{"id":"W3098788150","doi":"10.1109/lcsys.2020.3037038","title":"Convergence Analysis of a Continuous-Time Distributed Gradient Descent Algorithm","year":2020,"lang":"en","type":"article","venue":"IEEE Control Systems Letters","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Convergence (economics); Gradient descent; Computer science; Descent (aeronautics); Computation; Rate of convergence; Distributed algorithm; Gradient method; Mathematical optimization; Algorithm; Stochastic gradient descent; Nonlinear conjugate gradient method; Mathematics; Distributed computing; Artificial intelligence; Artificial neural network; Key (lock); Engineering","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":[0.0007210019,0.0005206665,0.001854447,0.0003522098,0.000125019,0.0002955878,0.001988814,0.0001468273,0.00001792517],"category_scores_gemma":[0.0001350884,0.0004982074,0.000737562,0.002277146,0.0001288552,0.0004451807,0.000106195,0.0002363565,0.000191894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002388331,"about_ca_system_score_gemma":0.00008094666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004646773,"about_ca_topic_score_gemma":0.000004413659,"domain_scores_codex":[0.9947866,0.0006645475,0.001535112,0.001084246,0.00105712,0.0008723548],"domain_scores_gemma":[0.9965109,0.0003324237,0.001031066,0.001156154,0.0003894901,0.0005800213],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001955551,0.0006161855,0.01563848,0.0004186026,0.02091687,0.0008133714,0.00238207,0.2897412,0.6244195,0.002364826,0.03471459,0.007778793],"study_design_scores_gemma":[0.002961815,0.0001190179,0.00219783,0.00006007365,0.0008991301,0.00001048087,0.00004693622,0.989592,0.0005976373,0.000001107662,0.003018863,0.0004951721],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03303983,0.0003435314,0.9581299,0.004079411,0.001534771,0.001297833,0.001133622,0.0004137759,0.00002727689],"genre_scores_gemma":[0.9973714,0.000004401558,0.0005016762,0.001584621,0.0002347885,0.0001514869,0.00009953965,0.00002824975,0.00002387586],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9643315,"threshold_uncertainty_score":0.999747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01093228038103017,"score_gpt":0.2026062027670316,"score_spread":0.1916739223860014,"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."}}