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A Bootstrap Algebraic Multilevel method for Markov Chains

Authors
  • Bolten, M.
  • Brandt, A.
  • Brannick, J.
  • Frommer, A.
  • Kahl, K.
  • Livshits, I.
Type
Preprint
Publication Date
Apr 21, 2010
Submission Date
Apr 08, 2010
Identifiers
arXiv ID: 1004.1451
Source
arXiv
License
Yellow
External links

Abstract

This work concerns the development of an Algebraic Multilevel method for computing stationary vectors of Markov chains. We present an efficient Bootstrap Algebraic Multilevel method for this task. In our proposed approach, we employ a multilevel eigensolver, with interpolation built using ideas based on compatible relaxation, algebraic distances, and least squares fitting of test vectors. Our adaptive variational strategy for computation of the state vector of a given Markov chain is then a combination of this multilevel eigensolver and associated multilevel preconditioned GMRES iterations. We show that the Bootstrap AMG eigensolver by itself can efficiently compute accurate approximations to the state vector. An additional benefit of the Bootstrap approach is that it yields an accurate interpolation operator for many other eigenmodes. This in turn allows for the use of the resulting AMG hierarchy to accelerate the MLE steps using standard multigrid correction steps. The proposed approach is applied to a range of test problems, involving non-symmetric stochastic M-matrices, showing promising results for all problems considered.

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