{"id":"W2795213612","doi":"10.2298/yjor180120014g","title":"Less is more: Simplified Nelder-Mead method for large unconstrained optimization","year":2018,"lang":"en","type":"article","venue":"Yugoslav journal of operations research","topic":"Advanced Optimization Algorithms Research","field":"Mathematics","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Regular polygon; Mathematical optimization; Simplex; Simplex algorithm; Simple (philosophy); Algorithm; Computer science; Continuous optimization; Mathematics; Optimization problem; Convex optimization; Linear programming; Combinatorics; Multi-swarm optimization; Geometry","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.006060963,0.0002189117,0.0004571654,0.0009750441,0.001136062,0.0003569511,0.0006954686,0.000197208,0.001175827],"category_scores_gemma":[0.006703333,0.0001887339,0.0001859687,0.001400126,0.0002951431,0.0007252988,0.0001664451,0.0007359771,0.00002201293],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002620946,"about_ca_system_score_gemma":0.00102118,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001320197,"about_ca_topic_score_gemma":0.00005376106,"domain_scores_codex":[0.9956942,0.0006437433,0.001034389,0.0003537077,0.001493024,0.0007809925],"domain_scores_gemma":[0.9874712,0.001679897,0.0001781725,0.000529942,0.009825611,0.0003151302],"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":[0.001650751,0.003184148,0.0001749621,0.0005383663,0.001187888,0.00009465263,0.01935733,0.5369676,0.01085688,0.2070916,0.1472677,0.07162819],"study_design_scores_gemma":[0.002975133,0.0007457307,0.000008031408,0.00009302779,0.00003629874,0.0001143446,0.004427887,0.966086,0.010247,0.0102816,0.004739368,0.0002455332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00170845,0.00007123441,0.9904083,0.005307255,0.0001876634,0.001049343,0.00008726941,0.00003179472,0.001148667],"genre_scores_gemma":[0.01672392,0.00009863263,0.9774769,0.0002161521,0.0007625266,0.00008933294,0.00002613436,0.00008087761,0.004525507],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4291185,"threshold_uncertainty_score":0.9997373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2462101905584332,"score_gpt":0.5465173684657684,"score_spread":0.3003071779073352,"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."}}