{"id":"W2760049195","doi":"10.1109/jproc.2018.2817461","title":"Network Topology and Communication-Computation Tradeoffs in Decentralized Optimization","year":2018,"lang":"en","type":"preprint","venue":"Proceedings of the IEEE","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Air Force Office of Scientific Research; Office of Naval Research; Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Computer science; Network topology; Distributed computing; Convergence (economics); Node (physics); Computation; Variety (cybernetics); Wireless sensor network; Topology (electrical circuits); State (computer science); Distributed algorithm; Function (biology); Mathematical optimization; Computer network; Mathematics; Algorithm; Engineering; Artificial intelligence","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":[0.0007517803,0.0001979833,0.0003694744,0.00009077717,0.0000993786,0.0001827124,0.001760671,0.0002366593,0.000001464007],"category_scores_gemma":[0.0001601516,0.0001712307,0.00006846489,0.0003369612,0.0001510939,0.0002799973,0.0008577663,0.0002982907,0.000001277702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001017124,"about_ca_system_score_gemma":0.00006380116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001184371,"about_ca_topic_score_gemma":0.00001031934,"domain_scores_codex":[0.9984476,0.00006965813,0.0005556159,0.0004084853,0.0002540411,0.0002645907],"domain_scores_gemma":[0.9983934,0.0001219993,0.0007254265,0.0003357628,0.0003774722,0.00004597147],"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":[0.0002113278,0.0004675642,0.0249868,0.001046444,0.0003670903,0.000001680675,0.01036082,0.8491247,0.0009228515,0.0831449,0.02244753,0.006918288],"study_design_scores_gemma":[0.001007783,0.0000342908,0.003844469,0.0006001905,0.00004164803,0.00001378853,0.00007550358,0.964157,0.0003810605,0.02927922,0.0002925746,0.0002724137],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1402344,0.003536704,0.827349,0.01676135,0.004460584,0.003710104,0.00002681348,0.000365413,0.003555667],"genre_scores_gemma":[0.9165848,0.000171409,0.08286981,0.0001770319,0.00009360343,0.00006237107,0.000007579196,0.00001367623,0.000019651],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7763505,"threshold_uncertainty_score":0.6982586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01969086396948241,"score_gpt":0.2527458612212268,"score_spread":0.2330549972517444,"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."}}