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We assume the existence of a parameterized family of control variates that could be used in a regenerative steady-state simulation. We show how such controls can be generated in the Markov-process setting, discuss the optimization problem of searching for a good choice of parameterization, and develop a strong law and central limit theorem for the resulting estimator.
We present a review of methods for simulation optimization. In particular, we focus on gradient-based techniques for continuous optimization. We demonstrate the main concepts using as an example the multidimensional newsvendor problem. We also discuss mathematical techniques and results that are useful in verifying and analyzing the simulation optimization procedures
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