{"id":"W2007277085","doi":"10.1007/s10107-007-0124-6","title":"Computing proximal points of nonconvex functions","year":2007,"lang":"en","type":"article","venue":"Mathematical Programming","topic":"Optimization and Variational Analysis","field":"Computer Science","cited_by":108,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Mathematics; Subgradient method; Proximal Gradient Methods; Bounded function; Lipschitz continuity; Mathematical optimization; Regular polygon; Function (biology); Convex optimization; Applied mathematics; Mathematical analysis; 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":[],"consensus_categories":[],"category_scores_codex":[0.0008525369,0.00008059157,0.0001603741,0.0001166377,0.000098391,0.00006831909,0.0002610329,0.000040254,0.00006631128],"category_scores_gemma":[0.0001731379,0.00006856425,0.00009043305,0.0006013745,0.00004041569,0.000170992,0.0001190108,0.00007718227,0.00007221723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001996789,"about_ca_system_score_gemma":0.00002346296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002686266,"about_ca_topic_score_gemma":0.000001533628,"domain_scores_codex":[0.9989009,0.00002220244,0.0003986571,0.0001780433,0.0002892362,0.0002108952],"domain_scores_gemma":[0.9992036,0.0002237687,0.0001323439,0.0002105462,0.0001460482,0.00008368861],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004280042,0.0004334885,0.0008444047,0.00009495413,0.00006885516,0.000004734731,0.0009732347,0.0002450717,0.0001307775,0.8349398,0.00005777448,0.1622027],"study_design_scores_gemma":[0.0004262675,0.0001008551,0.001755101,0.00006881882,0.0000494601,0.00002461753,0.0002352318,0.9655173,0.0009854437,0.02913101,0.00144892,0.0002569116],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002055959,0.000008896502,0.9910071,0.0004391444,0.0000486428,0.0001253868,2.828944e-7,0.0001185669,0.006196046],"genre_scores_gemma":[0.4776343,9.451436e-8,0.5221674,0.00004836121,0.00002500927,0.000001730083,0.000001640058,0.000003613459,0.0001178958],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9652723,"threshold_uncertainty_score":0.2795969,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01754484837312971,"score_gpt":0.2718162145073227,"score_spread":0.254271366134193,"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."}}