{"id":"W2012884505","doi":"10.1007/s00158-011-0666-3","title":"pyOpt: a Python-based object-oriented framework for nonlinear constrained optimization","year":2011,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":422,"is_retracted":false,"has_abstract":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Python (programming language); Computer science; Optimization problem; Mathematical optimization; Nonlinear programming; Object-oriented programming; Engineering optimization; Programming language; Theoretical computer science; Nonlinear system; Algorithm; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001285752,0.001730532,0.00157885,0.0008773546,0.0007779436,0.001744916,0.003898795,0.001293718,0.04530514],"category_scores_gemma":[0.003598421,0.001089843,0.001744039,0.001093488,0.0008202716,0.00180095,0.002234482,0.002810992,0.01291927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006177268,"about_ca_system_score_gemma":0.001947578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004607457,"about_ca_topic_score_gemma":0.005590914,"domain_scores_codex":[0.9994122,0.0001080529,0.00005557846,0.00008274432,0.0002536409,0.0000877522],"domain_scores_gemma":[0.9986778,0.0007093263,0.0000783271,0.00018288,0.0002391191,0.0001124163],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00103162,0.0005391635,0.003079946,0.002621053,0.0005007174,0.0007826024,0.0004701749,0.2240845,0.01382185,0.09047589,0.2766661,0.3859264],"study_design_scores_gemma":[0.0004387987,0.00005706961,0.0008304313,0.0001310372,0.0000599284,0.000233199,0.00005075591,0.8217344,0.01262344,0.05652374,0.1072117,0.0001055407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001717731,0.0001192407,0.8694876,0.0001535231,0.0001137002,0.0001109998,0.002299814,0.120253,0.005744351],"genre_scores_gemma":[0.06969276,0.0005361615,0.8502666,0.0006810515,0.0001168176,0.001154753,0.006689806,0.05868217,0.01217987],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04530514,"threshold_uncertainty_score":0.1515608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02009641153152643,"score_gpt":0.2790700468690601,"score_spread":0.2589736353375337,"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."}}