{"id":"W3112731927","doi":"","title":"Comparison of conjugate gradient method on solving unconstrained optimization problems","year":2020,"lang":"en","type":"article","venue":"International Conference on Industrial Engineering and Operations Management","topic":"Advanced Optimization Algorithms Research","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Conjugate gradient method; Computer science; Nonlinear conjugate gradient method; Conjugate; Mathematical optimization; Derivation of the conjugate gradient method; Gradient method; Conjugate residual method; Applied mathematics; Mathematics; Algorithm; Gradient descent; Artificial intelligence; Artificial neural network; Mathematical analysis","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":[0.003018018,0.001130916,0.001556612,0.001232915,0.0007188239,0.00120533,0.001046626,0.00173038,0.004229358],"category_scores_gemma":[0.007985834,0.0003612897,0.000893618,0.001393833,0.0006309195,0.001241511,0.001114529,0.001023459,0.0005141488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005079224,"about_ca_system_score_gemma":0.001579654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004527108,"about_ca_topic_score_gemma":0.003882914,"domain_scores_codex":[0.9984945,0.0008840148,0.00007411835,0.00009954631,0.0003681548,0.00007966827],"domain_scores_gemma":[0.9963086,0.002422041,0.00009218091,0.000219552,0.0008497681,0.0001078457],"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":[0.0008307446,0.000309354,0.001363075,0.0005558979,0.0002423537,0.00008630286,0.0000963557,0.8317305,0.003037749,0.01267349,0.00278811,0.1462861],"study_design_scores_gemma":[0.00004768622,0.0001449454,0.0005771874,0.00001608281,0.00002548915,0.00002531237,0.00002293285,0.9955445,0.001057999,0.001668179,0.0008573281,0.00001237285],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1123774,0.003148296,0.8649217,0.0005222018,0.0007048407,0.0002324674,0.0001514276,0.0006604297,0.01728108],"genre_scores_gemma":[0.4963375,0.001462252,0.4948342,0.0002857595,0.0001933357,0.0003302902,0.0004090838,0.0004727175,0.005674861],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004527108,"threshold_uncertainty_score":0.01596099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2004435237611576,"score_gpt":0.3959218037650013,"score_spread":0.1954782800038437,"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."}}