Optimization Of Stochastic Systems Via Simulation

In this paper, we discuss some research issues related to the general topic of optimizing a stochastic via simulation. In particular, we devote extensive attention to finite-difference estimators of objective function gradients and present a number of new limit theorems. We also discuss a new family of orthogonal funtion approximations to the global behavior of the objective function. We show that if the objective function is sufficiently smooth, the convergence rate can be made arbitrarily close to n/sup -1/2/ in the number of observations required. The paper concludes with a brief discussion of how these ideas can be integrated into an optimization algorithm.