{"id":"W3045794209","doi":"","title":"Convergence analysis of a new coefficient conjugate gradient method under exact line search","year":2020,"lang":"en","type":"article","venue":"International Journal of Advanced Science and Technology","topic":"Advanced Optimization Algorithms Research","field":"Mathematics","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Conjugate gradient method; Convergence (economics); Line search; Nonlinear conjugate gradient method; Conjugate; Computer science; Line (geometry); Applied mathematics; Conjugate residual method; Mathematical optimization; Mathematics; Algorithm; Gradient descent; Mathematical analysis; Artificial intelligence; Artificial neural network; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008557632,0.0001043199,0.0003632632,0.001556672,0.00006403759,0.00002962718,0.000904505,0.00005488803,0.00008644811],"category_scores_gemma":[0.002423921,0.00008718892,0.00007257173,0.003735323,0.0006308091,0.000321563,0.0002646834,0.0002862515,0.000001610422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001189462,"about_ca_system_score_gemma":0.0004465669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004189455,"about_ca_topic_score_gemma":0.000003354515,"domain_scores_codex":[0.997693,0.00003299683,0.0005669162,0.0002557308,0.001227339,0.0002239849],"domain_scores_gemma":[0.9959837,0.0002549276,0.0004251721,0.000151472,0.002980192,0.0002045251],"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":[0.0004172616,0.0003471594,0.001329015,0.00003038435,0.001339179,0.0001293994,0.002241739,0.5199306,0.1671654,0.1728117,0.0001586753,0.1340995],"study_design_scores_gemma":[0.002601294,0.001244218,0.0005370962,0.00008913164,0.0002764768,0.0001963775,0.004142458,0.6869553,0.2388196,0.06323812,0.001576892,0.0003229751],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.143235,0.0001392389,0.8466855,0.009608838,0.0001406247,0.0001016604,0.000009545552,0.00002135664,0.00005824784],"genre_scores_gemma":[0.7028073,0.0002706446,0.2966366,0.000191229,0.00003110011,0.000001244926,8.738101e-7,0.00000742211,0.000053632],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5595723,"threshold_uncertainty_score":0.3555461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06495578451469657,"score_gpt":0.4270622620179934,"score_spread":0.3621064775032968,"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."}}