Statistical Science

Simulated Annealing

Dimitris Bertsimas and John Tsitsiklis

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Simulated annealing is a probabilistic method proposed in Kirkpatrick, Gelett and Vecchi (1983) and Cerny (1985) for finding the global minimum of a cost function that may possess several local minima. It works by emulating the physical process whereby a solid is slowly cooled so that when eventually its structure is "frozen," this happens at a minimum energy configuration. We restrict ourselves to the case of a cost function defined on a finite set. Extensions of simulated annealing to the case of functions defined on continuous sets have also been introduced in the literature (e.g., Geman and Hwang, 1986; Gidas, 1985a; Holley, Kusuoka and Stroock, 1989; Jeng and Woods, 1990; Kushner, 1985). Our goal in this review is to describe the method, its convergence and its behavior in applications.

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Statist. Sci., Volume 8, Number 1 (1993), 10-15.

First available in Project Euclid: 19 April 2007

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Markov chains randomized algorithms simulated annealing


Bertsimas, Dimitris; Tsitsiklis, John. Simulated Annealing. Statist. Sci. 8 (1993), no. 1, 10--15. doi:10.1214/ss/1177011077.

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