Limit Theorems for Simulation-based Optimization via

Y. L. Chia and P. W. Glynn

ACM Transactions on Modeling and Computer Simulation, Vol. 23, No. 3, Article 16

This paper develops fundamental theory related to the use of simulation-based non-adaptive random search as a means of optimizing a function that can be expressed as an expectation. Our results establish rates of convergence that express the trade-off between exploration and estimation, and fully characterize the limit distributions that arise. Our rates of convergence results should be viewed as a baseline against which to compare more intelligent algorithms.