A survey of large time asymptotics of simulated annealing algorithms

Simulated annealing is a probabilistic algorithm for minimizing a general cost function which may have multiple local minima The amount of randomness in this algorithm is controlled by the “temperature”, a scalar parameter which is decreased to zero as the algorithm progresses. We consider the case where the minimization is carried out over a finite domain and we present a survey of several results and analytical tools for studying the asymptotic behavior of the simulated annealing algorithm, as time goes to infinity and temperature approaches zero.

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