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Volume 2, Issue 1
The Finest Level Acceleration of Multilevel Aggregation for Markov Chains

C. Wen & T.-Z. Huang

Int. J. Numer. Anal. Mod. B, 2 (2011), pp. 27-41

Published online: 2011-02

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  • 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 .
  • Keywords

Markov chains multilevel aggregation acceleration vector extrapolation methods

  • AMS Subject Headings

65C40 60J22

  • Copyright

COPYRIGHT: © Global Science Press

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@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} }
TY - JOUR T1 - The Finest Level Acceleration of Multilevel Aggregation for Markov Chains AU - C. Wen & T.-Z. Huang JO - International Journal of Numerical Analysis Modeling Series B VL - 1 SP - 27 EP - 41 PY - 2011 DA - 2011/02 SN - 2 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/ijnamb/297.html KW - Markov chains KW - multilevel aggregation KW - acceleration KW - vector extrapolation methods AB - 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 .
C. Wen and T.-Z. Huang. (2011). The Finest Level Acceleration of Multilevel Aggregation for Markov Chains. International Journal of Numerical Analysis Modeling Series B. 2 (1). 27-41. doi:
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