Approximate inverse preconditionings for sparse linear systems. (English) Zbl 0762.65025

Authors’ summary: We discuss a procedure for the adaptive construction of sparse approximate inverse preconditionings for general sparse linear systems. The approximate inverses are based on minimizing a consistent norm of the difference between the identity and the preconditioned matrix. The analysis provides positive definiteness and condition number estimates for the preconditioned system under certain circumstances.
We show that for the 1-norm, restricting the size of the difference matrix below 1 may require dense approximate inverses. However, this requirement does not hold for the 2-norm, and similarly reducing the Frobenius norm below 1 does not generally require that much fill-in. Moreover, for the Frobenius norm, the calculation of the approximate inverses yields naturally column-oriented parallelism.
General sparsity can be exploited in a straightforward fashion. Numerical criteria are considered for determining which columns of the sparse approximate inverse require additional fill-in. Sparse algorithms are discussed for the location of potential fill-in within each column. Results using a minimum-residual-type iterative method are presented to illustrate the potential of the method.
Reviewer: J.Mandel (Denver)


65F35 Numerical computation of matrix norms, conditioning, scaling
65F05 Direct numerical methods for linear systems and matrix inversion
65F50 Computational methods for sparse matrices
65F10 Iterative numerical methods for linear systems
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