{"id":"W4401134013","doi":"10.1007/s10898-024-01422-z","title":"Robust bilevel optimization for near-optimal lower-level solutions","year":2024,"lang":"en","type":"article","venue":"Journal of Global Optimization","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Bilevel optimization; Mathematical optimization; Mathematics; Robustness (evolution); Robust optimization; Heuristic; Optimization problem; Constraint (computer-aided design); Regular polygon; Upper and lower bounds; Convex optimization; Duality (order theory)","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":[0.004599803,0.001272417,0.002133885,0.001038847,0.0005613674,0.00397441,0.001194237,0.001482972,0.004694964],"category_scores_gemma":[0.01424618,0.0007923372,0.001356972,0.0009915167,0.00211044,0.002702448,0.00334199,0.002989159,0.0006926691],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001293924,"about_ca_system_score_gemma":0.001425758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001068254,"about_ca_topic_score_gemma":0.0006411512,"domain_scores_codex":[0.9979113,0.0009127968,0.0001137932,0.0003375217,0.0004653395,0.0002591917],"domain_scores_gemma":[0.995311,0.003106655,0.0005020496,0.0003731076,0.0005361186,0.0001712604],"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":[0.00006575061,0.00004868388,0.0002672068,0.0001304365,0.00004881839,0.00007267788,0.00007495583,0.8459294,0.002363269,0.136159,0.0006386073,0.01420114],"study_design_scores_gemma":[0.000006838603,0.00002891349,0.00003588137,0.00002016178,0.000005633523,0.00001463575,0.00001466888,0.9518801,0.0006786036,0.04680849,0.0004992034,0.000006849216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007601291,0.0001441049,0.9894478,0.0001340941,0.00001515131,0.00002157037,0.0000229094,0.00005646242,0.00255657],"genre_scores_gemma":[0.6726411,0.0005158982,0.3213764,0.0002312362,0.00006171641,0.0002796542,0.0001598677,0.000235501,0.004498606],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004694964,"threshold_uncertainty_score":0.02432638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1566208520963348,"score_gpt":0.3719881802762845,"score_spread":0.2153673281799497,"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."}}