{"id":"W2546659876","doi":"10.1007/s11228-016-0394-3","title":"Computing Proximal Points of Convex Functions with Inexact Subgradients","year":2016,"lang":"en","type":"article","venue":"Set-Valued and Variational Analysis","topic":"Advanced Optimization Algorithms Research","field":"Mathematics","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Subgradient method; Mathematics; Bounded function; Mathematical optimization; Function (biology); Subderivative; Saddle point; Point (geometry); Regular polygon; Convex function; Oracle; Minification; Convex optimization; Applied mathematics; Computer science; Mathematical analysis; Geometry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003758823,0.001896538,0.002669872,0.002016235,0.0007022276,0.002419285,0.002343541,0.002641373,0.003486595],"category_scores_gemma":[0.01860872,0.001736313,0.001167945,0.001408882,0.002199332,0.003052131,0.003904838,0.003579068,0.001009833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001333852,"about_ca_system_score_gemma":0.00205463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002641583,"about_ca_topic_score_gemma":0.003286966,"domain_scores_codex":[0.9985316,0.0006091139,0.00007601775,0.0001958235,0.0004783995,0.0001090443],"domain_scores_gemma":[0.9950133,0.003337998,0.0003189154,0.0003637445,0.0006489111,0.0003170186],"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":[0.0003396101,0.0001615096,0.0008032107,0.0003462037,0.000110676,0.0001655915,0.0002214315,0.8285965,0.002948166,0.07152344,0.002392871,0.09239078],"study_design_scores_gemma":[0.00001804852,0.00005257253,0.00009762942,0.00002335098,0.00001188905,0.00002656632,0.00002492174,0.9653906,0.001281223,0.03249444,0.0005698162,0.000009013435],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01231684,0.0001559835,0.9863583,0.00008776147,0.00003386915,0.00003676981,0.00003313762,0.0001583221,0.0008189504],"genre_scores_gemma":[0.245754,0.0003957923,0.7487752,0.00007200212,0.00009157242,0.0001959746,0.0002505191,0.0004095468,0.004055349],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003758823,"threshold_uncertainty_score":0.01987875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03364518097489857,"score_gpt":0.3203965137361248,"score_spread":0.2867513327612262,"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."}}