Relaxed fixed point iterations for matrix equations arising in Markov chain modeling

DOI10.1007/S11075-023-01496-YzbMATH Open1520.65024arXiv2205.13187OpenAlexW4317653927MaRDI QIDQ6133893

L. Gemignani, B. Meini

Publication date: 21 August 2023

Published in: Numerical Algorithms (Search for Journal in Brave)

Abstract: We present some accelerated variants of fixed point iterations for computing the minimal non-negative solution of the unilateral matrix equation associated with an M/G/1-type Markov chain. These variants derive from certain staircase regular splittings of the block Hessenberg M-matrix associated with the Markov chain. By exploiting the staircase profile we introduce a two-step fixed point iteration. The iteration can be further accelerated by computing a weighted average between the approximations obtained at two consecutive steps. The convergence of the basic two-step fixed point iteration and of its relaxed modification is proved. Our theoretical analysis, along with several numerical experiments show that the proposed variants generally outperform the classical iterations.

Full work available at URL: https://arxiv.org/abs/2205.13187

zbMATH Keywords

Markov chainnonlinear matrix equationM-matrixHessenberg matrixstaircase splitting

Mathematics Subject Classification ID

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