Ergodicity in Parametric Nonstationary Markov Chains: An Application to Simulated Annealing Methods

Shoshana Anily and Awi Federgruen
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Shoshana Anily: University of British Columbia, Vancouver, British Columbia
Awi Federgruen: Columbia University, New York, New York

Operations Research, 1987, vol. 35, issue 6, 867-874

Abstract: A nonstationary Markov chain is weakly ergodic if the dependence of the state distribution on the starting state vanishes as time tends to infinity. A chain is strongly ergodic if it is weakly ergodic and converges in distribution. In this paper we show that the two ergodicity concepts are equivalent for finite chains under rather general (and widely verifiable) conditions. We discuss applications to probabilistic analyses of general search methods for combinatorial optimization problems (simulated annealing).

Keywords: 568 nonstationary Markov chains; weak and strong ergodicity; applications to simulated annealing methods (search for similar items in EconPapers)
Date: 1987
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