{"id":"W2152778795","doi":"10.1080/10556788.2012.732074","title":"A new family of high-order directions for unconstrained optimization inspired by Chebyshev and Shamanskii methods","year":2012,"lang":"en","type":"article","venue":"Optimization methods & software","topic":"Iterative Methods for Nonlinear Equations","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Chebyshev filter; Newton's method; Convergence (economics); Newton's method in optimization; Chebyshev iteration; Mathematical optimization; Displacement (psychology); Computer science; Acceleration; Order (exchange); Applied mathematics; Mathematics; Iterative method; Algorithm; Local convergence; Nonlinear system; Mathematical analysis","routes":{"ca_aff":true,"ca_fund":true,"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":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003557493,0.0004157764,0.0007416049,0.0003266188,0.0003468497,0.00007775055,0.0001983491,0.0003102167,0.0003742145],"category_scores_gemma":[0.01136838,0.0004134654,0.0001667213,0.001119992,0.000151051,0.0006456324,0.00009353038,0.0002021418,9.400264e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009662998,"about_ca_system_score_gemma":0.0001823157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006048831,"about_ca_topic_score_gemma":0.000004149562,"domain_scores_codex":[0.996125,0.001711092,0.000957955,0.0004760756,0.0002589348,0.0004708715],"domain_scores_gemma":[0.9930121,0.004758814,0.0006221485,0.0004910613,0.0008424345,0.0002734577],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001597826,0.0008163994,0.0007414838,0.0005027482,0.0006190724,2.883826e-7,0.005325571,0.2518337,0.008607098,0.02804951,0.003809167,0.6995352],"study_design_scores_gemma":[0.007750436,0.0006158291,0.0003611156,0.0003538766,0.002126215,0.00003459166,0.001212005,0.7869993,0.05658568,0.119376,0.02202765,0.002557351],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001454879,0.0009662751,0.9960501,0.000211147,0.000538313,0.001261138,0.0002330769,0.0003081639,0.0002863185],"genre_scores_gemma":[0.00006794634,0.0001179418,0.9973997,0.0001116749,0.0001869421,0.0002128742,0.0004113948,0.0001364737,0.001355087],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6969778,"threshold_uncertainty_score":0.9998317,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07117554534218135,"score_gpt":0.4212679062819079,"score_spread":0.3500923609397265,"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."}}