An optimized computer implementation of incomplete Cholesky factorization |
| |
| Affiliation: | 1. Neuropsychiatric Epidemiology Research Unit, Division of Psychiatry, Medical School, University of Western Australia, Perth, Australia;2. Pharmacology, School of Biomedical Sciences, University of Western Australia, Perth, Australia;3. Centre for Clinical Research in Neuropsychiatry, Division of Psychiatry, Medical School, University of Western Australia, Perth, Australia;1. Research School of Economics, Australian National University, Australia;2. Centre for Applied Macroeconomic Analysis, Australian National University, Australia;1. Research Center for Applied Sciences, Academia Sinica, 11529, Taiwan;2. Department of Physics, National Cheng-Kung University, Tainan, 70101, Taiwan;3. King Abdulaziz University, Faculty of Science, Department of Physics, Jeddah, Saudi Arabia;4. Suez Canal University, Faculty of Science, Department of Physics, Ismailia, Egypt;1. Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 200030, Shanghai, China;2. Wuxi Mental Health Center, Nanjing Medical University, 214151, Wuxi, China |
| |
| Abstract: | Preconditioning techniques based on incomplete Cholesky factorization are very efficient in increasing the convergence rates of basic iterative methods. Complicated addressings and high demands for auxiliary storage, or increased factorization time, have reduced their appeal as general purpose preconditioners. In this study an elegant computational implementation is presented which succeeds in reducing both computing storage and factorization time. The proposed implementation is applied to two incomplete factorization schemes. The first is based on the rejection of certain terms according to their magnitude, while the second is based on a rejection criterion relative to the position of the zero terms of the coefficient matrix. Numerical results demonstrate the superiority of the proposed preconditioners over other types of preconditioning matrices, particularly for ill-conditioned problems. They also show their efficiency for large-scale problems in terms of computer storage and CPU time, over a direct solution method using the skyline storage scheme. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
