A Discrete Parameter Stochastic Approximation Algorithm for Simulation Optimization

Abstract

The authors develop a two-timescale simultaneous perturbation stochastic approximation algorithm for simulation-based parameter optimization over discrete sets. This algorithm is applicable in cases where the cost to be optimized is in itself the long-run average of certain cost functions whose noisy estimates are obtained via simulation. The authors present the convergence analysis of their algorithm. Next, they study applications of their algorithm to the problem of admission control in communication networks. They study this problem under two different experimental settings and consider appropriate continuous time queuing models in both settings. Their algorithm finds optimal threshold-type policies within suitable parameterized classes of these. They show results of several experiments for different network parameters and rejection cost. The authors also study the sensitivity of their algorithm with respect to its parameters and step sizes. The results obtained are along expected lines.