{"id":"W2169422553","doi":"10.1109/infcom.2007.32","title":"Global Convergence of An Iterative Gradient Algorithm for The Nash Equilibrium in An Extended OSNR Game","year":2007,"lang":"en","type":"article","venue":"","topic":"Optical Network Technologies","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Nash equilibrium; Computer science; Convergence (economics); Mathematical optimization; Wavelength-division multiplexing; Iterative method; Channel (broadcasting); Algorithm; Mathematics; Wavelength; Telecommunications; Optics; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002879954,0.001685352,0.002025684,0.0009241571,0.0007257748,0.001441744,0.001579626,0.002000824,0.00233108],"category_scores_gemma":[0.010922,0.0007097611,0.000697694,0.0005306992,0.002503166,0.001644864,0.002298881,0.001608107,0.0004572882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001544802,"about_ca_system_score_gemma":0.002559547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006031152,"about_ca_topic_score_gemma":0.004311444,"domain_scores_codex":[0.9990065,0.0004849767,0.00003516352,0.0001445893,0.0001753004,0.000153578],"domain_scores_gemma":[0.9954374,0.003553465,0.0002594861,0.000114934,0.0004833177,0.0001513264],"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":[0.0001082036,0.00006406449,0.0006286592,0.00009161825,0.00004102352,0.0001007308,0.0002001144,0.9349954,0.001117243,0.04453648,0.0008179642,0.01729855],"study_design_scores_gemma":[0.00001684403,0.00002957327,0.00003399538,0.00000807391,0.000003607183,0.00001093459,0.00001627281,0.9912984,0.0001740121,0.00817158,0.0002313483,0.000005318273],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02259087,0.0001757246,0.971261,0.0002864414,0.00003170167,0.0001122247,0.00002237526,0.0001832744,0.00533643],"genre_scores_gemma":[0.6406987,0.0003238589,0.3522609,0.0002242637,0.0000435333,0.0006826008,0.00009862556,0.0001430419,0.005524369],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006031152,"threshold_uncertainty_score":0.01523077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0131157422633303,"score_gpt":0.2695823812794256,"score_spread":0.2564666390160953,"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."}}