Incomplete factorization of singular M-matrices
SIAM Journal on Algebraic and Discrete Methods
SIAM Journal on Scientific and Statistical Computing
Iterative solution methods
A Sparse Approximate Inverse Preconditioner for the Conjugate Gradient Method
SIAM Journal on Scientific Computing
Parallel Preconditioning with Sparse Approximate Inverses
SIAM Journal on Scientific Computing
A Sparse Approximate Inverse Preconditioner for Nonsymmetric Linear Systems
SIAM Journal on Scientific Computing
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
A comparative study of sparse approximate inverse preconditioners
IMACS'97 Proceedings on the on Iterative methods and preconditioners
Matrix analysis and applied linear algebra
Matrix analysis and applied linear algebra
Comparison of Partitioning Techniques for Two-Level Iterative Solvers on Large, Sparse Markov Chains
SIAM Journal on Scientific Computing
A Priori Sparsity Patterns for Parallel Sparse Approximate Inverse Preconditioners
SIAM Journal on Scientific Computing
Preconditioning techniques for large linear systems: a survey
Journal of Computational Physics
Journal of Parallel and Distributed Computing
The Journal of Supercomputing - Special issue: Parallel and distributed processing and applications
A parallel priority queueing system with finite buffers
Journal of Parallel and Distributed Computing
International Journal of Computer Mathematics
Applied Numerical Mathematics
A Block FSAI-ILU Parallel Preconditioner for Symmetric Positive Definite Linear Systems
SIAM Journal on Scientific Computing
A new parallel block aggregated algorithm for solving Markov chains
The Journal of Supercomputing
A generalized Block FSAI preconditioner for nonsymmetric linear systems
Journal of Computational and Applied Mathematics
A new parallel algorithm for solving large-scale Markov chains
The Journal of Supercomputing
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We consider the parallel computation of the stationary probability distribution vector of ergodic Markov chains with large state spaces by preconditioned Krylov subspace methods. The parallel preconditioner is obtained as an explicit approximation, in factorized form, of a particular generalized inverse of the generator matrix of the Markov process. Graph partitioning is used to parallelize the whole algorithm, resulting in a two-level method.Conditions that guarantee the existence of the preconditioner are given, and the results of a parallel implementation are presented. Our results indicate that this method is well suited for problems in which the generator matrix can be explicitly formed and stored.