Iterative methods based on splittings for stochastic automata networks
Author
Uysal, E.
Dayar T.
Date
1998Source Title
European Journal of Operational Research
Print ISSN
0377-2217
Electronic ISSN
1872-6860
Volume
110
Issue
1
Pages
166 - 186
Language
English
Type
ArticleItem Usage Stats
118
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86
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Abstract
This paper presents iterative methods based on splittings (Jacobi, Gauss-Seidel, Successive Over Relaxation) and their block versions for Stochastic Automata Networks (SANs). These methods prove to be better than the power method that has been used to solve SANs until recently. With the help of three examples we show that the time it takes to solve a system modeled as a SAN is still substantial and it does not seem to be possible to solve systems with tens of millions of states on standard desktop workstations with the current state of technology. However, the SAN methodology enables one to solve much larger models than those could be solved by explicitly storing the global generator in the core of a target architecture especially if the generator is reasonably dense. © 1998 Elsevier Science B.V. All rights reserved.
Keywords
Block MethodsMarkov Processes
Splittings
Stochastic Automata Networks
Tensor Algebra
Automata Theory
Computer Networks
Computer Workstations
Iterative Methods
Mathematical Models
Problem Solving
Tensors
Parallel Processing Systems