GMRES: a generalized minimal residual algorithm for solving nonsymmetric linear systems
SIAM Journal on Scientific and Statistical Computing
Efficient parallel solution of linear systems
STOC '85 Proceedings of the seventeenth annual ACM symposium on Theory of computing
Lanczos methods for the solution of nonsymmetric systems of linear equations
SIAM Journal on Matrix Analysis and Applications
Parallel Preconditioning with Sparse Approximate Inverses
SIAM Journal on Scientific Computing
Approximate Inverse Preconditioners via Sparse-Sparse Iterations
SIAM Journal on Scientific Computing
Parallelization of ILU Decomposition for Elliptic Boundary Value Problem of the PDE on AP3000
ISHPC '99 Proceedings of the Second International Symposium on High Performance Computing
Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
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The minimal residual (MR) algorithm for computing a sparse approximate inverse M of the coefficient matrix A is investigated. It is very useful for deriving a precise preconditioner in restarted GMRES(m) algorithm, mainly in a parallel environment. Numerical experiments are reported to show convergence behavior of the GMRES(m) algorithm with MR preconditioning on test problems by using the shared memory parallel machine Origin 2400.