{"id":"W2154146583","doi":"10.1007/s10107-007-0126-4","title":"Regularization using a parameterized trust region subproblem","year":2007,"lang":"en","type":"article","venue":"Mathematical Programming","topic":"Numerical methods in inverse problems","field":"Mathematics","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Regularization (linguistics); Trust region; Parameterized complexity; Mathematics; Conjugate gradient method; Curvature; Mathematical optimization; Applied mathematics; Algorithm; MATLAB; Computer science; Artificial intelligence; Geometry","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":[0.002193226,0.001102931,0.001462682,0.0005849893,0.0004262983,0.001646421,0.001312136,0.002583315,0.002636094],"category_scores_gemma":[0.008401576,0.0008458347,0.001111943,0.0005793668,0.001488515,0.002570802,0.00263172,0.002391348,0.0005696206],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000788261,"about_ca_system_score_gemma":0.001208038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001303702,"about_ca_topic_score_gemma":0.0008810368,"domain_scores_codex":[0.998929,0.0005672004,0.0000449934,0.0001607791,0.0002522167,0.00004582685],"domain_scores_gemma":[0.9972239,0.001733112,0.0002188694,0.0003449836,0.000371211,0.0001079171],"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":[0.0001216449,0.00006491534,0.0003150463,0.0001787382,0.00005819107,0.0001336726,0.0001055477,0.7894711,0.008262306,0.1636559,0.002605557,0.03502725],"study_design_scores_gemma":[0.000005718526,0.0000107662,0.00001523395,0.000003678205,0.000004304024,0.00001558417,0.000003958411,0.9881148,0.0005560435,0.01074169,0.0005247564,0.000003560679],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002050546,0.00003299059,0.9970937,0.00009493921,0.0000141103,0.00001377439,0.00001116907,0.00003868738,0.0006500171],"genre_scores_gemma":[0.2576668,0.0003159655,0.733048,0.0001760423,0.0001122758,0.000307625,0.0001702558,0.0004727529,0.007730318],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002636094,"threshold_uncertainty_score":0.011599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1567767446349228,"score_gpt":0.3865109760349728,"score_spread":0.22973423140005,"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."}}