Abstract
In this short paper we show how the convergence of the iterative aggregation-disaggregation methods for computing the Perron eigenvector of a large sparse irreducible stochastic matrix can be improved by an appropriate ordering of the data and by the choice of a basic iteration matrix. Some theoretical estimates are introduced and a fast algorithm is proposed for obtaining the desired ordering. Numerical examples are presented.
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Pultarová, I. (2009). Ordering of Matrices for Iterative Aggregation - Disaggregation Methods. In: Bru, R., Romero-Vivó, S. (eds) Positive Systems. Lecture Notes in Control and Information Sciences, vol 389. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02894-6_37
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DOI: https://doi.org/10.1007/978-3-642-02894-6_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02893-9
Online ISBN: 978-3-642-02894-6
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