Numerical iterative methods for Markovian dependability and performability models: new results and a comparison
Visualitza/Obre
Estadístiques de LA Referencia / Recolecta
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/21068
Tipus de documentArticle
Data publicació2000-02
Condicions d'accésAccés obert
Llevat que s'hi indiqui el contrari, els
continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
:
Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
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 0 when computing that vector. Finally, we compare several splitting-based iterative methods an a variant of the Generalized Minimal Residual projection method.
CitacióSuñe, V.; Domingo, J.; Carrasco, J. Numerical iterative methods for Markovian dependability and performability models: new results and a comparison. "Performance evaluation", Febrer 2000, vol. 39, núm. 1-4, p. 99-125.
ISSN0166-5316
Fitxers | Descripció | Mida | Format | Visualitza |
---|---|---|---|---|
PEVA_00.pdf | 238,4Kb | Visualitza/Obre |