{"id":"W4320342471","doi":"10.48550/arxiv.2302.04370","title":"Adaptive State-Dependent Diffusion for Derivative-Free Optimization","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"ETH Zürich Foundation; Eidgenössische Technische Hochschule Zürich; McGill University; National Science Foundation","keywords":"Simulated annealing; Derivative (finance); Simplex; Convergence (economics); Rate of convergence; Mathematical optimization; Algebraic number; Global optimization; Applied mathematics; Mathematics; Variance (accounting); Adaptive simulated annealing; Computer science; State (computer science); Algorithm; Key (lock); Mathematical analysis; Combinatorics","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":[0.001695917,0.0008593115,0.0009322615,0.0007046551,0.0005179602,0.001345332,0.001094675,0.001508538,0.002304661],"category_scores_gemma":[0.005859392,0.0005217213,0.0009522666,0.000528254,0.001879639,0.001367489,0.001838189,0.002104873,0.0004885715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001609645,"about_ca_system_score_gemma":0.001224637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002564444,"about_ca_topic_score_gemma":0.001750949,"domain_scores_codex":[0.999112,0.0003558295,0.00003944912,0.000151785,0.000283999,0.00005700612],"domain_scores_gemma":[0.9984443,0.001033916,0.0001255575,0.0001116276,0.0002074805,0.00007713965],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004655079,0.00003963133,0.0003289976,0.0001064139,0.00005929453,0.00007694423,0.00009261254,0.5593634,0.004243892,0.4175878,0.001123694,0.01693064],"study_design_scores_gemma":[0.000006732385,0.00001162975,0.00004045828,0.000005793139,0.000004410136,0.0000105283,0.000002196843,0.9564542,0.0003099342,0.04238073,0.0007662618,0.000007088047],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00607306,0.0003692411,0.9896713,0.0003267362,0.00005939281,0.00002200569,0.00001901327,0.00008891207,0.003370422],"genre_scores_gemma":[0.7232579,0.001187897,0.256152,0.0004266521,0.0002089455,0.0003244357,0.000139033,0.0003088012,0.01799428],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002564444,"threshold_uncertainty_score":0.01167881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1246808000343557,"score_gpt":0.1891514892014781,"score_spread":0.06447068916712244,"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."}}