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Fast multilevel methods for Markov chains


Killian Miller, Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.



This paper describes multilevel methods for the calculation of the stationary probability vector of large, sparse, irreducible Markov chains. In particular, several recently proposed significant improvements to the multilevel aggregation method of Horton and Leutenegger are described and compared. Furthermore, we propose a very simple improvement of that method using an over-correction mechanism. We also compare with more traditional iterative methods for Markov chains such as weighted Jacobi, two-level aggregation/disaggregation, and preconditioned stabilized biconjugate gradient and generalized minimal residual method. Numerical experiments confirm that our improvements lead to significant speedup, and result in multilevel methods that are competitive with leading iterative solvers for Markov chains. Copyright © 2011 John Wiley & Sons, Ltd.