Performance continuity and differentiability in Monte Carlo optimization

This paper describes a class of Monte Carlo optimization problems for which unbiased derivative estimators of the infinitesimal perturbation analysis (IPA) type can be derived; and also a simple framework within which to establish unbiasedness. Of central importance are systems with continuous, piecewise differentiable sample performance functions. Experience suggests that continuity is, in practice, almost necessary for IPA to work. "Piecewise" differentiable is a concession to the discrete nature of many applied probability models. We discuss a variety of examples, including both static and dynamic systems.