{"id":"W4245074662","doi":"10.23952/jano.2.2020.2.08","title":"The effect of deterministic noise on a quasi-subgradient method for quasi-convex feasibility problems","year":2020,"lang":"en","type":"article","venue":"Journal of Applied and Numerical Optimization","topic":"Optimization and Variational Analysis","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Department of Education of Guangdong Province; Natural Science Foundation of Guangdong Province; Natural Science Foundation of Chongqing; Chongqing University; Shenzhen University; National Natural Science Foundation of China","keywords":"Subgradient method; Mathematical optimization; Regular polygon; Noise (video); Mathematics; Convex optimization; Applied mathematics; Computer science; Artificial intelligence; 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":[0.0007528643,0.0001193982,0.0003525662,0.00005820605,0.0001439544,0.00008558819,0.0002620996,0.00004579031,0.000004190196],"category_scores_gemma":[0.0002980902,0.00007202628,0.000140139,0.0003505569,0.00003093298,0.000110758,0.00003846074,0.0001029575,7.270648e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000265436,"about_ca_system_score_gemma":0.00004113737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001145242,"about_ca_topic_score_gemma":9.771573e-8,"domain_scores_codex":[0.9987513,0.000122835,0.0005312389,0.0001953283,0.0002822723,0.0001170173],"domain_scores_gemma":[0.9980579,0.0009063235,0.0005849029,0.0001287236,0.0001844308,0.0001376906],"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":[0.0006761312,0.0001409277,0.0001193322,0.00005877097,0.00007716975,5.753523e-7,0.0004781938,0.9605634,0.0001939973,0.0171068,0.00006666577,0.02051806],"study_design_scores_gemma":[0.0009432227,0.002203947,0.0001204359,0.000009975327,0.00006708558,0.000003085797,0.0000113834,0.9949875,0.0004923574,0.0008496872,0.0002335753,0.0000777644],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0005152154,0.00003503753,0.9963451,0.002618566,0.00006459516,0.0003105004,0.000002478346,0.00001166386,0.00009682645],"genre_scores_gemma":[0.6805698,0.00003773346,0.3189271,0.0003667934,0.00006763263,0.00001495626,0.000003075174,0.000007798699,0.000005128219],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6800545,"threshold_uncertainty_score":0.2937147,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01580478980916792,"score_gpt":0.2717489569966035,"score_spread":0.2559441671874356,"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."}}