{"id":"W2594607718","doi":"10.48550/arxiv.1702.08166","title":"Linear Convergence of the Proximal Incremental Aggregated Gradient Method under Quadratic Growth Condition","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China; Centre de Recherches Mathématiques; National Science Foundation","keywords":"Mathematics; Rate of convergence; Convex function; Convergence (economics); Applied mathematics; Quadratic equation; Lyapunov function; Function (biology); Regular polygon; Linear growth; Quadratic function; Mathematical analysis; Nonlinear system; Computer science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001988099,0.001163995,0.0009245948,0.0006196877,0.0003607234,0.0008856078,0.001038388,0.001159565,0.002126335],"category_scores_gemma":[0.00602652,0.0004051615,0.0006921663,0.0004910111,0.001488632,0.001644891,0.002002757,0.002033553,0.0005298369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006397332,"about_ca_system_score_gemma":0.001052769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002386885,"about_ca_topic_score_gemma":0.00148166,"domain_scores_codex":[0.9993661,0.0002659192,0.00002682791,0.00008996614,0.0001850452,0.00006621084],"domain_scores_gemma":[0.9981323,0.001188058,0.00009594878,0.0001067562,0.0004004302,0.00007663739],"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":[0.0002255374,0.00006619585,0.0008696427,0.0003960132,0.00009547505,0.0001973685,0.0003467133,0.7813954,0.01058374,0.1231275,0.004160837,0.07853558],"study_design_scores_gemma":[0.000005858497,0.00003098282,0.00004502741,0.00000630077,0.00000441151,0.00002196481,0.000008775611,0.9919508,0.0008824846,0.006631328,0.0004072341,0.000004796574],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007315021,0.0002532568,0.9900274,0.0002249885,0.00004453427,0.00002979912,0.00002075313,0.0001168878,0.001967329],"genre_scores_gemma":[0.5867798,0.001147171,0.3990555,0.000404834,0.0002511552,0.0003143573,0.0002402939,0.0002919743,0.01151491],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002386885,"threshold_uncertainty_score":0.0105142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06799127060950622,"score_gpt":0.2193622684884809,"score_spread":0.1513709978789747,"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."}}