DocumentCode :
1129339
Title :
The Gauss-Seidel numerical procedure for Markov stochastic games
Author :
Kushner, Harold J.
Author_Institution :
Appl. Math. Dept., Brown Univ., Providence, RI, USA
Volume :
49
Issue :
10
fYear :
2004
Firstpage :
1779
Lastpage :
1784
Abstract :
Consider the problem of value iteration for solving Markov stochastic games. One simply iterates backward, via a Jacobi-like procedure. The convergence of the Gauss-Seidel form of this procedure is shown for both the discounted and ergodic cost problems, under appropriate conditions, with extensions to problems where one stops when a boundary is hit or if any one of the players chooses to stop, with associated costs. Generally, the Gauss-Seidel procedure accelerates convergence.
Keywords :
Jacobian matrices; Markov processes; convergence of numerical methods; iterative methods; Gauss Seidel numerical procedure; Jacobi procedure; Markov stochastic game; convergence; Acceleration; Convergence; Cost function; Gaussian processes; Jacobian matrices; State feedback; Stochastic processes; Gauss–Seidel procedure; Markov games; numerical algorithms; stochastic games;
fLanguage :
English
Journal_Title :
Automatic Control, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9286
Type :
jour
DOI :
10.1109/TAC.2004.835402
Filename :
1341576
Link To Document :
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