{"id":"W3155416702","doi":"10.3934/math.2021383","title":"A hybrid FR-DY conjugate gradient algorithm for unconstrained optimization with application in portfolio selection","year":2021,"lang":"en","type":"article","venue":"AIMS Mathematics","topic":"Advanced Optimization Algorithms Research","field":"Mathematics","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Broyden–Fletcher–Goldfarb–Shanno algorithm; Line search; Conjugate gradient method; Selection (genetic algorithm); Mathematical optimization; Convergence (economics); Gradient descent; Algorithm; Nonlinear conjugate gradient method; Computer science; Descent (aeronautics); Portfolio; Mathematics; Artificial intelligence","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000494497,0.0002780947,0.000442572,0.0002344003,0.0001460738,0.00008172665,0.0001645028,0.0001018941,0.0001049796],"category_scores_gemma":[0.0004895567,0.0002647381,0.00007843276,0.0007704969,0.00008624862,0.000253498,0.00005226837,0.0002124066,0.00001035671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002384657,"about_ca_system_score_gemma":0.0002482429,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007457293,"about_ca_topic_score_gemma":0.00004373428,"domain_scores_codex":[0.9978548,0.00006307389,0.0006855181,0.0004806787,0.0004621154,0.0004538391],"domain_scores_gemma":[0.9978142,0.0004944133,0.0003451873,0.0004236947,0.0007973684,0.0001250761],"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":[0.0001713687,0.007089687,0.0002852144,0.002210428,0.00057615,0.0001749202,0.003559092,0.6120359,0.001196668,0.2458985,0.002175202,0.1246268],"study_design_scores_gemma":[0.001627467,0.0001387996,0.00000314605,0.0001064367,0.0000512163,0.0001673689,0.0004503145,0.9195023,0.004767655,0.07256789,0.0003162276,0.0003012331],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002529697,0.00002896033,0.994432,0.0001710219,0.0000507345,0.001593158,0.00004536581,0.000176329,0.0009727708],"genre_scores_gemma":[0.006556526,0.00004529436,0.9912687,0.00006239548,0.00006732607,0.0006726936,0.0002138372,0.0001005619,0.001012685],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3074663,"threshold_uncertainty_score":0.9999805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02337774315872634,"score_gpt":0.312855859412198,"score_spread":0.2894781162534716,"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."}}