{"id":"W2079171899","doi":"10.1016/j.cma.2012.10.008","title":"Preconditioned iteration for saddle-point systems with bound constraints arising in contact problems","year":2012,"lang":"en","type":"article","venue":"Computer Methods in Applied Mechanics and Engineering","topic":"Advanced Numerical Methods in Computational Mathematics","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Électricité de France; State of New Jersey Department of Agriculture","keywords":"Preconditioner; Saddle point; Lagrange multiplier; Mathematics; Mathematical optimization; Block (permutation group theory); Interior point method; Saddle; Applied mathematics; Minification; Iterative method; Algorithm; Geometry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001853722,0.0009311253,0.001875156,0.0007255569,0.001044456,0.001718283,0.001623333,0.002954304,0.004945998],"category_scores_gemma":[0.007949718,0.0008019414,0.0009219917,0.0006390282,0.002658575,0.001418255,0.003341703,0.002287672,0.0004631472],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007469712,"about_ca_system_score_gemma":0.001746592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005769678,"about_ca_topic_score_gemma":0.005659874,"domain_scores_codex":[0.999351,0.0003419043,0.0000367398,0.00006473267,0.0001473317,0.00005834901],"domain_scores_gemma":[0.9972538,0.001955452,0.0001537637,0.0001541194,0.0002880777,0.0001948841],"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":[0.0002105231,0.0001222214,0.0007072725,0.000258083,0.00007708296,0.000278517,0.000283948,0.8223694,0.002149253,0.1515716,0.002154654,0.01981746],"study_design_scores_gemma":[0.00001893227,0.00001226116,0.00003806724,0.000007521383,0.000003595625,0.000008141983,0.00001378917,0.9811902,0.0001820269,0.01817093,0.0003493245,0.000005237394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03625581,0.0003775489,0.9513016,0.0005718695,0.0001770669,0.0001193483,0.0001044696,0.0001892917,0.01090293],"genre_scores_gemma":[0.6796165,0.0004957516,0.3039723,0.0002388573,0.0001704022,0.0006633835,0.000359821,0.0003924356,0.01409047],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005769678,"threshold_uncertainty_score":0.01654601,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02901817862751086,"score_gpt":0.2945136073378437,"score_spread":0.2654954287103328,"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."}}