Lecchini Visintini, A and Lygeros, J and Maciejowski, JM (2007) Rigorous finite-time guarantees for optimization on continuous domains by simulated annealing. In: NIPS 2007: Neural Information Processing Systems, 3-12-2007 to 6-12-2007, Vancouver, Canada -..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.
|Item Type:||Conference or Workshop Item (UNSPECIFIED)|
|Additional Information:||Event type = conference|
|Divisions:||Div F > Control|
|Depositing User:||Cron Job|
|Date Deposited:||28 Oct 2011 16:40|
|Last Modified:||26 Dec 2011 01:24|
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