{"id":"W4400959697","doi":"10.23952/jano.6.2024.3.09","title":"A self-adaptive algorithm with multi-step inertia for solving convex bilevel optimization problems","year":2024,"lang":"en","type":"article","venue":"Journal of Applied and Numerical Optimization","topic":"Optimization and Variational Analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Tianjin Municipal Education Commission","keywords":"Bilevel optimization; Inertia; Mathematical optimization; Computer science; Regular polygon; Algorithm; Convex optimization; Mathematics; Optimization problem; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0007765094,0.0006763358,0.0009564962,0.000572831,0.0004362107,0.0007392975,0.001431367,0.001155645,0.001732344],"category_scores_gemma":[0.002103313,0.0004595722,0.0006882613,0.0006085635,0.000708603,0.001103445,0.001305852,0.001248228,0.0004283417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002976359,"about_ca_system_score_gemma":0.0007184905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001612312,"about_ca_topic_score_gemma":0.00133041,"domain_scores_codex":[0.9995826,0.00009747561,0.00003356782,0.00007912629,0.0001637342,0.00004352338],"domain_scores_gemma":[0.9994413,0.0002450894,0.00006645179,0.00006714628,0.0001402483,0.00003973304],"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":[0.000149831,0.0001279069,0.00126974,0.0002470559,0.0001451294,0.0001400013,0.0001646389,0.7416918,0.01112006,0.02975707,0.002249175,0.2129376],"study_design_scores_gemma":[0.00001426504,0.00003213184,0.00005443561,0.00000467095,0.000005498887,0.0000204152,0.00000353873,0.9975777,0.000539744,0.001050626,0.000690782,0.000006138554],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004820063,0.0001309566,0.993942,0.00005101936,0.00004015976,0.00002100248,0.00000502712,0.0001282256,0.0008616624],"genre_scores_gemma":[0.3359135,0.0003638888,0.6596155,0.0001821043,0.000131878,0.0003390363,0.00009699084,0.0001398665,0.003217291],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001732344,"threshold_uncertainty_score":0.0057953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01268038976696528,"score_gpt":0.2277845085534329,"score_spread":0.2151041187864677,"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."}}