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dc.contributor.authorBuchholz, P.en_US
dc.contributor.authorDayar T.en_US
dc.date.accessioned2016-02-08T10:25:30Z
dc.date.available2016-02-08T10:25:30Zen_US
dc.date.issued2004en_US
dc.identifier.issn0010-485X
dc.identifier.issn1436-5057
dc.identifier.urihttp://hdl.handle.net/11693/24195en_US
dc.description.abstractThe paper presents a class of numerical methods to compute the stationary distribution of Markov chains (MCs) with large and structured state spaces. A popular way of dealing with large state spaces in Markovian modeling and analysis is to employ Kronecker-based representations for the generator matrix and to exploit this matrix structure in numerical analysis methods. This paper presents various multilevel (ML) methods for a broad class of MCs with a hierarchcial Kronecker structure of the generator matrix. The particular ML methods are inspired by multigrid and aggregation-disaggregation techniques, and differ among each other by the type of multigrid cycle, the type of smoother, and the order of component aggregation they use. Numerical experiments demonstrate that so far ML methods with successive over-relaxation as smoother provide the most effective solvers for considerably large Markov chains modeled as HMMs with multiple macrostates.en_US
dc.language.isoEnglishen_US
dc.source.titleComputingen_US
dc.relation.isversionofhttp://dx.doi.org/10.1007/s00607-004-0074-2en_US
dc.subjectAggregation-Disaggregationen_US
dc.subjectKronecker-Based Numerical Techniquesen_US
dc.subjectMarkov Chainsen_US
dc.subjectMultigriden_US
dc.subjectMultilevel Methodsen_US
dc.subjectAggregation-Disaggregationen_US
dc.subjectKronecker-Based Numerical Techniquesen_US
dc.subjectMultigriden_US
dc.subjectMultilevel Methodsen_US
dc.subjectComputational Methodsen_US
dc.subjectMathematical Modelsen_US
dc.subjectMatrix Algebraen_US
dc.subjectNumerical Methodsen_US
dc.subjectPetri Netsen_US
dc.subjectProblem Solvingen_US
dc.subjectQueueing Networksen_US
dc.subjectVectorsen_US
dc.subjectMarkov Processesen_US
dc.titleComparison of multilevel methods for kronecker-based Markovian representationsen_US
dc.typeArticleen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.citation.spage349en_US
dc.citation.epage371en_US
dc.citation.volumeNumber73en_US
dc.citation.issueNumber4en_US
dc.identifier.doi10.1007/s00607-004-0074-2en_US
dc.publisherSpringeren_US


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