Efficient Algorithm for Computing the Ergodic Projector of Markov Multi-chains

J. Berkhout; B.F. Heidergott

doi:10.1016/j.procs.2015.05.403

Efficient Algorithm for Computing the Ergodic Projector of Markov Multi-chains

Amsterdam Business Research Institute

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Abstract

This paper extends the Markov uni-chain series expansion theory to Markov multi-chains, i.e., to Markov chains having multiple ergodic classes and possible transient states. The introduced series expansion approximation (SEA) provides a controllable approximation for Markov multichain ergodic projectors which may be a useful tool in large-scale network analysis. As we will illustrate by means of numerical examples, the new algorithm is faster than the power algorithm for large networks.

Original language	English
Pages (from-to)	1818-1827
Journal	Procedia Computer Science
Volume	51
DOIs	https://doi.org/10.1016/j.procs.2015.05.403
Publication status	Published - 2015
Event	International Conference On Computational Science - New York Duration: 1 Jan 2015 → 1 Jan 2015

Bibliographical note

Proceedings title: International Conference On Computational Science, ICCS 2015 — Computational Science at the Gates of Nature
Publisher: Elsevier
Place of publication: New York

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SDG 14 Life Below Water

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abstract = "This paper extends the Markov uni-chain series expansion theory to Markov multi-chains, i.e., to Markov chains having multiple ergodic classes and possible transient states. The introduced series expansion approximation (SEA) provides a controllable approximation for Markov multichain ergodic projectors which may be a useful tool in large-scale network analysis. As we will illustrate by means of numerical examples, the new algorithm is faster than the power algorithm for large networks.",

author = "J. Berkhout and B.F. Heidergott",

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PY - 2015

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N2 - This paper extends the Markov uni-chain series expansion theory to Markov multi-chains, i.e., to Markov chains having multiple ergodic classes and possible transient states. The introduced series expansion approximation (SEA) provides a controllable approximation for Markov multichain ergodic projectors which may be a useful tool in large-scale network analysis. As we will illustrate by means of numerical examples, the new algorithm is faster than the power algorithm for large networks.

AB - This paper extends the Markov uni-chain series expansion theory to Markov multi-chains, i.e., to Markov chains having multiple ergodic classes and possible transient states. The introduced series expansion approximation (SEA) provides a controllable approximation for Markov multichain ergodic projectors which may be a useful tool in large-scale network analysis. As we will illustrate by means of numerical examples, the new algorithm is faster than the power algorithm for large networks.

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DO - 10.1016/j.procs.2015.05.403

M3 - Article

SN - 1877-0509

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JO - Procedia Computer Science

JF - Procedia Computer Science

T2 - International Conference On Computational Science

Y2 - 1 January 2015 through 1 January 2015

ER -

Efficient Algorithm for Computing the Ergodic Projector of Markov Multi-chains

Abstract

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