Simulated Annealing: Rigorous finite-time guarantees for optimization on continuous domains

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dc.creatorLecchini-Visintini, A.
dc.creatorLygeros, J.
dc.creatorMaciejowski, J.
dc.date2007-09-19
dc.date.accessioned2026-07-07T08:30:50Z
dc.date.available2026-07-07T08:30:50Z
dc.descriptionSimulated 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.
dc.description10 pages, 2 figures. Preprint. The final version will appear in: Advances in Neural Information Processing Systems 20, Proceedings of NIPS 2007, MIT Press
dc.identifierhttps://arxiv.org/abs/0709.2989
dc.identifierhttp://arxiv.org/abs/0709.2989
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/138343
dc.subjectMachine Learning
dc.titleSimulated Annealing: Rigorous finite-time guarantees for optimization on continuous domains
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