A new search algorithm for discrete stochastic optimization

We present a new method for finding a global optimal solution to a discrete stochastic optimization problem. This method resembles the simulated annealing method for discrete deterministic optimization. However, in our method the annealing schedule (the cooling temperature) is kept fixed, and the mechanism for estimating the optimal solution is different from that used in the original simulated annealing method. We state a convergence result that shows that our method converges almost surely to a global optimal solution under mild conditions. We also present empirical results that illustrate the performance of the proposed approach on a simple example.