On the Use of Direct Search Methods for Stochastic Optimization

We examine the conventional wisdom that commends the use of direct search methods in the presence of random noise To do so we introduce new formulations of stochastic optimiza tion and direct search These formulations suggest a natural strategy for constructing globally convergent direct search algorithms for stochastic optimization by controlling the error rates of the ordering decisions on which direct search depends This strategy is successfully applied to the class of generalized pattern search methods However a great deal of sampling is required to guarantee convergence with probability one