{"id":"W3092221604","doi":"10.1002/nla.2337","title":"Minimizing convex quadratics with variable precision conjugate gradients","year":2020,"lang":"en","type":"article","venue":"Numerical Linear Algebra with Applications","topic":"Advanced Optimization Algorithms Research","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Agence Nationale de la Recherche","keywords":"Mathematics; Conjugate gradient method; Context (archaeology); Computation; Conjugate; Quadratic equation; Matrix (chemical analysis); Mathematical optimization; Regular polygon; Variable (mathematics); Convex optimization; Applied 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":[0.002619691,0.0008186459,0.001011136,0.0004794197,0.0003467749,0.001352337,0.0007178892,0.0008510525,0.001747298],"category_scores_gemma":[0.009643325,0.0004088129,0.0003821666,0.0006296287,0.001707103,0.001216391,0.001388189,0.001470659,0.0004660195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007929584,"about_ca_system_score_gemma":0.001065142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00160354,"about_ca_topic_score_gemma":0.001285627,"domain_scores_codex":[0.9989427,0.0005721984,0.00002682087,0.0001067974,0.0002862309,0.00006524228],"domain_scores_gemma":[0.9972474,0.001840546,0.0002109442,0.0002856228,0.0003346898,0.00008085911],"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":[0.0001105122,0.00002802749,0.0002506879,0.0001019625,0.00003527881,0.00005382302,0.00005840533,0.8590746,0.003781101,0.1035456,0.001602756,0.03135725],"study_design_scores_gemma":[0.000006628292,0.00001911926,0.00003499588,0.000004842605,0.000001980531,0.000006830784,0.000003439533,0.9849195,0.0008862377,0.01377145,0.0003410917,0.00000385855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01616641,0.0002515632,0.9807767,0.0003404157,0.00005693838,0.00002009601,0.00001430367,0.0001100038,0.002263564],"genre_scores_gemma":[0.6348909,0.0005058144,0.3580291,0.0001993001,0.0001345628,0.0001214742,0.00006995363,0.0002625599,0.00578632],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002619691,"threshold_uncertainty_score":0.01385444,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04017474802119845,"score_gpt":0.3171565609560845,"score_spread":0.2769818129348861,"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."}}