{"id":"W2429556411","doi":"10.1007/s10589-016-9854-9","title":"Smoothing projected Barzilai–Borwein method for constrained non-Lipschitz optimization","year":2016,"lang":"en","type":"article","venue":"Computational Optimization and Applications","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Simon Fraser University; Shenzhen University; National Natural Science Foundation of China","keywords":"Lipschitz continuity; Mathematics; Smoothing; Stationary point; Mathematical optimization; Convergence (economics); Convex function; Regular polygon; Applied mathematics; Mathematical analysis","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.00009982082,0.00015271,0.0001473598,0.0001404851,0.0001865374,0.00006346151,0.00008786463,0.00008319208,0.00003128377],"category_scores_gemma":[0.00002820423,0.0001343828,0.00004085695,0.0002352958,0.00005386517,0.0001570571,0.00002003141,0.00004846553,0.00000300258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003608363,"about_ca_system_score_gemma":0.00003834187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001595297,"about_ca_topic_score_gemma":4.497983e-7,"domain_scores_codex":[0.9992142,0.0000212091,0.0002689253,0.0002439268,0.000103334,0.0001484599],"domain_scores_gemma":[0.9992017,0.0002687251,0.00006963606,0.0001328695,0.0002574195,0.00006972401],"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":[0.000005334942,0.00001706905,0.000009440113,0.000012228,0.00002225019,1.068186e-7,0.00003710918,0.9770433,0.0008359335,0.007325144,0.0006560616,0.01403598],"study_design_scores_gemma":[0.0005664615,0.00001767527,0.00004104339,0.00003947797,0.00002211177,0.00000746646,0.00001767987,0.9928901,0.001059238,0.003165778,0.001982132,0.0001908309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00009939198,0.00004434299,0.9966714,0.000448984,0.00004404568,0.0008780427,0.00004703845,0.0006338794,0.001132891],"genre_scores_gemma":[0.1061127,0.00005012302,0.8928126,0.0001556934,0.00009465987,0.0004672447,0.0002125784,0.00003773297,0.00005664223],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1060133,"threshold_uncertainty_score":0.5479972,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01128787792532096,"score_gpt":0.2649933677149898,"score_spread":0.2537054897896688,"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."}}