{"id":"W2037284017","doi":"10.1287/moor.1040.0116","title":"An Interior Point Cutting Plane Method for the Convex Feasibility Problem with Second-Order Cone Inequalities","year":2005,"lang":"en","type":"article","venue":"Mathematics of Operations Research","topic":"Advanced Optimization Algorithms Research","field":"Mathematics","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Université de Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Cutting-plane method; Mathematics; Interior point method; Ball (mathematics); Regular polygon; Dual cone and polar cone; Cone (formal languages); Convex set; Second-order cone programming; Center (category theory); Plane (geometry); Convex optimization; Feasible region; Point (geometry); Mathematical optimization; Mathematical analysis; Algorithm; Geometry; Integer programming","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.007355988,0.0002287697,0.0004884144,0.000290605,0.0006910523,0.00027348,0.0007117683,0.0001060541,0.0009958373],"category_scores_gemma":[0.0034019,0.000149704,0.00006720442,0.0006338838,0.0004573111,0.0005481661,0.0001888981,0.0005326982,0.00001552439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001673084,"about_ca_system_score_gemma":0.0004003188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003768853,"about_ca_topic_score_gemma":0.0007846544,"domain_scores_codex":[0.996518,0.0005382583,0.0009212875,0.0004056972,0.001043641,0.0005731612],"domain_scores_gemma":[0.9900922,0.00537776,0.0001304768,0.001097036,0.003158314,0.0001442822],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005783581,0.003792898,0.0001087256,0.005233023,0.0007032868,0.000006112183,0.06534862,0.1024782,0.02607798,0.779214,0.003759755,0.01269908],"study_design_scores_gemma":[0.001387188,0.0006148192,0.000007659731,0.0001648384,0.00003077655,0.00003225966,0.01580459,0.9253024,0.027,0.02881105,0.0005887411,0.0002556906],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02134429,0.00006279149,0.9714,0.00199723,0.0000168523,0.003760506,0.0001605845,0.00007550592,0.001182209],"genre_scores_gemma":[0.05426366,0.00001732956,0.9409959,0.00004275332,0.00009156088,0.0008861171,0.00003914436,0.0000744939,0.00358902],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8228242,"threshold_uncertainty_score":0.9999174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1986081145333806,"score_gpt":0.4984818102359596,"score_spread":0.299873695702579,"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."}}