Stochastic Gradient Estimation

Michael C. Fu ()
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Michael C. Fu: University of Maryland

Chapter Chapter 5 in Handbook of Simulation Optimization, 2015, pp 105-147 from Springer

Abstract: Abstract This chapter reviews simulation-based methods for estimating gradients, which are central to gradient-based simulation optimization algorithms such as stochastic approximation and sample average approximation. We begin by describing approaches based on finite differences, including the simultaneous perturbation method. The remainder of the chapter then focuses on the direct gradient estimation techniques of perturbation analysis, the likelihood ratio/score function method, and the use of weak derivatives (also known as measure-valued differentiation). Various examples are provided to illustrate the different estimators—for a single random variable, a stochastic activity network, and a single-server queue. Recent work on quantile sensitivity estimation is summarized, and several newly proposed approaches for using stochastic gradients in simulation optimization are discussed.

Date: 2015
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DOI: 10.1007/978-1-4939-1384-8_5

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