{"id":"W2472752992","doi":"10.1080/03610918.2015.1030414","title":"Computing A-optimal and E-optimal designs for regression models via semidefinite programming","year":2015,"lang":"en","type":"article","venue":"Communications in Statistics - Simulation and Computation","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Semidefinite programming; Optimal design; Mathematical optimization; MATLAB; Linear programming; Computer science; Mathematics; Convergence (economics); Semidefinite embedding; Construct (python library); Quadratically constrained quadratic program; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.003360963,0.000193516,0.0003104506,0.0003580237,0.0003901436,0.0003941715,0.000383816,0.0001033525,0.000002539532],"category_scores_gemma":[0.001766106,0.0001841833,0.00003066442,0.0005280619,0.0002521863,0.0005250482,0.0003755498,0.0001783304,0.000004420927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009330558,"about_ca_system_score_gemma":0.00007517182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002586379,"about_ca_topic_score_gemma":0.00001102363,"domain_scores_codex":[0.9971845,0.0007546454,0.0008979399,0.0004314451,0.0005055452,0.0002259796],"domain_scores_gemma":[0.9910648,0.007144559,0.0004004439,0.0005226976,0.0007043116,0.0001631985],"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":[0.00006834347,0.00006567303,0.0005743342,0.000006985148,0.000005269378,5.228578e-7,0.002992919,0.7134866,0.00006215696,0.0108794,0.00007601486,0.2717818],"study_design_scores_gemma":[0.0009385921,0.0001557404,0.000770525,0.00003633527,0.00001369179,0.000005380644,0.001492703,0.9204538,0.0000210252,0.07548784,0.000438694,0.0001856957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02438666,0.0006052486,0.9738052,0.0001300703,0.00008690985,0.0006872003,0.00003533562,0.00005562323,0.0002077491],"genre_scores_gemma":[0.4972548,0.00001168637,0.5025567,0.00003609354,0.00001025272,0.00002079944,0.00007952311,0.00001190574,0.00001823474],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4728681,"threshold_uncertainty_score":0.7510776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5693965277082369,"score_gpt":0.5724371946419906,"score_spread":0.003040666933753711,"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."}}