{"id":"W2149623001","doi":"10.1016/s0024-3795(01)00472-4","title":"Efficient matrix preconditioners for black box linear algebra","year":2002,"lang":"en","type":"article","venue":"Linear Algebra and its Applications","topic":"Matrix Theory and Algorithms","field":"Computer Science","cited_by":72,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Preconditioner; Linear algebra; Algebra over a field; Mathematics; Toeplitz matrix; Numerical linear algebra; Computation; Matrix (chemical analysis); Algebraic number; Linear system; Pure mathematics; Algorithm; Mathematical analysis; Geometry","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":[0.0009483795,0.0009522352,0.001266144,0.0005533,0.0009956544,0.00122669,0.001324259,0.001004723,0.01657108],"category_scores_gemma":[0.003670656,0.0005458042,0.000544405,0.001084539,0.0009612317,0.00235414,0.001957894,0.002695058,0.004367729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004917193,"about_ca_system_score_gemma":0.001297141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001842663,"about_ca_topic_score_gemma":0.004684907,"domain_scores_codex":[0.9991302,0.0002306662,0.00003522773,0.00007713554,0.0004158633,0.0001108407],"domain_scores_gemma":[0.9987876,0.0004839831,0.00008938876,0.0003300057,0.0002344512,0.00007464793],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0009993637,0.0002490805,0.0003506938,0.0003249632,0.00009709352,0.0001588074,0.0002890476,0.1994828,0.0221632,0.3845066,0.04054424,0.3508342],"study_design_scores_gemma":[0.0001454933,0.00006279612,0.00009709695,0.00002555283,0.00001621777,0.00003350308,0.00004128931,0.7428606,0.01108369,0.2306002,0.01501077,0.000022891],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008324414,0.0001543962,0.982685,0.0002892444,0.0001708456,0.00005582555,0.0001497668,0.001782776,0.006387827],"genre_scores_gemma":[0.1673327,0.0003416124,0.807866,0.0002539311,0.0002493001,0.0003779765,0.0005025718,0.001162892,0.02191294],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01657108,"threshold_uncertainty_score":0.05543578,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01530175664444624,"score_gpt":0.2593083641973076,"score_spread":0.2440066075528613,"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."}}