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https://dx.doi.org/10.48550/ar...
Article . 2018
License: arXiv Non-Exclusive Distribution
Data sources: Datacite
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Linear Least Square Method for the Computation of the Mean First Passage Times of Ergodic Markov Chains

Authors: Chen, Yaming;

Linear Least Square Method for the Computation of the Mean First Passage Times of Ergodic Markov Chains

Abstract

An efficient and accurate iterative scheme for the computation of the mean first passage times (MFPTs) of ergodic Markov chains has been presented. Firstly, the computation problem of MFPTs is transformed into a set of linear equations. It has been proven that each of these equations is compatible and their minimal norm solutions constitute MFPTs. A new presentation of the MFPTs is also derived. Using linear least square algorithms, some numerical examples compared with the finite algorithm of Hunter [LAA, 549(2018)100-122] and iterative algorithm of J.Xu [AMC, 250(2025)372-389] are given. These results show that the new algorithm is suitable for large sparse systems.

9 pages with 5 figures

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Keywords

FOS: Mathematics, Mathematics - Numerical Analysis, Numerical Analysis (math.NA)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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