{"id":"W4388904464","doi":"10.1016/j.ifacol.2023.10.619","title":"Distributed optimization by Newton consensus over connected digraphs","year":2023,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hessian matrix; Newton's method; Quasi-Newton method; Computer science; Mathematical optimization; Computation; Distributed algorithm; Graph; Laplace operator; Mathematics; Algorithm; Theoretical computer science; Applied mathematics; Distributed computing; Nonlinear system","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003302151,0.0003818196,0.0004427567,0.0002086916,0.000235426,0.0002949165,0.0009262795,0.0002058672,0.00008162225],"category_scores_gemma":[0.0003662542,0.0003701253,0.0001749352,0.002133651,0.0000896805,0.0003083464,0.0002057162,0.0002192883,0.0002650731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001200973,"about_ca_system_score_gemma":0.00009415371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001954593,"about_ca_topic_score_gemma":0.00003271527,"domain_scores_codex":[0.9970732,0.0001861282,0.0005724929,0.0008125107,0.0006025207,0.0007530898],"domain_scores_gemma":[0.9980085,0.0003586875,0.0002662722,0.0008584585,0.0002212188,0.0002868281],"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":[0.0006197154,0.002667744,0.02109676,0.00046122,0.002467093,0.001756139,0.003338658,0.4722043,0.2472557,0.03589378,0.1792352,0.03300368],"study_design_scores_gemma":[0.002575095,0.00008967903,0.002773181,0.0000468261,0.00003243199,0.00001957984,0.0001050611,0.9840801,0.000657494,0.00006234938,0.009033891,0.0005243297],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1092885,0.0005018522,0.8605511,0.01652753,0.002180215,0.001389415,0.004259293,0.004631323,0.0006707062],"genre_scores_gemma":[0.8420922,0.0001685712,0.1343946,0.001741199,0.0005481833,0.0002250709,0.01579516,0.0001643858,0.004870706],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7328036,"threshold_uncertainty_score":0.9998751,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01073207797552232,"score_gpt":0.238471121155172,"score_spread":0.2277390431796497,"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."}}