@Article{IJNAMB-2-27,
author = {C. Wen and T.-Z. Huang},
title = { The Finest Level Acceleration of Multilevel Aggregation for Markov Chains},
journal = {International Journal of Numerical Analysis Modeling Series B},
year = {2011},
volume = {2},
number = {1},
pages = {27--41},
abstract = {In this paper, we consider a class of new accelerated multilevel aggregation methods using two polynomial-type vector extrapolation methods, namely the reduced rank extrapolation
(RRE) and the generalization of quadratic extrapolation (GQE) methods. We show how to
combine the multilevel aggregation methods with the RRE and GQE algorithms on the finest
level in order to speed up the numerical computation of the stationary probability vector for an
irreducible Markov chain. Numerical experiments on typical Markov chain problems are reported
to illustrate the efficiency of the accelerated multilevel aggregation methods
.},
issn = {},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/ijnamb/297.html}
}