{"id":"W2020538086","doi":"10.1109/tip.2012.2208979","title":"Efficient Algorithm for Nonconvex Minimization and Its Application to PM Regularization","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Regularization (linguistics); Algorithm; Mathematics; Computation; Convex function; Minification; Mathematical optimization; Rate of convergence; Convergence (economics); Iterative method; Regular polygon; Computer science; Channel (broadcasting); 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":[],"consensus_categories":[],"category_scores_codex":[0.00007267693,0.0001210674,0.00009770588,0.0001373592,0.0001617823,0.00005044651,0.00004454073,0.0000658177,0.000002932598],"category_scores_gemma":[0.000003148154,0.0001326771,0.00002455756,0.0002103516,0.00001267744,0.0001692987,7.295519e-7,0.00006816168,0.000006968581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004677788,"about_ca_system_score_gemma":0.000008526945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.940775e-7,"about_ca_topic_score_gemma":4.1731e-7,"domain_scores_codex":[0.9994172,0.00000902869,0.0001448015,0.0001491699,0.0000925853,0.0001871539],"domain_scores_gemma":[0.9996679,0.00002120751,0.00002861698,0.00009610891,0.0001098174,0.00007628107],"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":[0.000009661045,0.00006697762,0.00000102407,0.00007039307,0.00001085987,1.298361e-7,0.000556052,0.102649,0.1456989,0.00001441045,0.00007210476,0.7508505],"study_design_scores_gemma":[0.00009802551,0.00001400049,0.00001180959,0.000040223,0.00001980689,0.00000332523,0.00002242304,0.6291566,0.3703134,0.00002272676,0.0001968843,0.0001007305],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005578327,0.0001819475,0.9929309,0.00004599782,0.0001606534,0.000474188,0.00001078894,0.0004895718,0.0001275997],"genre_scores_gemma":[0.8658587,0.00001085022,0.1337467,0.00005473481,0.00008299726,0.0001620505,0.000005468586,0.00003681608,0.00004160379],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8602804,"threshold_uncertainty_score":0.5410414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01135676314097631,"score_gpt":0.2475961981336607,"score_spread":0.2362394349926844,"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."}}