{"id":"W2092554297","doi":"10.1137/060675320","title":"Exact Regularization of Convex Programs","year":2007,"lang":"en","type":"article","venue":"SIAM Journal on Optimization","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":105,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Regularization (linguistics); Mathematics; Regularization perspectives on support vector machines; Regular polygon; Proximal gradient methods for learning; Lagrange multiplier; Applied mathematics; Exact solutions in general relativity; Degenerate energy levels; Convex optimization; Backus–Gilbert method; Semidefinite programming; Mathematical optimization; Mathematical analysis; Subderivative; Tikhonov regularization; Inverse problem; Computer science; Geometry; Physics","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":[0.0002179773,0.00008249065,0.0001071161,0.0001488743,0.00004037283,0.00002999952,0.00006563265,0.0000710387,0.0000285957],"category_scores_gemma":[0.00001968103,0.00007753208,0.00004046722,0.0001815518,0.00001838435,0.0001186221,0.000005409198,0.0001295549,0.000001934084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004140993,"about_ca_system_score_gemma":0.000008470281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":5.298547e-7,"about_ca_topic_score_gemma":2.911968e-7,"domain_scores_codex":[0.9993866,0.00001606165,0.0002478927,0.00005844207,0.000166495,0.0001244997],"domain_scores_gemma":[0.9996029,0.00001725387,0.00009627968,0.0001017343,0.0001348901,0.00004695342],"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":[0.00002308282,0.00003253029,0.0001493882,0.000007696621,0.00002112748,0.000008444511,0.00007060546,0.974991,0.003180542,0.0007542276,0.0003424071,0.02041892],"study_design_scores_gemma":[0.0005184269,0.0003543324,0.0006726972,0.0003399606,0.00002877171,0.0001099702,0.00006602918,0.8152846,0.1791931,0.001202585,0.001969301,0.0002603038],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01723918,0.0000913164,0.9747015,0.00002243684,0.0002558441,0.00009953331,3.415959e-7,0.0002133809,0.007376505],"genre_scores_gemma":[0.9547403,0.0001577445,0.0448682,0.00002616675,0.0001173725,7.047186e-7,0.00001155815,0.00002486918,0.00005301491],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9375012,"threshold_uncertainty_score":0.3161666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01231304077871429,"score_gpt":0.2311079300959838,"score_spread":0.2187948893172695,"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."}}