{"id":"W2087131745","doi":"10.1109/tmag.2011.2176318","title":"Efficient Implementation of Gaussian Belief Propagation Solver for Large Sparse Diagonally Dominant Linear Systems","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Magnetics","topic":"Matrix Theory and Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Solver; Speedup; Computer science; Diagonally dominant matrix; Parallel computing; Conjugate gradient method; Gaussian elimination; Diagonal; Algorithm; Belief propagation; Gaussian; Parallel algorithm; Sparse matrix; Applied mathematics; Mathematics; Invertible matrix; Geometry; Physics","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":[0.0007615381,0.000670034,0.0005247609,0.0003798755,0.0004616078,0.0006810604,0.001200302,0.0006975065,0.003889709],"category_scores_gemma":[0.002383415,0.0003281138,0.0003512197,0.0006605582,0.0004985307,0.0007064248,0.0009666083,0.001072493,0.001419822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005600645,"about_ca_system_score_gemma":0.002064164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006869537,"about_ca_topic_score_gemma":0.01119009,"domain_scores_codex":[0.9995584,0.0001029324,0.00001909332,0.00004373189,0.0002209312,0.00005489583],"domain_scores_gemma":[0.9991679,0.000322851,0.00005565206,0.0001135813,0.0002863205,0.00005372697],"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":[0.000270373,0.0002164516,0.001474297,0.0001766243,0.00008816163,0.0002387813,0.0002373955,0.5970267,0.02933558,0.04597841,0.009471836,0.3154854],"study_design_scores_gemma":[0.00001636343,0.00001283988,0.00004634315,0.000002447728,0.000003097693,0.00001132706,0.000006807536,0.9926702,0.003806525,0.002461409,0.0009588166,0.000003981722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006154586,0.00002900032,0.9910077,0.00009830882,0.00001950327,0.00002498611,0.00003129656,0.001320592,0.001313917],"genre_scores_gemma":[0.1380808,0.00006903002,0.8587021,0.0000762748,0.00002176529,0.0001180234,0.0001928957,0.0002560475,0.002483107],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006869537,"threshold_uncertainty_score":0.01365906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01633148315383508,"score_gpt":0.2743900749975526,"score_spread":0.2580585918437175,"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."}}