{"id":"W2771039244","doi":"10.1016/j.automatica.2019.108691","title":"A Lie bracket approximation approach to distributed optimization over directed graphs","year":2019,"lang":"en","type":"preprint","venue":"Automatica","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University; Kingston Health Sciences Centre","funders":"","keywords":"Computation; Saddle point; Mathematical optimization; Simple (philosophy); Computer science; Bracket; Lie group; Topological graph theory; Invariant (physics); Graph; Mathematics; Topology (electrical circuits); Theoretical computer science; Algorithm; Voltage graph; Pure mathematics; Combinatorics","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","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.000672367,0.0006672808,0.0009477668,0.0004594033,0.0001404046,0.00116027,0.002512189,0.0005580775,0.00002789139],"category_scores_gemma":[0.0004474903,0.0006624575,0.0002987692,0.001225226,0.0000400384,0.0005543248,0.001687198,0.0005186587,0.0003903576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003605947,"about_ca_system_score_gemma":0.0002715335,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007228667,"about_ca_topic_score_gemma":0.000001800507,"domain_scores_codex":[0.9952785,0.0004221454,0.0010818,0.001473657,0.001049942,0.0006938918],"domain_scores_gemma":[0.9956026,0.0002104398,0.0007570892,0.002744363,0.0003767257,0.0003087536],"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":[0.0000280818,0.0009911868,0.0003297009,0.001383189,0.0005538728,0.00001021307,0.001555148,0.9145727,0.0001539766,0.04649085,0.03015332,0.003777753],"study_design_scores_gemma":[0.0007097566,0.00003604911,0.004004246,0.0002835634,0.00006981156,0.000007637312,0.00001771901,0.9915618,0.00005261719,0.002042251,0.0005064396,0.0007081194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002547441,0.00007033243,0.9840205,0.0005129567,0.001337905,0.003443881,0.0004518682,0.002975434,0.004639727],"genre_scores_gemma":[0.6573982,0.000007160102,0.3371566,0.0002700177,0.0001056861,0.0009490225,0.003840382,0.00007416061,0.0001987495],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6548508,"threshold_uncertainty_score":0.9998766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01629249398203245,"score_gpt":0.2423224211310963,"score_spread":0.2260299271490639,"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."}}