Simulated annealing: a proof of convergence
- 1 June 1994
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 16 (6) , 652-656
- https://doi.org/10.1109/34.295910
Abstract
We prove the convergence of the simulated annealing procedure when the decision to change the current configuration is blind of the cost of the new configuration. In case of filtering binary images, the proof easily generalizes to other procedures, including that of Metropolis. We show that a function Q associated with the algorithm must be chosen as large as possible to provide a fast rate of convergence. The worst case (Q constant) is associated with the "blind" algorithm. On the other hand, an appropriate Q taking sufficiently high values yields a better rate of convergence than that of Metropolis procedure.Keywords
This publication has 10 references indexed in Scilit:
- A Bayesian filter for a mixture model of noise in image remote sensingComputational Statistics & Data Analysis, 1993
- A new modelisation of noise in image remote sensingStatistics & Probability Letters, 1992
- Simultaneous parameter estimation and segmentation of Gibbs random fields using simulated annealingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1989
- Cooling Schedules for Optimal AnnealingMathematics of Operations Research, 1988
- Simulated Annealing: Theory and ApplicationsPublished by Springer Nature ,1987
- Analysis of simulated annealing for optimizationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1985
- Nonstationary Markov chains and convergence of the annealing algorithmJournal of Statistical Physics, 1985
- Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of ImagesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1984
- Markov Random Field Texture ModelsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1983
- Monte Carlo MethodsPublished by Springer Nature ,1964