On Kemeny's constant and stochastic complement

Given a stochastic matrix P partitioned in four blocks Pij, i,j=1,2, Kemeny's constant κ(P) is expressed in terms of Kemeny's constants of the stochastic complements P1=P11+P12(I−P22)−1P21, and P2=P22+P21(I−P11)−1P12. Specific cases concerning periodic Markov chains and Kronecker products of stochastic matrices are investigated. Bounds to Kemeny's constant of perturbed matrices are given. Relying on these theoretical results, a divide-and-conquer algorithm for the efficient computation of Kemeny's constant of graphs is designed. Numerical experiments performed on real world problems show the high efficiency and reliability of this algorithm.

Authors

Publication Details

Journal
Linear Algebra and its Applications
Published
2024-09-06
DOI
https://doi.org/10.1016/j.laa.2024.09.001
Citations
2
Primary Topic
Matrix Theory and Algorithms
Type
article
Field-Weighted Citation Impact
0.72

Funders

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article

On Kemeny's constant and stochastic complement

Beatrice Meini, Fabio Durastante, Dario A. Bini, Sooyeong Kim
2 citations
Linear Algebra and its Applications
Matrix Theory and Algorithms
0.72
article

On Kemeny's constant and stochastic complement

Beatrice Meini, Fabio Durastante, Dario A. Bini, Sooyeong Kim
article en
2 citations

Abstract

Given a stochastic matrix P partitioned in four blocks Pij, i,j=1,2, Kemeny's constant κ(P) is expressed in terms of Kemeny's constants of the stochastic complements P1=P11+P12(I−P22)−1P21, and P2=P22+P21(I−P11)−1P12. Specific cases concerning periodic Markov chains and Kronecker products of stochastic matrices are investigated. Bounds to Kemeny's constant of perturbed matrices are given. Relying on these theoretical results, a divide-and-conquer algorithm for the efficient computation of Kemeny's constant of graphs is designed. Numerical experiments performed on real world problems show the high efficiency and reliability of this algorithm.

Linear Algebra and its ApplicationsVol. 703
Istituto Nazionale di Alta Matematica "Francesco Severi", Fields Institute for Research in Mathematical Sciences, Gruppo Nazionale per il Calcolo Scientifico, Natural Sciences and Engineering Research Council of Canada
Openalex Percentile: Top 26%
Matrix Theory and Algorithms
0.72
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