2000
DOI: 10.1016/s0166-5316(99)00060-7
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Numerical iterative methods for Markovian dependability and performability models: new results and a comparison

Abstract: In this paper we deal with iterative numerical methods to solve linear systems arising in continuous-time Markov chain (CTMC) models. We develop an algorithm to dynamically tune the relaxation parameter of the successive over-relaxation method. We give a sufficient condition for the Gauss-Seidel method to converge when computing the steady-state probability vector of a finite irreducible CTMC, an a suffient condition for the Generalized Minimal Residual projection method not to converge to the trivial solution… Show more

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Cited by 3 publications
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