{"id":"W2098204196","doi":"10.1080/10556788.2014.936438","title":"Penalty decomposition methods for rank minimization","year":2014,"lang":"en","type":"preprint","venue":"Optimization methods & software","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Rank (graph theory); Minification; Mathematical optimization; Penalty method; Mathematics; Low-rank approximation; Matrix (chemical analysis); Sequence (biology); Algorithm; Computer science; Combinatorics; Hankel matrix","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001825512,0.001256243,0.001033389,0.0006662107,0.0004107978,0.000954102,0.0009302017,0.001347548,0.003627461],"category_scores_gemma":[0.005190806,0.0004343803,0.0006925387,0.0009073638,0.001207341,0.001470716,0.001446499,0.002374095,0.001509143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004341609,"about_ca_system_score_gemma":0.0007585302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007833406,"about_ca_topic_score_gemma":0.0008820473,"domain_scores_codex":[0.9989108,0.0004896824,0.00004653657,0.000141317,0.0003560053,0.00005559022],"domain_scores_gemma":[0.9982713,0.000956916,0.0001632303,0.0002246768,0.0003082628,0.00007551691],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001395513,0.0001047348,0.0005433738,0.0007992348,0.0001234249,0.0002073068,0.0001646105,0.4452574,0.01894074,0.3222463,0.009882045,0.2015912],"study_design_scores_gemma":[0.00001221394,0.00004340073,0.00007947522,0.00003139158,0.000009861224,0.00009788249,0.00001387329,0.9468371,0.002829217,0.0421957,0.007835754,0.00001419599],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0008896248,0.0002980029,0.9976672,0.00009784562,0.00003121809,0.00001562027,0.00002002028,0.00004792577,0.0009325576],"genre_scores_gemma":[0.07105533,0.001606842,0.9189174,0.0002124562,0.0002421837,0.000212107,0.0002586336,0.0002534271,0.007241612],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003627461,"threshold_uncertainty_score":0.01213503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03348095355379413,"score_gpt":0.3955012384163833,"score_spread":0.3620202848625892,"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."}}