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Simple conditions for the convergence of simulated annealing type algorithms

Part of: Probabilistic methods, simulation and stochastic differential equations Stochastic processes Markov processes

Published online by Cambridge University Press:  14 July 2016

J. R. Cruz  and
C. C. Y. Dorea
J. R. Cruz*
Affiliation:
Universidade de Brasília
C. C. Y. Dorea*
Affiliation:
Universidade de Brasília
*
∗Postal address: C.P. 04322, Universidade de Brasília, 70919–970, Brasília-DF, Brazil.
∗Postal address: C.P. 04322, Universidade de Brasília, 70919–970, Brasília-DF, Brazil.
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Abstract

We study a class of simulated annealing type algorithms for global minimization with general acceptance probabilities. This paper presents simple conditions, easy to verify in practice, which ensure the convergence of the algorithm to the global minimum with probability 1.

Keywords

Simulated annealing algorithmnon-homogeneous Markov chainstationary distributionergodicity

MSC classification

Primary: 65K99: None of the above, but in this section 60G70: Extreme value theory; extremal processes
Secondary: 60G40: Stopping times; optimal stopping problems; gambling theory 60J05: Discrete-time Markov processes on general state spaces 65C05: Monte Carlo methods
Type
Research Papers
Information
Journal of Applied Probability , Volume 35 , Issue 4 , December 1998 , pp. 885 - 892
Copyright
Copyright © Applied Probability Trust 1998 

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Footnotes

Partially supported by CNPq-Brasil.

References

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