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Rigorous finite-time guarantees for optimization on continuous domains by simulated annealing

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. In this talk I will introduce new results which allow one to guarantee finite-time performance of simulated annealing in the optimization of functions of continuous variables. I will first recall a result of Vidyasagar which introduced rigorous finite-time guarantees for the optimization of expected-value criteria based on independent sampling of the optimization domain Then, I will show that a similar type of finite-time guarantees can be achieved by the more general family of simulated annealing algorithms. 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.

Type of Seminar:
Public Seminar
Prof. Andrea Lecchini-Visintini
Department of Engineering, University of Leicester, U.K
Apr 24, 2007   17:15

ETH Zentrum, Gloriastrasse 35, Building ETZ, Room E 6
Contact Person:

Prof. John Lygeros
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Biographical Sketch:
Andrea Lecchini-Visintini received the Laurea degree in computer engineering from the University of Pavia, Italy, in 1997 and the Ph.D. degree in computer engineering from the University of Brescia, Italy, in 2001. From April 2001 to June 2003, he was a Postdoctoral Researcher with the Center for System Engineering and Applied Mechanics, Université-Catholique de Louvain, Belgium. From July 2003 to June 2006, he was a Research Associate with the Control Group, Department of Engineering, University of Cambridge, U.K. Since July 2006, he is a Lecturer in Engineering with the Control & Instrumentation Group, Department of Engineering, University of Leicester, U.K. His research interests are in system identification and data-based control system design; optimization and predictive control for complex stochastic systems, with application to air traffic control; stochastic optimization; and Monte Carlo methods.