{"id":"W4389986743","doi":"10.1137/1.9781611977806.ch29","title":"Chapter 29: The Proximal Gradient Method","year":2023,"lang":"en","type":"book-chapter","venue":"Society for Industrial and Applied Mathematics eBooks","topic":"Advanced Optimization Algorithms Research","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Computer science","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001499363,0.0006389486,0.0008794214,0.00007963575,0.0006047262,0.0001437562,0.0004522601,0.0009051291,0.00007502127],"category_scores_gemma":[0.0001742467,0.0004542801,0.0007143057,0.00004186242,0.0004140474,0.00002903609,0.0004169403,0.001189925,0.00002903874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009381428,"about_ca_system_score_gemma":0.0001240878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.128031e-7,"about_ca_topic_score_gemma":0.000002355373,"domain_scores_codex":[0.9971964,0.00001035943,0.0008496174,0.000630348,0.0007413171,0.0005719738],"domain_scores_gemma":[0.9964151,0.001947889,0.0006076397,0.0006625411,0.0001876769,0.0001791556],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002688084,0.00002306586,1.107945e-8,0.000354707,0.0004242273,9.573529e-7,0.001664715,0.00001286811,0.00003686915,0.9739975,0.005619025,0.01783918],"study_design_scores_gemma":[0.001331814,0.00008084135,1.005051e-8,0.0002164706,0.0003648321,0.0000100852,0.000719731,0.003011031,0.0004673065,0.9255865,0.0676717,0.0005396734],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.00002197419,0.00006581932,0.2469727,0.0007209614,0.000527399,0.01151528,0.0006286587,0.0006923279,0.7388549],"genre_scores_gemma":[0.000009280686,0.00006522284,0.4264219,0.0001049888,0.0009045192,0.0007351678,0.00004738022,0.0003441202,0.5713674],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.1794492,"threshold_uncertainty_score":0.9997909,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1828859315271553,"score_gpt":0.3525431464180329,"score_spread":0.1696572148908775,"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."}}