.00261117,0.002681652,0.0001898359,0.00048137],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005799507,0.00005457332,0.9942068,0.001698313,0.0001874061,0.001451359,0.00003539567,0.001608842,0.0001773],"genre_scores_gemma":[0.1264793,0.00002499487,0.8709537,0.001586295,0.000116377,0.0005088606,0.0001007853,0.00007082356,0.0001588555],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1258994,"threshold_uncertainty_score":0.9998239,"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."}}