Browsing by Subject "Ordinary Lumpability"

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    Iterative disaggregation for a class of lumpable discrete-time stochastic automata networks
    (Elsevier, 2003) Gusak, O.; Dayar T.; Fourneau, J. M.
    Stochastic automata networks (SANs) have been developed and used in the last 15 years as a modeling formalism for large systems that can be decomposed into loosely connected components. In this work, we concentrate on the not so much emphasized discrete-time SANs. First, we remodel and extend an SAN that arises in wireless communications. Second, for an SAN with functional transitions, we derive conditions for a special case of ordinary lumpability in which aggregation is done automaton by automaton. Finally, for this class of lumpable discrete-time SANs we devise an efficient aggregation-iterative disaggregation algorithm and demonstrate its performance on the SAN model of interest. © 2002 Elsevier Science B.V. All rights reserved.
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    Lumpable continuous-time stochastic automata networks
    (Elsevier, 2003-07-16) Gusak, O.; Dayar, T.; Fourneau, J. M.
    The generator matrix of a continuous-time stochastic automata network (SAN) is a sum of tensor products of smaller matrices, which may have entries that are functions of the global state space. This paper specifies easy to check conditions for a class of ordinarily lumpable partitionings of the generator of a continuous-time SAN in which aggregation is performed automaton by automaton. When there exists a lumpable partitioning induced by the tensor representation of the generator, it is shown that an efficient aggregation-iterative disaggregation algorithm may be employed to compute the steady-state distribution. The results of experiments with two SAN models show that the proposed algorithm performs better than the highly competitive block Gauss-Seidel in terms of both the number of iterations and the time to converge to the solution. © 2002 Elsevier Science B.V. All rights reserved.