{"id":"W4210850497","doi":"10.28924/2291-8639-20-2022-11","title":"Nonconvex Vector Optimization and Optimality Conditions for Proper Efficiency","year":2022,"lang":"en","type":"article","venue":"International Journal of Analysis and Applications","topic":"Optimization and Variational Analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Mathematics; Vector optimization; Generalization; Context (archaeology); Closure (psychology); Algebraic number; Function (biology); Mathematical optimization; Closed set; Set (abstract data type); Vector space; Boundary (topology); Function space; Feasible region; Space (punctuation); Optimization problem; Pure mathematics; Computer science; Mathematical analysis; Multi-swarm optimization","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0004461221,0.00007733254,0.0001776755,0.0005213449,0.0003550861,0.0001833549,0.0004499366,0.00001877242,0.0001312698],"category_scores_gemma":[0.00003364371,0.00007033533,0.0001624442,0.0008237518,0.00003929949,0.0003183247,0.0001494123,0.00008188438,5.67466e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005468926,"about_ca_system_score_gemma":0.00007768277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001154136,"about_ca_topic_score_gemma":0.000002441596,"domain_scores_codex":[0.9988809,0.00005350757,0.0004113461,0.0002003242,0.0003753485,0.00007854951],"domain_scores_gemma":[0.9985151,0.0001252932,0.0004038562,0.0001298103,0.0007495065,0.00007642477],"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":[0.00001449512,0.0002772913,0.001832289,0.000003899436,0.00115918,0.000001272477,0.0002263918,0.8435942,0.0002009137,0.1494314,0.0002107947,0.003047848],"study_design_scores_gemma":[0.0004558461,0.00006848183,0.003416649,0.000001822087,0.0004042432,0.00003296273,0.0001045118,0.9871022,0.00005628505,0.001964158,0.006281736,0.0001111587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001570241,0.0001021541,0.9929518,0.004933517,0.00006703212,0.0001449794,0.00007645607,0.00001243834,0.0001413999],"genre_scores_gemma":[0.9053292,0.0001019106,0.09364241,0.0004260185,0.0001148219,0.0001260563,0.00009148813,0.000005004983,0.0001631527],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9037589,"threshold_uncertainty_score":0.2868191,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008775792801600332,"score_gpt":0.2771069673568705,"score_spread":0.2683311745552701,"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."}}