{"id":"W1986322690","doi":"10.1109/acssc.2014.7094558","title":"On the convergence of an alternating direction penalty method for nonconvex problems","year":2014,"lang":"en","type":"article","venue":"2014 48th Asilomar Conference on Signals, Systems and Computers","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Penalty method; Convergence (economics); Mathematical optimization; Scalability; Quadratic equation; Function (biology); Computer science; Augmented Lagrangian method; Point (geometry); Optimization problem; Constrained optimization; Quadratic programming; Mathematics; Algorithm; Applied mathematics","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":[0.0006563982,0.0002185497,0.0003415461,0.00008678608,0.000110611,0.0001034379,0.0002356488,0.00007709352,0.000007977337],"category_scores_gemma":[0.00002311525,0.0001610919,0.00006298806,0.00006253806,0.00004283023,0.00008384792,0.00002626514,0.0001307756,0.000004618803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001846261,"about_ca_system_score_gemma":0.00001352361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002182427,"about_ca_topic_score_gemma":0.00000835577,"domain_scores_codex":[0.9987613,0.0002061731,0.0003287643,0.000289961,0.0001997613,0.0002140263],"domain_scores_gemma":[0.9988167,0.0004623737,0.0001740124,0.0003242613,0.0001432589,0.00007938161],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002251681,0.0002377155,0.0004146304,0.001126888,0.0005597275,0.000006538108,0.001784359,0.2808703,0.1515266,0.3316648,0.02126593,0.2103173],"study_design_scores_gemma":[0.000233952,0.0005005192,0.0001826045,0.0005305637,0.00001494378,0.000008620475,0.00006872272,0.9808944,0.01413735,0.002177254,0.001045015,0.0002060245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1278501,0.0001127763,0.8679575,0.00008314037,0.0008046525,0.0006892745,0.00001179626,0.0002795917,0.002211209],"genre_scores_gemma":[0.9948081,0.0000326965,0.004806354,0.00009557284,0.0001327006,0.00006672455,0.000005512351,0.00002711382,0.00002521879],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.866958,"threshold_uncertainty_score":0.6569138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03133406381037876,"score_gpt":0.2633691879002347,"score_spread":0.232035124089856,"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."}}