A new iterative method for solving large-scale Markov chains

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

In this paper, we propose a new iterative method for solving Large-Scale Markov chains. This method combines some of the well known techniques such as aggregation, Gauss-Seidel effect and overrelaxation. Our aim is to take advantage of those techniques for accelerating the convergence rate.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages180-193
Number of pages14
Volume977
ISBN (Print)9783540603009
Publication statusPublished - 1995
Event8th International Conference on Modelling Techniques and Tools for Computer performance Evaluation, Performance Tools 1995 and 8th GI/ITG Conference on Measuring, Modelling and Evaluating Computing and Communication Systems, MMB 1995 - Heidelberg, Germany
Duration: Sep 20 1995Sep 22 1995

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume977
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other8th International Conference on Modelling Techniques and Tools for Computer performance Evaluation, Performance Tools 1995 and 8th GI/ITG Conference on Measuring, Modelling and Evaluating Computing and Communication Systems, MMB 1995
CountryGermany
CityHeidelberg
Period9/20/959/22/95

Fingerprint

Iterative methods
Markov processes
Markov chain
Agglomeration
Iteration
Gauss-Seidel
Convergence Rate
Aggregation

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Touzene, A. (1995). A new iterative method for solving large-scale Markov chains. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 977, pp. 180-193). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 977). Springer Verlag.

A new iterative method for solving large-scale Markov chains. / Touzene, Abderezak.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 977 Springer Verlag, 1995. p. 180-193 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 977).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Touzene, A 1995, A new iterative method for solving large-scale Markov chains. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 977, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 977, Springer Verlag, pp. 180-193, 8th International Conference on Modelling Techniques and Tools for Computer performance Evaluation, Performance Tools 1995 and 8th GI/ITG Conference on Measuring, Modelling and Evaluating Computing and Communication Systems, MMB 1995, Heidelberg, Germany, 9/20/95.
Touzene A. A new iterative method for solving large-scale Markov chains. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 977. Springer Verlag. 1995. p. 180-193. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Touzene, Abderezak. / A new iterative method for solving large-scale Markov chains. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 977 Springer Verlag, 1995. pp. 180-193 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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