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dc.contributor.authorDayar, T.en_US
dc.contributor.authorNoyan, G. N.en_US
dc.date.accessioned2016-02-08T09:49:20Z
dc.date.available2016-02-08T09:49:20Z
dc.date.issued2011en_US
dc.identifier.issn1068-9613
dc.identifier.urihttp://hdl.handle.net/11693/21654
dc.description.abstractA Google-like matrix is a positive stochastic matrix given by a convex combination of a sparse, nonnegative matrix and a particular rank one matrix. Google itself uses the steady-state vector of a large matrix of this form to help order web pages in a search engine. We investigate the computation of the steady-state vectors of such matrices using block iterative methods. The block partitionings considered include those based on block triangular form and those having triangular diagonal blocks obtained using cutsets. Numerical results show that block Gauss-Seidel with partitionings based on block triangular form is most often the best approach. However, there are cases in which a block partitioning with triangular diagonal blocks is better, and the Gauss-Seidel method is usually competitive. Copyright © 2011, Kent State University.en_US
dc.language.isoEnglishen_US
dc.source.titleElectronic Transactions on Numerical Analysisen_US
dc.subjectBlock iterative methodsen_US
dc.subjectCutsetsen_US
dc.subjectGoogleen_US
dc.subjectPageRanken_US
dc.subjectPartitioningsen_US
dc.subjectPower methoden_US
dc.subjectStochastic matricesen_US
dc.subjectTriangular blocksen_US
dc.subjectBlock iterative methoden_US
dc.subjectCutsetsen_US
dc.titleSteady-state analysis of google-like stochastic matrices with block iterative methodsen_US
dc.typeArticleen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.citation.spage69en_US
dc.citation.epage97en_US
dc.citation.volumeNumber38en_US
dc.publisherKent State Universityen_US


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