{"id":"W2915050298","doi":"10.1007/s10107-021-01663-w","title":"Status determination by interior-point methods for convex optimization problems in domain-driven form","year":2021,"lang":"en","type":"preprint","venue":"Mathematical Programming","topic":"Advanced Optimization Algorithms Research","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Office of Naval Research","keywords":"Conic section; Conic optimization; Duality (order theory); Mathematical optimization; Domain (mathematical analysis); Context (archaeology); Dual (grammatical number); Duality gap; Regular polygon; Interior point method; Computer science; Point (geometry); Convex optimization; Mathematics; Effective domain; Optimization problem; Convex analysis; Discrete mathematics; Geometry","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":[0.002322047,0.0006323424,0.001283917,0.0004037445,0.0001513102,0.0005871049,0.0005333888,0.0006685312,0.0002762281],"category_scores_gemma":[0.004918205,0.0006309666,0.0003296621,0.0004805522,0.0001914064,0.0003800154,0.001026974,0.001019501,0.000004934745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008781331,"about_ca_system_score_gemma":0.000237874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000768951,"about_ca_topic_score_gemma":0.00001516532,"domain_scores_codex":[0.9950096,0.0004647641,0.001752359,0.001045234,0.0006202052,0.001107827],"domain_scores_gemma":[0.9953994,0.002039605,0.0007414007,0.0008227923,0.0007093249,0.0002875094],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001696794,0.003452802,0.000111162,0.03756221,0.0005239071,0.00004521269,0.03124981,0.0946225,0.001044817,0.02146563,0.0003758025,0.8093765],"study_design_scores_gemma":[0.001010097,0.0001099141,5.950633e-7,0.001175399,0.00005886351,0.00001144834,0.001194291,0.7678319,0.0005202911,0.2268644,0.0006668644,0.0005558467],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005950899,0.0002130014,0.9918211,0.0002496644,0.0001815166,0.006238757,0.00003373328,0.0002821507,0.0003850049],"genre_scores_gemma":[0.001106241,0.00008287248,0.9931339,0.00003273384,0.00004888059,0.00453896,0.0005745984,0.0002168347,0.0002649644],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8088206,"threshold_uncertainty_score":0.9996142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07236776562694941,"score_gpt":0.4375472251117842,"score_spread":0.3651794594848348,"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."}}