{"id":"W4409289916","doi":"10.1016/j.automatica.2025.112286","title":"Compressed gradient tracking algorithms for distributed nonconvex optimization","year":2025,"lang":"en","type":"article","venue":"Automatica","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; Australian Research Council; Science and Technology Commission of Shanghai Municipality; Knut och Alice Wallenbergs Stiftelse; National University's Basic Research Foundation of China; National Natural Science Foundation of China; Vetenskapsrådet; Japan Society for the Promotion of Science; Stiftelsen för Strategisk Forskning","keywords":"Algorithm; Tracking (education); Compressed sensing; Computer science; Mathematical optimization; Optimization algorithm; Mathematics","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":[0.001521078,0.001054052,0.001351499,0.0008428808,0.0006540044,0.001031439,0.001620944,0.001712402,0.004171054],"category_scores_gemma":[0.008726319,0.0007366286,0.0004721826,0.001426169,0.001424149,0.002413106,0.002449728,0.002506273,0.0006807382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001183836,"about_ca_system_score_gemma":0.001912738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008124516,"about_ca_topic_score_gemma":0.007855083,"domain_scores_codex":[0.9993154,0.0002015348,0.00003100304,0.0001144949,0.0002772997,0.00006028047],"domain_scores_gemma":[0.9969765,0.00185302,0.0001936684,0.0003713242,0.0004991404,0.0001062471],"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":[0.0002150744,0.0001024949,0.0002601254,0.0001124109,0.00004103701,0.00005101161,0.00008576927,0.8375561,0.001673399,0.04552678,0.004657413,0.1097185],"study_design_scores_gemma":[0.00001683193,0.00001295351,0.00002623229,0.000003975116,0.000002018691,0.000006384705,0.000003848958,0.9888614,0.0002623378,0.01039566,0.0004052971,0.000003057227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005349881,0.0002074985,0.9922389,0.0002415152,0.0000816919,0.00003563373,0.00004623342,0.0002697791,0.001528907],"genre_scores_gemma":[0.4342207,0.0005930191,0.5526733,0.0004264645,0.0003316118,0.0004558454,0.0006694997,0.0004520921,0.01017747],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008124516,"threshold_uncertainty_score":0.01615447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02144396792399237,"score_gpt":0.2817025295125486,"score_spread":0.2602585615885562,"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."}}