{"id":"W2051271081","doi":"10.5539/mas.v2n6p124","title":"Study on the GMRES (m) Method of Krylov Subspace and Its Application","year":2008,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Matrix Theory and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Generalized minimal residual method; Krylov subspace; Iterative method; Computer science; Nonlinear system; Projection (relational algebra); Scale (ratio); Mathematics; Mathematical optimization; Nonlinear programming; Subspace topology; Applied mathematics; Algorithm; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001112545,0.0009479025,0.0009979123,0.0009337892,0.0005482038,0.0006380228,0.0007563208,0.001171552,0.003131339],"category_scores_gemma":[0.002143497,0.0003395168,0.0009948276,0.00134754,0.001166739,0.001587125,0.001087491,0.001972354,0.001033176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004386548,"about_ca_system_score_gemma":0.0008617439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001962245,"about_ca_topic_score_gemma":0.000821157,"domain_scores_codex":[0.9991035,0.0003220894,0.00004391255,0.0001597254,0.0003227828,0.00004794445],"domain_scores_gemma":[0.9993132,0.0003075695,0.0000472178,0.00005789783,0.0002347335,0.00003946173],"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":[0.0001096269,0.00008878323,0.001411009,0.00111796,0.0001381429,0.0004277638,0.0005494507,0.1188626,0.01673843,0.5908759,0.008919909,0.2607604],"study_design_scores_gemma":[0.00003890403,0.0002192441,0.001095957,0.000208103,0.00006147759,0.001104717,0.0001500444,0.7227746,0.008506641,0.1820216,0.08370795,0.0001107355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006096437,0.01273134,0.9624032,0.0008085013,0.0003321291,0.00003594581,0.00002868916,0.0001272297,0.01743643],"genre_scores_gemma":[0.340555,0.04573379,0.5846063,0.001016296,0.002034374,0.0002879082,0.0002493992,0.0004128486,0.0251039],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003131339,"threshold_uncertainty_score":0.01047534,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03653612240684453,"score_gpt":0.2900708469644943,"score_spread":0.2535347245576498,"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."}}