The Gauss-Seidel Numerical Procedure for Markov Stochastic Games

Abstract

Consider the problem of value iteration for solving Markov stochastic games. One simply iterates backwards, 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.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2004
Accession Number
ADA460599

Entities

People

  • Harold J. Kushner

Organizations

  • Brown University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Applied Mathematics
  • Boundaries
  • Computational Science
  • Computations
  • Convergence
  • Diffusion
  • Equations
  • Inequalities
  • Information Operations
  • Intact Stability
  • Iterations
  • Markov Chains
  • Mathematics
  • Probability
  • Random Variables
  • Transitions

Fields of Study

  • Mathematics

Readers

  • Mathematical Modeling and Probability Theory.
  • Operations Research