We propose two sparsity pattern selection algorithms for factored approximate inverse preconditioners to solve general sparse matrices. The sparsity pattern is adaptively updated in the construction phase by using combined information of the inverse and original triangular factors of the original matrix. In order to determine the sparsity pattern, our first algorithm uses the norm of the inverse factors multiplied by the largest absolute value of the original factors, and the second employs the norm of the inverse factors divided by the norm of the original factors. Experimental results show that these algorithms improve the robustness of the preconditioners to solve general sparse matrices.
|Number of pages||8|
|Journal||Computers and Mathematics with Applications|
|State||Published - Jul 2011|
Bibliographical noteFunding Information:
The first author’s research work was supported in part by the Keystone Innovation Starter Kit Grant Project under grant contract number C000032549 . The second’s research work was supported in part by the US National Science Foundation under grant CCF-0527967 , in part by the National Institutes of Health under grant 1R01HL086644-01 , in part by the Kentucky Science and Engineering Foundation under grant KSEF-148-502-06-186 , and in part by the Alzheimer’s Association under Grant NIGR-06-25460 .
- Approximate inverse
- Sparsity pattern
ASJC Scopus subject areas
- Modeling and Simulation
- Computational Theory and Mathematics
- Computational Mathematics