Technical Note—Accelerated Computation of the Expected Discounted Return in a Markov Chain

Evan L. Porteus and John C. Totten
Additional contact information
Evan L. Porteus: Stanford University, Stanford, California
John C. Totten: Procter and Gamble Company, Cincinnati, Ohio

Operations Research, 1978, vol. 26, issue 2, 350-358

Abstract: This note investigates the use of extrapolations with certain iterative methods to accelerate the computation of the expected discounted return in a finite Markov chain. An easily administered algorithm for reordering the equations allows an attractive stopping rule to be used with Gauss-Seidel iteration. Lower bound and norm reducing extrapolations are introduced. Certain of these extrapolations that are optimal are easily computed. From the results of a small numerical example, it appears that the effects of reordering can be dramatic. Some form of extrapolation with Gauss-Seidel iteration after reordering may turn out to be more efficient in practice than successive over-relaxation.

Date: 1978
References: Add references at CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1287/opre.26.2.350 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:26:y:1978:i:2:p:350-358

Access Statistics for this article

More articles in Operations Research from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().