{"id":"W2964128584","doi":"10.48550/arxiv.1907.10169","title":"Distributed Model Predictive Control Under Inexact Primal-Dual Gradient Optimization Based on Contraction Analysis","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Mathematical optimization; Convergence (economics); Dual (grammatical number); Computer science; Optimization problem; Model predictive control; Constraint (computer-aided design); Nonlinear system; Contraction (grammar); Control theory (sociology); Stability (learning theory); Mathematics; Control (management)","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":[0.00094058,0.0008530701,0.001195512,0.0002777975,0.0004350668,0.0008127328,0.0008721286,0.0006805806,0.000938217],"category_scores_gemma":[0.001394809,0.0003181609,0.0004973052,0.0004012645,0.0009300967,0.000768171,0.001388238,0.00125601,0.0001481001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005493765,"about_ca_system_score_gemma":0.001128149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002536739,"about_ca_topic_score_gemma":0.001517511,"domain_scores_codex":[0.9996083,0.000122089,0.00001330564,0.00007959834,0.0001371477,0.00003945615],"domain_scores_gemma":[0.9995584,0.0002196719,0.00006238181,0.00003866245,0.00009953024,0.00002127346],"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":[0.00003063132,0.00001754438,0.0001253845,0.00004616611,0.00001501378,0.00004799147,0.00004173498,0.9677743,0.001791904,0.01566888,0.0003288761,0.01411147],"study_design_scores_gemma":[0.000003531949,0.00001163347,0.00001130747,0.000001278616,0.000001421065,0.000004805953,0.000001700593,0.9981933,0.0001822286,0.00146151,0.000126086,0.000001217809],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007977188,0.000105113,0.9898078,0.00008939932,0.00002028055,0.00001898005,0.000009252009,0.00007163201,0.001900457],"genre_scores_gemma":[0.8829376,0.0002619982,0.1134995,0.00009104524,0.00005356308,0.0002548019,0.0000708579,0.00005251655,0.002778092],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002536739,"threshold_uncertainty_score":0.005043924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02162860359813129,"score_gpt":0.1665109564651523,"score_spread":0.144882352867021,"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."}}