{"id":"W4245609702","doi":"10.23952/asvao.3.2021.3.02","title":"A Dai-Liao-like projection method for solving convex constrained nonlinear monotone equations and minimizing the $\\ell_1$-regularized problem","year":2021,"lang":"en","type":"article","venue":"Applied Set-Valued Analysis and Optimization","topic":"Advanced Optimization Algorithms Research","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Petroleum Technology Development Fund; Deutscher Akademischer Austauschdienst","keywords":"Monotone polygon; Regular polygon; Mathematics; Projection (relational algebra); Nonlinear system; Applied mathematics; Projection method; Convex optimization; Convex analysis; Mathematical optimization; Mathematical analysis; Algorithm; Dykstra's projection algorithm; Physics; Geometry","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":[0.001299898,0.0002904733,0.0006189573,0.000360766,0.0008840951,0.0003033113,0.0001246272,0.0001796748,0.00008034355],"category_scores_gemma":[0.0006535947,0.0002438162,0.0001603095,0.001872845,0.000150721,0.0002070033,0.0001228231,0.0002133807,8.163346e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006503183,"about_ca_system_score_gemma":0.000153131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001953955,"about_ca_topic_score_gemma":0.00005444783,"domain_scores_codex":[0.9976345,0.0002050721,0.0006954254,0.0006928855,0.0004061378,0.0003659322],"domain_scores_gemma":[0.9970846,0.001313987,0.0003920207,0.0004065739,0.000682095,0.0001207709],"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":[0.0001277684,0.000216282,0.0000346217,0.0002264743,0.002028089,0.000002532124,0.002660923,0.9150108,0.00587547,0.06102416,0.00007652624,0.01271635],"study_design_scores_gemma":[0.001588712,0.00002926561,0.000006052442,0.0000185966,0.001504111,0.000005824271,0.002219933,0.9847824,0.001759878,0.007715581,0.0000971234,0.0002724994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000309498,0.00009364896,0.9969712,0.0005746853,0.00002900222,0.001557277,0.00004911784,0.0001034516,0.0003120758],"genre_scores_gemma":[0.00589624,0.0002055915,0.9919037,0.0001557949,0.00006631383,0.0004739528,0.0006934077,0.00005501024,0.0005499531],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.06977163,"threshold_uncertainty_score":0.9942535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04316706657146967,"score_gpt":0.3540700728513287,"score_spread":0.310903006279859,"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."}}