{"id":"W4391956260","doi":"10.1007/s10957-024-02383-9","title":"A Mirror Inertial Forward–Reflected–Backward Splitting: Convergence Analysis Beyond Convexity and Lipschitz Smoothness","year":2024,"lang":"en","type":"article","venue":"Journal of Optimization Theory and Applications","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Convexity; Smoothness; Inertial frame of reference; Mathematics; Lipschitz continuity; Theory of computation; Applied mathematics; Rate of convergence; Function (biology); Convergence (economics); Mathematical optimization; Mathematical analysis; Computer science; Key (lock); Algorithm; Classical mechanics","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":[0.0003076764,0.000108635,0.000196261,0.000245806,0.00008925347,0.0001002717,0.00008219856,0.00006488581,0.00004895017],"category_scores_gemma":[0.00002389451,0.00009632748,0.00007403352,0.0005356396,0.00007908875,0.0001819467,0.00002144509,0.0001555047,0.000001243221],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001491389,"about_ca_system_score_gemma":0.00001961362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001668644,"about_ca_topic_score_gemma":0.00000104969,"domain_scores_codex":[0.999343,0.00004978896,0.0003017002,0.0001209664,0.0000919728,0.00009260884],"domain_scores_gemma":[0.9994549,0.0001463571,0.00007986341,0.0001155041,0.0001246978,0.00007865671],"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":[0.0002294034,0.0001652933,0.001985043,0.0003657538,0.003484595,0.00003434666,0.002071249,0.6615563,0.03336795,0.2545442,0.001408469,0.04078737],"study_design_scores_gemma":[0.0003504708,0.00007819781,0.001296631,0.0001288628,0.001534857,0.0001427515,0.0002747946,0.9373457,0.01414386,0.03507105,0.009232825,0.0004000335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06142453,0.001864139,0.9356441,0.0001169666,0.00007908607,0.0001206806,0.00000876613,0.0001467246,0.000595036],"genre_scores_gemma":[0.9805673,0.0009330933,0.01828603,0.00004682164,0.00007715203,0.00001480239,0.000008765037,0.00001683033,0.0000492149],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9191428,"threshold_uncertainty_score":0.3928121,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00657811400616399,"score_gpt":0.2477912233952005,"score_spread":0.2412131093890365,"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."}}