Approximated tensor sum preconditioner for stochastic automata networks

Authors:
Abderezak Touzene
Affiliations:
Sultan Qaboos University, College of Science, Dept. of Computer Science, Oman
Venue:
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Year:
2006

Citing 5
Cited 0

Stochastic Automata Network of Modeling Parallel Systems

IEEE Transactions on Software Engineering
Efficient descriptor-vector multiplications in stochastic automata networks

Journal of the ACM (JACM)
On the stochastic structure of parallelism and synchronization models for distributed algorithms
Solution of the matrix equation AX + XB = C [F4]

Communications of the ACM
The ubiquitous Kronecker product

Journal of Computational and Applied Mathematics - Special issue on numerical analysis 2000 Vol. III: linear algebra

Quantified Score

Hi-index	0.00

Visualization

Abstract

Some iterative and projection methods for SAN have been tested with a modest success. Several preconditioners for SAN have been developed to speedup the convergence rate. Recently Langville and Stewart proposed the Nearest Kronecker Product (NKP) preconditioner for SAN with a great success. Encouraged by their work, we propose a new preconditioning method, called Approximated Tensor Sum Preconditioner (ATSP), which uses tensor sum preconditioner rather than Kronecker product preconditioner. In ATSP, we take into account the effect of the synchronizations using an approximation technique. Our preconditioner outperforms the NKP preconditioner for the tested SAN Model.