{"id":"W3207184872","doi":"10.1007/978-1-0716-0843-2_14","title":"Algorithms for General Convex Problems","year":2021,"lang":"en","type":"book-chapter","venue":"Texts in computer science","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Proper convex function; Variety (cybernetics); Regular polygon; Convex analysis; Mathematical optimization; Convex optimization; Computer science; Mathematics; Conic optimization; Convex function; Subderivative; Algorithm; Artificial intelligence","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":[0.0001938477,0.0002562522,0.0003185337,0.000246211,0.00006518467,0.0001352722,0.0005564655,0.0001477148,0.00002205086],"category_scores_gemma":[0.000003827291,0.0002680989,0.0000772241,0.00009846186,0.0002150913,0.0001148181,0.0002088182,0.0002405025,0.00001073781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001100463,"about_ca_system_score_gemma":0.0000906659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000455235,"about_ca_topic_score_gemma":0.000006483161,"domain_scores_codex":[0.998653,0.000003732667,0.000257221,0.0004822353,0.0002687375,0.0003350613],"domain_scores_gemma":[0.9992487,0.00004058927,0.00004699161,0.0004629038,0.0001331227,0.00006766116],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003036813,0.00002906831,0.000006893505,0.0001805564,0.00005227738,0.0001524417,0.0003661346,0.05836366,0.002704955,0.1047984,0.01590416,0.8174384],"study_design_scores_gemma":[0.0001580953,0.00005599664,0.00002642472,0.0005728633,0.000007721062,0.00003470472,3.278882e-7,0.8878766,0.003992924,0.03091829,0.07584491,0.0005111198],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003230027,0.001420229,0.9402236,0.00004853319,0.002287968,0.0006044278,0.00001529591,0.0007900876,0.05428689],"genre_scores_gemma":[0.0142767,0.0004556546,0.9301922,0.0005855628,0.001937687,0.00007316252,0.00003962629,0.0002039683,0.05223542],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.829513,"threshold_uncertainty_score":0.9999771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03624600821493167,"score_gpt":0.2567250139113038,"score_spread":0.2204790056963721,"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."}}