Stable computation with the fundamental matrix of a Markov chain

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

The short term behavior of a Markov chain can be inferred from its fundamental matrix F. One method of computing the parts of F that are needed is to compute Fy for a given vector y. It is shown that all forward stable algorithms that solve a particular least squares problem lead to forward stable algorithms for computing Fy. This in turn leads to a class of algorithms that compute Fy accurately whenever the underlying problem is well-conditioned. One algorithm from this class is based upon the Grassman-Taksar-Heyman variant of Gaussian elimination. Other such algorithms include one based upon orthogonal factorization and one based upon the conjugate gradient least squares algorithm.

Original languageEnglish (US)
Pages (from-to)230-241
Number of pages12
JournalSIAM Journal on Matrix Analysis and Applications
Volume22
Issue number1
DOIs
StatePublished - Jan 1 2000

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Fundamental Matrix
Markov chain
Orthogonal Factorization
Gaussian elimination
Computing
Conjugate Gradient
Gradient Algorithm
Least Square Algorithm
Least Squares Problem
Term

All Science Journal Classification (ASJC) codes

  • Analysis

Cite this

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Stable computation with the fundamental matrix of a Markov chain. / Barlow, Jesse L.

In: SIAM Journal on Matrix Analysis and Applications, Vol. 22, No. 1, 01.01.2000, p. 230-241.

Research output: Contribution to journalArticle

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