{"id":"W4367369757","doi":"10.1145/3558481.3591101","title":"A Simple and Efficient Parallel Laplacian Solver","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Complexity and Algorithms in Graphs","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Solver; Binary logarithm; Combinatorics; Cholesky decomposition; Factorization; Simple (philosophy); Laplacian matrix; Mathematics; Laplace operator; Diagonal; Upper and lower bounds; Log-log plot; Discrete mathematics; Graph; Computer science; Eigenvalues and eigenvectors; Algorithm; Physics; Mathematical analysis; Mathematical optimization; 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":[0.000288308,0.001097061,0.0008594749,0.0005473199,0.0008226117,0.001237761,0.001780736,0.001184268,0.02939579],"category_scores_gemma":[0.00193303,0.0004422965,0.0006250648,0.0009770548,0.0005192499,0.001694545,0.002532294,0.00142604,0.009788229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00055736,"about_ca_system_score_gemma":0.001748848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002253304,"about_ca_topic_score_gemma":0.005652107,"domain_scores_codex":[0.9994749,0.00004482995,0.00002836626,0.0001363444,0.0002257478,0.00008974062],"domain_scores_gemma":[0.999587,0.00008580837,0.00002096163,0.0001061889,0.0001469596,0.00005306366],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004255222,0.0003151134,0.001070965,0.0004603533,0.0001020359,0.0007733728,0.0002582577,0.1947371,0.03059959,0.1593769,0.1215775,0.4903033],"study_design_scores_gemma":[0.0001527321,0.0000490563,0.000119111,0.0000175156,0.00001640594,0.0003429854,0.00006896521,0.8579472,0.006667561,0.1097477,0.02484603,0.00002473731],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009712403,0.0002205926,0.9548796,0.0008816738,0.0002503671,0.000154784,0.0006957972,0.006701775,0.02650302],"genre_scores_gemma":[0.1833954,0.000278919,0.7742881,0.0006446627,0.000261467,0.0003386713,0.001738255,0.001654312,0.03740027],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02939579,"threshold_uncertainty_score":0.09833872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0479539260316339,"score_gpt":0.2786057914216173,"score_spread":0.2306518653899834,"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."}}