{"id":"W4401399850","doi":"10.1142/9789811267048_0004","title":"Two Heuristic Methods for Solving Generalized Nash Equilibrium Problems Using a Novel Penalty Function","year":2024,"lang":"en","type":"book-chapter","venue":"Series on computers and operations research","topic":"Optimization and Variational Analysis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Penalty method; Mathematical optimization; Nash equilibrium; Heuristic; Function (biology); Computer science; Mathematics; Applied mathematics; Mathematical economics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00129732,0.0002929744,0.0003705332,0.0007618472,0.0008128712,0.001924779,0.0004456797,0.0001508614,0.00006880439],"category_scores_gemma":[0.00006411118,0.0002643083,0.0001661977,0.0003358801,0.0001225929,0.0005392327,0.0005532058,0.0004307855,0.00001921143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00014924,"about_ca_system_score_gemma":0.0003396328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008128068,"about_ca_topic_score_gemma":0.00007429388,"domain_scores_codex":[0.9977643,0.00009209945,0.0004568895,0.0008498394,0.0004903708,0.0003464423],"domain_scores_gemma":[0.9980253,0.0003532949,0.00005837729,0.0004553592,0.0009592957,0.0001483604],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000146767,0.00002252613,2.327472e-7,0.00008075876,0.0001352217,0.000001241429,0.0001590262,0.2043872,0.000597015,0.7902998,0.0003827779,0.003919533],"study_design_scores_gemma":[0.0003405521,0.0002371209,0.000001219114,0.0001733375,0.00004831906,0.00001493773,0.000008999663,0.9406567,0.00002861275,0.02337775,0.03484162,0.0002708632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00001329557,0.0005743759,0.9895231,0.001948287,0.0006848753,0.0007155137,0.00004737341,0.0001025671,0.006390593],"genre_scores_gemma":[0.0005398446,0.0001928245,0.8784913,0.0003143723,0.0005149517,0.00009381492,0.0002002824,0.00006000191,0.1195926],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.766922,"threshold_uncertainty_score":0.9999809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1415827206551833,"score_gpt":0.4142872780819191,"score_spread":0.2727045574267358,"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."}}