{"id":"W2102138843","doi":"","title":"Practical Large-Scale Optimization for Max-norm Regularization","year":2010,"lang":"en","type":"article","venue":"CaltechAUTHORS (California Institute of Technology)","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":131,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Air Force Office of Scientific Research; Office of Naval Research; Natural Sciences and Engineering Research Council of Canada; Defense Advanced Research Projects Agency","keywords":"Norm (philosophy); Computer science; Mathematical optimization; Regular polygon; Cluster analysis; Matrix decomposition; Scalability; Algorithm; Mathematics; Theoretical computer science; Artificial intelligence; Eigenvalues and eigenvectors","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003008409,0.001814528,0.001214857,0.0005389649,0.0007832465,0.001512886,0.001679694,0.001852614,0.005837749],"category_scores_gemma":[0.00985307,0.0006598183,0.0007158802,0.001160594,0.002121394,0.002757312,0.002489015,0.00442322,0.002058348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001349633,"about_ca_system_score_gemma":0.001870844,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002150297,"about_ca_topic_score_gemma":0.002932497,"domain_scores_codex":[0.9980803,0.0007846667,0.00006823524,0.000377888,0.0005707592,0.0001182989],"domain_scores_gemma":[0.9955974,0.002885205,0.000251923,0.0006142275,0.0005192469,0.0001319803],"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":[0.0001758277,0.0001287728,0.0003586813,0.0002731513,0.00005015215,0.0001120923,0.000129727,0.6296468,0.005757149,0.2228284,0.01575395,0.1247853],"study_design_scores_gemma":[0.000008393001,0.00002072359,0.00003484836,0.000008330331,0.000002285484,0.00002281533,0.000009383906,0.9575914,0.001021014,0.03922366,0.002051092,0.000006013047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0008450956,0.00009418008,0.9973637,0.0001883069,0.0000248092,0.00002001855,0.00002372039,0.0001548134,0.001285277],"genre_scores_gemma":[0.09587672,0.0004470962,0.8973876,0.0002646377,0.0002159094,0.0003099437,0.0002669092,0.000296972,0.004934231],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005837749,"threshold_uncertainty_score":0.01952916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01366517478085955,"score_gpt":0.2614087523939279,"score_spread":0.2477435776130683,"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."}}