{"id":"W2949973360","doi":"10.48550/arxiv.1107.5290","title":"A numerical method for variational problems with convexity constraints","year":2011,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Optimization Algorithms Research","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Convexity; Variational inequality; Mathematics; Constraint (computer-aided design); Convex analysis; Regular polygon; Mathematical optimization; Convex optimization; Conic optimization; Variational analysis; Applied mathematics; Proper convex function; Optimization problem; Cone (formal languages); Convex cone; Algorithm; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001538622,0.0007076369,0.0008405177,0.0009000779,0.0008528047,0.001288516,0.00166045,0.001709328,0.004865067],"category_scores_gemma":[0.005214478,0.0004946796,0.0008301249,0.0009232981,0.001649295,0.001387553,0.002364192,0.002229605,0.001366361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008928257,"about_ca_system_score_gemma":0.001631856,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003423524,"about_ca_topic_score_gemma":0.00356564,"domain_scores_codex":[0.9993461,0.0001908907,0.00002966782,0.00007316951,0.0003233739,0.00003692365],"domain_scores_gemma":[0.9988405,0.0006052021,0.00008460507,0.0001391413,0.0002441312,0.00008645833],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00008915985,0.00007948598,0.0006040297,0.0002886804,0.00006233313,0.0001589338,0.0001801989,0.5506617,0.0109624,0.3309979,0.004615994,0.1012992],"study_design_scores_gemma":[0.00001834684,0.00001632929,0.00003971479,0.00002685155,0.000005440021,0.00003158956,0.00001077194,0.960373,0.0007375463,0.03120372,0.007528031,0.00000862399],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001196482,0.0001948871,0.995372,0.00018123,0.0001191831,0.0000356238,0.00002937628,0.0001057026,0.002765446],"genre_scores_gemma":[0.0603753,0.0004106605,0.9311591,0.0002241091,0.0001294527,0.0003249912,0.000113127,0.0002778162,0.006985461],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004865067,"threshold_uncertainty_score":0.01627529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2214860799207179,"score_gpt":0.2897211908661678,"score_spread":0.06823511094544987,"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."}}