{"id":"W2539372614","doi":"10.1137/15m1048896","title":"On Convergence Rate of Distributed Stochastic Gradient Algorithm for Convex Optimization with Inequality Constraints","year":2016,"lang":"en","type":"article","venue":"SIAM Journal on Control and Optimization","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Government of Jiangsu Province; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China; City University of Hong Kong","keywords":"Mathematics; Rate of convergence; Convex function; Convergence (economics); Bounded function; Convex optimization; Mathematical optimization; Computation; Regular polygon; Constraint (computer-aided design); Optimization problem; Algorithm; Computer science; Mathematical analysis","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":[0.007574771,0.001718142,0.00165151,0.001124064,0.0009619989,0.001466445,0.001888674,0.001882873,0.003064334],"category_scores_gemma":[0.0267205,0.0005026435,0.001206032,0.0009005865,0.002246282,0.002656575,0.002406807,0.003560435,0.0007490855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001549907,"about_ca_system_score_gemma":0.002415536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004465233,"about_ca_topic_score_gemma":0.002431889,"domain_scores_codex":[0.9973623,0.001290995,0.00009858948,0.0003491867,0.000659361,0.0002395817],"domain_scores_gemma":[0.9872411,0.009492967,0.0005005749,0.0005967203,0.001894832,0.0002738852],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000344688,0.00009175858,0.001300818,0.0002511656,0.00009474908,0.0001276508,0.0002262875,0.8317003,0.003362583,0.1174795,0.002455367,0.04256519],"study_design_scores_gemma":[0.00001241072,0.00002742534,0.00005815726,0.0000148847,0.000006333392,0.00001650985,0.000009462076,0.9913474,0.0006149157,0.007495502,0.0003908901,0.00000611322],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009530894,0.0007844129,0.9851781,0.0004127188,0.00007960231,0.00005749638,0.00002136326,0.0002112066,0.003724267],"genre_scores_gemma":[0.5224141,0.001938159,0.4663336,0.0006115755,0.000192461,0.0006287083,0.0002406216,0.0007050139,0.006935745],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007574771,"threshold_uncertainty_score":0.04005969,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0106527033334437,"score_gpt":0.2261616350650727,"score_spread":0.215508931731629,"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."}}