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dc.contributor.authorGusak, O.en_US
dc.contributor.authorDayar, T.en_US
dc.contributor.authorFourneau, J. M.en_US
dc.date.accessioned2015-07-28T11:57:16Z
dc.date.available2015-07-28T11:57:16Zen_US
dc.date.issued2003-07-16en_US
dc.identifier.issn0377-2217
dc.identifier.issn1872-6860
dc.identifier.urihttp://hdl.handle.net/11693/11258en_US
dc.description.abstractThe 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.en_US
dc.language.isoEnglishen_US
dc.source.titleEuropean Journal of Operational Researchen_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/S0377-2217(02)00431-9en_US
dc.subjectMarkov Processesen_US
dc.subjectStochastic Automata Networksen_US
dc.subjectOrdinary Lumpabilityen_US
dc.subjectAggregation With Iterative Disaggregationen_US
dc.titleLumpable continuous-time stochastic automata networksen_US
dc.typeArticleen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.citation.spage436en_US
dc.citation.epage451en_US
dc.citation.volumeNumber148en_US
dc.citation.issueNumber2en_US
dc.identifier.doi10.1016/S0377-2217(02)00431-9en_US
dc.publisherElsevieren_US


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