Rates of Convergence for a Class of Global Stochastic Optimization Algorithms

Inspired and motivated by the recent advances in simulated annealing algorithms, this paper analyzes the convergence rates of a class of recursive algorithms for global optimization via Monte Carlo methods. By using perturbed Liapunov function methods, stability results of the algorithms are established. Then the rates of convergence are ascertained by examining the asymptotic properties of suitably scaled estimation error sequences.

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