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On the utility of the multi-level algorithm for the solution of nearly completely decomposable Markov chains

机译:多级算法在求解几乎完全可分解的马尔可夫链中的应用

摘要

Recently the Multi-Level algorithm was introduced as a general purpose solver for the solution of steady state Markov chains. In this paper, we consider the performance of the Multi-Level algorithm for solving Nearly Completely Decomposable (NCD) Markov chains, for which special-purpose iteractive aggregation/disaggregation algorithms such as the Koury-McAllister-Stewart (KMS) method have been developed that can exploit the decomposability of the the Markov chain. We present experimental results indicating that the general-purpose Multi-Level algorithm is competitive, and can be significantly faster than the special-purpose KMS algorithm when Gauss-Seidel and Gaussian Elimination are used for solving the individual blocks.
机译:最近,引入了多级算法作为稳态马尔可夫链求解的通用求解器。在本文中,我们考虑了求解几乎完全可分解(NCD)马尔可夫链的多级算法的性能,为此针对该算法开发了专用的迭代聚合/分解算法,例如Koury-McAllister-Stewart(KMS)方法可以利用马尔可夫链的可分解性。我们提供的实验结果表明,当使用高斯-赛德尔和高斯消元法求解单个块时,通用多级算法具有竞争力,并且可以比专用KMS算法快得多。

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