{"id":"W2511419272","doi":"10.1109/cicn.2015.237","title":"An Interior Point Method for Large Scale Linear Feasibility Problems","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Optimization Algorithms Research","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Interior point method; Linear programming; Convergence (economics); Mathematical optimization; Point (geometry); Computer science; Set (abstract data type); Scale (ratio); Algorithm; 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":[],"consensus_categories":[],"category_scores_codex":[0.002285292,0.0001332362,0.0002451403,0.0000723878,0.00006981301,0.00004209464,0.0002475,0.00008262025,0.0002891977],"category_scores_gemma":[0.00149444,0.0001074234,0.00006897539,0.0001784543,0.00002671714,0.0003152328,0.0001088055,0.0001302656,0.00003502187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000134475,"about_ca_system_score_gemma":0.00009640239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009094696,"about_ca_topic_score_gemma":0.0001081211,"domain_scores_codex":[0.9985075,0.0001619376,0.0003330637,0.0003518575,0.0002911862,0.000354429],"domain_scores_gemma":[0.9981397,0.0002550921,0.00007488162,0.0005621363,0.0006733827,0.000294856],"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":[0.00524786,0.02755264,0.0111376,0.006820434,0.00087766,0.00004355605,0.09112338,0.05232104,0.02732588,0.487003,0.149834,0.140713],"study_design_scores_gemma":[0.001742432,0.00034753,0.000009472131,0.00001377578,0.000009701462,0.000005748911,0.001372731,0.8632616,0.004281181,0.1251248,0.00364894,0.000182172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001396996,0.000009195421,0.994791,0.0004047574,0.0000711221,0.001352925,0.00004276374,0.0002284087,0.001702832],"genre_scores_gemma":[0.002475667,0.000001495755,0.9927006,0.0001158015,0.00008204885,0.0001426715,0.00002392851,0.00004473794,0.004413082],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8109405,"threshold_uncertainty_score":0.4380598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1666626111468491,"score_gpt":0.4805207972896432,"score_spread":0.3138581861427941,"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."}}