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.
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.
|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|
|Divisions:||Div F > Control|
|Depositing User:||Cron Job|
|Date Deposited:||19 Dec 2011 15:11|
|Last Modified:||20 May 2013 01:41|
Actions (login required)