On the convergence of Jacobi and Gauss-Seidel iteration for steady-state probabilities of finite-state continuous time Markov chains

On the convergence of Jacobi and Gauss-Seidel iteration for steady-state probabilities of finite-state continuous time Markov chains

0.00 Avg rating—0 Votes
Article ID: iaor1992278
Country: United States
Volume: 7
Start Page Number: 185
End Page Number: 189
Publication Date: Aug 1991
Journal: Stochastic Models
Authors: ,
Abstract:

The authors consider two nonsingular versions of the problem described in the title. For one of these versions, they show by example that neither Jacobi nor Gauss-Seidel iteraton is guaranteed to converge; for the other version, the authors outline a proof that both methods are guaranteed to converge.

Reviews

Required fields are marked *. Your email address will not be published.