Abstract
Simulated Annealing is a family of randomized algorithms for solving multivariate global optimization problems. Empirical results from the application of Simulated Annealing algorithms to certain hard problems including certain types of NP-complete problems demonstrate that these algorithms yield better results than known heuristic algorithms. But for the worst case input, the time bound can be exponential.
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