{"id":"W1672652176","doi":"10.48550/arxiv.0808.2316","title":"A new secant method for unconstrained optimization","year":2008,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Advanced Optimization Algorithms Research","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Secant method; Computer science; Mathematics; Mathematical optimization; Applied mathematics; Newton's method; Physics; Nonlinear system","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":[0.0005237313,0.0004632658,0.0006981412,0.000280814,0.0002083295,0.00005805745,0.0005638376,0.0005063522,0.0004659483],"category_scores_gemma":[0.002366596,0.0004680634,0.0002880707,0.0003255842,0.00007586171,0.0001342504,0.0005453221,0.000720247,0.0000314006],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000276922,"about_ca_system_score_gemma":0.001067957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000518892,"about_ca_topic_score_gemma":0.0000154934,"domain_scores_codex":[0.9972285,0.0001727666,0.0007303966,0.0008309237,0.0004848546,0.0005525552],"domain_scores_gemma":[0.996682,0.0009835364,0.0004672827,0.0009380561,0.0006398478,0.0002892998],"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":[0.00009782771,0.0001581037,0.0005486905,0.000536604,0.0002871485,0.00002492135,0.0008931648,0.9698244,0.00008291757,0.00225512,0.01299091,0.01230016],"study_design_scores_gemma":[0.002154429,0.00009774597,0.00006802156,0.0002391555,0.000138586,0.00004329714,0.0001452824,0.9532537,0.001786522,0.03693224,0.00424425,0.0008967688],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005466364,0.00009676861,0.9938789,0.0007886703,0.0004553907,0.002038427,0.0001058912,0.0003526028,0.001736683],"genre_scores_gemma":[0.0004117239,0.0003375791,0.9884002,0.0001280644,0.0005158436,0.0003094542,0.0002843334,0.0001683243,0.009444482],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03467713,"threshold_uncertainty_score":0.9997771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1553815099555758,"score_gpt":0.4243592182204488,"score_spread":0.268977708264873,"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."}}