Multigrid Treatment and Robustness Enhancement for Factored Sparse Approximate Inverse Preconditioning
نویسنده
چکیده
We investigate the use of sparse approximate inverse techniques (SAI) in a grid based multilevel ILU preconditioner (GILUM) to design a robust parallelizable precon-ditioner for solving general sparse matrix. Taking the advantages of grid based mul-tilevel methods, the resulting preconditioner outperforms sparse approximate inverse in robustness and eeciency. Conversely, taking the advantages of sparse approximate inverse, it aaords an easy and convenient way to introduce parallelism within multi-level structure. Moreover, an independent set search strategy with automatic diagonal thresholding and a relative threshold dropping strategy are proposed to improve pre-conditioner performance. Numerical experiments are used to show the eeectiveness and eeciency of the proposed preconditioner, and to compare it with some single and multilevel preconditioners.
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