{"id":"W1968250174","doi":"10.1137/040620047","title":"A Multipreconditioned Conjugate Gradient Algorithm","year":2006,"lang":"en","type":"article","venue":"SIAM Journal on Matrix Analysis and Applications","topic":"Matrix Theory and Algorithms","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Conjugate gradient method; Preconditioner; Mathematics; Derivation of the conjugate gradient method; Conjugate residual method; Algorithm; Positive-definite matrix; Generalization; Matrix (chemical analysis); Nonlinear conjugate gradient method; Applied mathematics; Biconjugate gradient method; Linear system; Block (permutation group theory); Cholesky decomposition; Gradient method; Conjugate; Domain decomposition methods; Relation (database); Coefficient matrix; Gradient descent; Computer science; Iterative method; Combinatorics; Mathematical analysis; Eigenvalues and eigenvectors; Finite element method; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000419709,0.0001470552,0.0002470867,0.0004485634,0.0005822123,0.0003833156,0.0003790319,0.00004586129,0.00007775429],"category_scores_gemma":[0.000004423522,0.0001211626,0.0002435546,0.001167454,0.00005068277,0.0002429827,0.00005094738,0.0001948487,0.00007469094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002945117,"about_ca_system_score_gemma":0.00002187939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002067908,"about_ca_topic_score_gemma":0.00000611439,"domain_scores_codex":[0.998769,0.00008294563,0.0003860715,0.0003123841,0.000227709,0.0002219218],"domain_scores_gemma":[0.9990438,0.000101576,0.0002413363,0.0003540169,0.0001036897,0.0001555814],"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":[0.000005210492,0.000473801,0.0003316345,0.000007967594,0.0004579575,0.00002177127,0.00005867282,0.003935085,0.0003247855,0.8631027,0.0006388948,0.1306415],"study_design_scores_gemma":[0.001695929,0.0002803443,0.009227598,0.00002525827,0.001034143,0.0003774857,0.0001034728,0.4200037,0.001802121,0.4193366,0.1451926,0.0009207429],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003140372,0.0003096943,0.9943689,0.000834387,0.00004692952,0.0001414731,0.00002553791,0.00006568181,0.001067024],"genre_scores_gemma":[0.8165972,0.0003718216,0.1784594,0.0004021448,0.0009424608,0.0001451855,0.00006646567,0.00001972554,0.002995601],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8159094,"threshold_uncertainty_score":0.4940869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005290628705126683,"score_gpt":0.2455948881573417,"score_spread":0.240304259452215,"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."}}