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      Iterative methods based on splittings for stochastic automata networks

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      Author
      Uysal, E.
      Dayar T.
      Date
      1998
      Source Title
      European Journal of Operational Research
      Print ISSN
      0377-2217
      Electronic ISSN
      1872-6860
      Volume
      110
      Issue
      1
      Pages
      166 - 186
      Language
      English
      Type
      Article
      Item Usage Stats
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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 Methods
      Markov Processes
      Splittings
      Stochastic Automata Networks
      Tensor Algebra
      Automata Theory
      Computer Networks
      Computer Workstations
      Iterative Methods
      Mathematical Models
      Problem Solving
      Tensors
      Parallel Processing Systems
      Permalink
      http://hdl.handle.net/11693/25406
      Published Version (Please cite this version)
      https://doi.org/10.1016/S0377-2217(97)00215-4
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      • Department of Computer Engineering 1368
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