{"id":"W3123799354","doi":"10.1007/s10898-022-01245-w","title":"TREGO: a trust-region framework for efficient global optimization","year":2022,"lang":"en","type":"article","venue":"Journal of Global Optimization","topic":"Advanced Optimization Algorithms Research","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Global optimization; Python (programming language); Computer science; Benchmark (surveying); Mathematical optimization; Id, ego and super-ego; Trust region; Convergence (economics); Optimization problem; Dimension (graph theory); Algorithm; Mathematics; Geography; Psychology; Economics; Social psychology","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":[0.0008211767,0.0002800869,0.0004922328,0.0001566542,0.0005358497,0.0001176499,0.0005363103,0.0001542204,0.0003529727],"category_scores_gemma":[0.00234151,0.0002800279,0.0003122719,0.001661383,0.00007209245,0.0003370922,0.0001837311,0.0003483708,0.000001680186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002164479,"about_ca_system_score_gemma":0.0003996512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002784204,"about_ca_topic_score_gemma":8.794087e-7,"domain_scores_codex":[0.9965844,0.0002844959,0.001090843,0.0003269907,0.001248749,0.0004645376],"domain_scores_gemma":[0.9965194,0.0003372044,0.001348298,0.0003335481,0.001229519,0.0002320003],"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":[0.0005807691,0.0004560063,0.0002548673,0.00003475728,0.00007553209,0.00002116774,0.00008209737,0.9497946,6.801553e-7,0.04490935,0.00213695,0.001653238],"study_design_scores_gemma":[0.00209668,0.0005795623,0.00002644112,0.00005020678,0.0001079817,0.0003797737,0.0004429965,0.9622341,0.000007683726,0.03311134,0.0007053778,0.0002578809],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0007973975,0.0002280729,0.9951761,0.001331461,0.0008889049,0.0008591848,0.0001223855,0.00007870054,0.00051779],"genre_scores_gemma":[0.02143961,0.00007543014,0.977724,0.0001967362,0.0002903745,0.00006750564,0.00004035215,0.00004859365,0.0001174361],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02064222,"threshold_uncertainty_score":0.9999652,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0386669940503243,"score_gpt":0.3691441841456404,"score_spread":0.3304771900953161,"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."}}