Lecchini-Visintini, A and Lygeros, J and Maciejowski, J (2007) Simulated Annealing: Rigorous finite-time guarantees for optimization on continuous domains.
Full text not available from this repository.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.
| Item Type: | Article |
|---|---|
| Additional Information: | 10 pages, 2 figures. Preprint. The final version will appear in: Advances in Neural Information Processing Systems 20, Proceedings of NIPS 2007, MIT Press |
| Subjects: | UNSPECIFIED |
| Divisions: | Div F > Control |
| Depositing User: | Cron Job |
| Date Deposited: | 19 Dec 2011 15:11 |
| Last Modified: | 20 May 2013 01:41 |
| DOI: |
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