The score function approach for sensitivity analysis of computer simulation models
Mathematics and Computers in Simulation
Infinitesimal perturbation analysis for general discrete event systems
Journal of the ACM (JACM)
Optimization in simulation: a survey of recent results
WSC '87 Proceedings of the 19th conference on Winter simulation
Optimization by Vector Space Methods
Optimization by Vector Space Methods
An overview of derivative estimation
WSC '91 Proceedings of the 23rd conference on Winter simulation
Variance properties of sample path derivatives of parametric random variables
Operations Research Letters
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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.