{"id":"W2761564338","doi":"10.1007/s10589-019-00078-w","title":"A sub-additive DC approach to the complementarity problem","year":2019,"lang":"en","type":"article","venue":"Computational Optimization and Applications","topic":"Advanced Optimization Algorithms Research","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"Agence Nationale de la Recherche","keywords":"Mixed complementarity problem; Complementarity theory; Mathematics; Linear complementarity problem; Maxima and minima; Monotone polygon; Mathematical optimization; Complementarity (molecular biology); Nonlinear complementarity problem; Optimization problem; Regular polygon; Convex function; Stationary point; Applied mathematics; Nonlinear system; Mathematical analysis","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":[],"consensus_categories":[],"category_scores_codex":[0.0002423906,0.0001427226,0.0001523851,0.0001133077,0.0003653571,0.0001033035,0.0002200204,0.00004100658,0.0001635743],"category_scores_gemma":[0.00004451081,0.0001184702,0.00003553351,0.00064596,0.00007031555,0.0001475422,0.0001357542,0.000141676,0.0001043317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006785717,"about_ca_system_score_gemma":0.00006695916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003516109,"about_ca_topic_score_gemma":0.00000253319,"domain_scores_codex":[0.9986962,0.00007174678,0.0003013169,0.0003705859,0.0003658456,0.0001943083],"domain_scores_gemma":[0.9986746,0.0004194831,0.0001135483,0.0002553245,0.0004166112,0.0001204469],"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":[0.00000476105,0.0001113479,0.00005034466,0.00001596838,0.00001576648,3.387951e-8,0.0001708237,0.7637889,0.000003708433,0.2324998,0.001760057,0.001578491],"study_design_scores_gemma":[0.0004788847,0.00002582973,0.0002331854,0.000008284524,0.0000128825,0.000007643568,0.0002920808,0.9403502,0.00001978484,0.04121702,0.0171767,0.0001775537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002392997,0.0000181661,0.986429,0.001648482,0.00001793156,0.002600515,0.00009871428,0.00009778627,0.008850085],"genre_scores_gemma":[0.02771101,0.00002052206,0.968864,0.0008061781,0.00007825946,0.001142518,0.0006435867,0.00003213984,0.0007018474],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1912827,"threshold_uncertainty_score":0.4831076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03961898554497199,"score_gpt":0.3377216631295292,"score_spread":0.2981026775845572,"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."}}