{"id":"W4243792189","doi":"10.23952/jano.3.2021.1.04","title":"Non-Euclidean proximal methods for convex-concave saddle-point problems","year":2021,"lang":"en","type":"article","venue":"Journal of Applied and Numerical Optimization","topic":"Advanced Optimization Algorithms Research","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Israel Science Foundation; Deutsche Forschungsgemeinschaft","keywords":"Saddle point; Regular polygon; Saddle; Euclidean geometry; Mathematics; Euclidean distance; Point (geometry); Combinatorics; Geometry; Computer science; Mathematical optimization","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008567446,0.0001747892,0.0005230379,0.0001245908,0.0001304325,0.000087339,0.0001219372,0.0001207875,0.0001032955],"category_scores_gemma":[0.0006880767,0.0001481947,0.0001161242,0.0003321361,0.00006697022,0.0002205113,0.00007052514,0.0002778016,0.000001151398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007730417,"about_ca_system_score_gemma":0.0001583507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":2.561819e-7,"about_ca_topic_score_gemma":6.506672e-8,"domain_scores_codex":[0.9983891,0.00006990439,0.0007525969,0.0002361966,0.0002890133,0.0002632526],"domain_scores_gemma":[0.9975605,0.0006723908,0.0005524903,0.000152805,0.0008601584,0.0002016739],"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":[0.0008190446,0.0007869124,0.0000363531,0.0005793475,0.0003425565,0.00002444841,0.001740362,0.8473705,0.007079049,0.02956619,0.001071057,0.1105842],"study_design_scores_gemma":[0.002637855,0.0003220992,0.00001097551,0.00006338899,0.00009627751,0.0001041363,0.0004440872,0.9270068,0.01181945,0.05514172,0.002095047,0.0002582019],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000168477,0.0001134025,0.9968184,0.000551962,0.0001061566,0.0005215782,0.000003972717,0.00002150501,0.001694541],"genre_scores_gemma":[0.005997419,0.0001871306,0.9931979,0.0001654549,0.0001568805,0.00003761688,0.00001324568,0.00004734369,0.00019705],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.110326,"threshold_uncertainty_score":0.6043206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04057491881040825,"score_gpt":0.3809193250032356,"score_spread":0.3403444061928274,"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."}}