{"id":"W4300677122","doi":"10.48550/arxiv.1805.03756","title":"A Residual Smoothing Strategy for Accelerating Newton Method\\n Continuation","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Optimization Algorithms Research","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Centre for Applied Research in Cancer Control; Sandia National Laboratories; University of Wyoming; National Aeronautics and Space Administration","keywords":"Jacobian matrix and determinant; Smoothing; Residual; Newton's method; Nonlinear system; Convergence (economics); Local convergence; Continuation; Quasi-Newton method; Limit (mathematics); Quadratic equation; Mathematics; Applied mathematics; Mathematical optimization; Computer science; Iterative method; Algorithm; Mathematical analysis; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001163756,0.000365671,0.0004932015,0.0003275667,0.0003466691,0.0001843153,0.0006565491,0.0004735967,0.0001902698],"category_scores_gemma":[0.001374412,0.0004362194,0.0001908489,0.0003791187,0.0001105425,0.0003732854,0.0006983197,0.0006777684,0.00001942479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003515853,"about_ca_system_score_gemma":0.0003379734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005755678,"about_ca_topic_score_gemma":0.00007360524,"domain_scores_codex":[0.9975533,0.0003366091,0.0004144131,0.001023852,0.0001793169,0.0004924543],"domain_scores_gemma":[0.9963325,0.001239461,0.0005656836,0.0007538214,0.0009552332,0.0001532775],"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":[0.0002525185,0.0001488471,0.0001325295,0.000578262,0.0002636435,0.00004853867,0.0007606087,0.7603012,0.0001942933,0.2297207,0.003821676,0.003777284],"study_design_scores_gemma":[0.0007803777,0.00007854065,0.0000184615,0.000119974,0.0000985299,0.00000159117,0.0003923109,0.6812494,0.000810525,0.3158009,0.0002912947,0.0003581698],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02473674,0.00001670283,0.9706094,0.0001000569,0.0001960348,0.001306648,0.00006575653,0.0002560545,0.002712585],"genre_scores_gemma":[0.1856511,0.00006949026,0.7956377,0.00005977136,0.0007143524,0.00002033643,0.0002003876,0.000161461,0.01748542],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1749717,"threshold_uncertainty_score":0.999809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3877810444801083,"score_gpt":0.3529854665986066,"score_spread":0.03479557788150167,"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."}}