{"id":"W4384567367","doi":"10.23952/jnva.7.2023.3.09","title":"A new inertial relaxed Tseng extrgradient method for solving quasi-monotone bilevel variational inequality problems in Hilbert spaces","year":2023,"lang":"en","type":"article","venue":"Journal of Nonlinear and Variational Analysis","topic":"Optimization and Variational Analysis","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Monotone polygon; Inertial frame of reference; Hilbert space; Variational inequality; Mathematics; Inequality; Applied mathematics; Pure mathematics; Mathematical economics; Mathematical optimization; Mathematical analysis; Physics; Geometry; Classical mechanics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00300835,0.0002069578,0.0006228738,0.001846402,0.0001854012,0.0002580879,0.0003961571,0.0001183764,0.00007232543],"category_scores_gemma":[0.0007749738,0.0001757944,0.0004563794,0.004379998,0.00001460388,0.0007742558,0.0001218531,0.0002000258,0.000004936349],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001140179,"about_ca_system_score_gemma":0.0004570514,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004579263,"about_ca_topic_score_gemma":0.0002788848,"domain_scores_codex":[0.9971138,0.0002106352,0.001240771,0.0004022307,0.0007612666,0.000271278],"domain_scores_gemma":[0.9967904,0.00118807,0.0008610056,0.000203067,0.0007499622,0.0002074812],"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":[0.00008082101,0.000400629,0.02401464,0.00003229071,0.002206584,0.000006514977,0.001782545,0.8759984,0.000478499,0.09032988,0.0005827514,0.004086457],"study_design_scores_gemma":[0.001106967,0.00008807198,0.08966972,0.00001706372,0.0004248015,0.000008869626,0.00003306534,0.8947424,0.00001423648,0.01293853,0.0007706888,0.000185651],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002677244,0.00007167461,0.9842203,0.01261812,0.0001675783,0.0001511701,0.00003822512,0.00002999062,0.00002573921],"genre_scores_gemma":[0.121624,0.0001083297,0.8766533,0.0003623747,0.0005823072,0.00001752532,0.0001740351,0.00001485249,0.0004632999],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1189467,"threshold_uncertainty_score":0.7168688,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03181744319934866,"score_gpt":0.3131816914988432,"score_spread":0.2813642482994946,"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."}}