{"id":"W2119883478","doi":"10.1002/cpa.20303","title":"Iteratively reweighted least squares minimization for sparse recovery","year":2009,"lang":"en","type":"article","venue":"Communications on Pure and Applied Mathematics","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":1319,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Army Research Office; Office of Naval Research; European Commission; Goddard Space Flight Center; York University; Princeton University; Deutscher Akademischer Austauschdienst; National Science Foundation","keywords":"Mathematics; Restricted isometry property; Combinatorics; Hyperplane; Iteratively reweighted least squares; Limit point; Norm (philosophy); Sequence (biology); Limit (mathematics); Matrix (chemical analysis); Weight; Algorithm; Element (criminal law); Compressed sensing; Discrete mathematics; Applied mathematics; Non-linear least squares; Mathematical analysis; Pure mathematics; Lie algebra; Estimation theory","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":[0.001308255,0.0009982398,0.001052987,0.0005993675,0.0003114322,0.0006255326,0.001301202,0.00143946,0.001567242],"category_scores_gemma":[0.005388704,0.0005306719,0.0006215166,0.0006533359,0.001096776,0.001344781,0.001144281,0.001937451,0.0006463893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007033588,"about_ca_system_score_gemma":0.001201224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003256493,"about_ca_topic_score_gemma":0.00225887,"domain_scores_codex":[0.9993293,0.0002588789,0.00002853003,0.0001570009,0.0001792499,0.00004702366],"domain_scores_gemma":[0.9985252,0.000857671,0.0002012355,0.0001235116,0.0002443916,0.00004797102],"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":[0.0001262347,0.00006867717,0.0005775777,0.0001734494,0.00007852962,0.0001422742,0.0001616967,0.858668,0.01724416,0.0351741,0.002243979,0.08534122],"study_design_scores_gemma":[0.000003305592,0.00001326652,0.00003660508,0.000006085942,0.000002115982,0.00001260964,0.000004470078,0.9954699,0.00112656,0.003015846,0.0003054276,0.00000379876],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005540718,0.0001418785,0.9935274,0.0001202858,0.00001434972,0.0000184378,0.00001880394,0.0001044325,0.0005136627],"genre_scores_gemma":[0.1924685,0.0003077888,0.8019136,0.0001576898,0.00007450572,0.0001630533,0.0001872173,0.0001819444,0.004545778],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003256493,"threshold_uncertainty_score":0.006918728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03427797395926509,"score_gpt":0.2604920053561653,"score_spread":0.2262140313969002,"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."}}