Simulated Annealing: Rigorous finite-time guarantees for optimization on continuous domains
Abstract
Simulated annealing is a popular method for approaching the solution of a global optimization problem. Existing results on its performance apply to discrete combinatorial optimization where the optimization variables can assume only a finite set of possible values. We introduce a new general formulation of simulated annealing which allows one to guarantee finite-time performance in the optimization of functions of continuous variables. The results hold universally for any optimization problem on a bounded domain and establish a connection between simulated annealing and up-to-date theory of convergence of Markov chain Monte Carlo methods on continuous domains. This work is inspired by the concept of finite-time learning with known accuracy and confidence developed in statistical learning theory.
Cite
@article{arxiv.0709.2989,
title = {Simulated Annealing: Rigorous finite-time guarantees for optimization on continuous domains},
author = {A. Lecchini-Visintini and J. Lygeros and J. Maciejowski},
journal= {arXiv preprint arXiv:0709.2989},
year = {2007}
}
Comments
10 pages, 2 figures. Preprint. The final version will appear in: Advances in Neural Information Processing Systems 20, Proceedings of NIPS 2007, MIT Press