Abstract
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.
Research supported by the Army Research Office under contract DAAAG-29–84-K-0005.
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© 1988 Springer-Verlag New York Inc.
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Tsitsiklis, J.N. (1988). A Survey of Large Time Asymptotics of Simulated Annealing Algorithms. In: Fleming, W., Lions, PL. (eds) Stochastic Differential Systems, Stochastic Control Theory and Applications. The IMA Volumes in Mathematics and Its Applications, vol 10. Springer, New York, NY. https://doi.org/10.1007/978-1-4613-8762-6_33
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DOI: https://doi.org/10.1007/978-1-4613-8762-6_33
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