{"id":"W4415156424","doi":"10.48550/arxiv.2504.09375","title":"Efficient Gradient-Enhanced Bayesian Optimizer with Comparisons to Conjugate-Gradient and Quasi-Newton Optimizers for Unconstrained Local Optimization","year":2025,"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; University of Toronto","keywords":"Bayesian probability; Probabilistic logic; Bayesian optimization; Function (biology); Minification; Surrogate model; Local optimum","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004162795,0.001633809,0.001866529,0.001022529,0.0007007429,0.001601646,0.001532975,0.001662118,0.006436467],"category_scores_gemma":[0.0108797,0.0008556882,0.00108903,0.0009400093,0.001041082,0.001814642,0.001759794,0.00232582,0.002150004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00112382,"about_ca_system_score_gemma":0.002963551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007633832,"about_ca_topic_score_gemma":0.008442612,"domain_scores_codex":[0.9979843,0.0008765158,0.000102598,0.0001740912,0.0007652656,0.00009733163],"domain_scores_gemma":[0.9970998,0.001363041,0.0002321155,0.000358202,0.0008493179,0.00009755151],"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":[0.0002188738,0.0001067779,0.0004621256,0.0002287345,0.00009665638,0.00007070554,0.0001166446,0.8464034,0.004324405,0.03500733,0.004042408,0.1089218],"study_design_scores_gemma":[0.00001383625,0.0000277636,0.00008921939,0.0000141444,0.000007815198,0.00001564856,0.000007811375,0.9943012,0.001259525,0.003004696,0.001245889,0.00001234772],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005080044,0.0003823499,0.9895613,0.000183345,0.00003480836,0.00006049412,0.00004236021,0.001311782,0.003343534],"genre_scores_gemma":[0.1515775,0.0004568969,0.8406567,0.0002751183,0.000057775,0.0003534786,0.0002905844,0.001843501,0.004488366],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007633832,"threshold_uncertainty_score":0.02201521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05163697294827471,"score_gpt":0.3423064489945036,"score_spread":0.2906694760462289,"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."}}