Optimized sparse approximate inverse smoothers for solving Laplacian linear systems

Parallel Smoothers Using Sparse Approximate Inverse

Sparse approximate inverses' usefulness in a parallel environment has motivated much interest in recent years. However, the superior capability of an approximate inverse in eliminating the local error has not yet been fully exploited in multi-grid algorithms. We propose a new class of sparse approximate inverse smoothers in this paper and present their analytic smoothing factors for constant co...

Sparse Approximate Inverse Smoothers For Geometric and Algebraic Multigrid

Sparse Approximate Inverse Smoothers For Geometric and Algebraic Multigrid

Block Approximate Inverse Preconditioners for Sparse Nonsymmetric Linear Systems

Abstract. In this paper block approximate inverse preconditioners to solve sparse nonsymmetric linear systems with iterative Krylov subspace methods are studied. The computation of the preconditioners involves consecutive updates of variable rank of an initial and nonsingular matrix A0 and the application of the Sherman-MorrisonWoodbury formula to compute an approximate inverse decomposition of...

A Sparse Approximate Inverse Preconditioner for Nonsymmetric Linear Systems

This paper is concerned with a new approach to preconditioning for large sparse linear systems A procedure for computing an incomplete factorization of the inverse of a nonsymmetric matrix is developed and the resulting factorized sparse approximate inverse is used as an explicit preconditioner for conjugate gradient type methods Some theoretical properties of the preconditioner are discussed a...