Simulation output analysis using standardized time series
Mathematics of Operations Research
Likelihood ratio gradient estimation for stochastic systems
Communications of the ACM - Special issue on simulation
Stochastic approximation and optimization of random systems
Stochastic approximation and optimization of random systems
Acceleration of stochastic approximation by averaging
SIAM Journal on Control and Optimization
Sensitivity analysis of discrete event systems by the “push out” method
Annals of Operations Research - Special issue on sensitivity analysis and optimization of discrete event systems
Stochastic approximation for Monte Carlo optimization
WSC '86 Proceedings of the 18th conference on Winter simulation
Journal of Experimental Algorithmics (JEA)
The mathematics of continuous-variable simulation optimization
Proceedings of the 40th Conference on Winter Simulation
The stochastic root-finding problem: Overview, solutions, and open questions
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Numerical estimation of the impact of interferences on the localization problem in sensor networks
WEA'06 Proceedings of the 5th international conference on Experimental Algorithms
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In principle, known central limit theorems for stochastic approximation schemes permit the simulationist to provide confidence regions for both the optimum and optimizer of a stochastic optimization problem that is solved by means of such algorithms. Unfortunately, the covariance structure of the limiting normal distribution depends in a complex way on the problem data. In particular, the covariance matrix depends not only on variance constants but also on even more statistically challenging parameters (e.g. the Hessian of the objective function at the optimizer). In this paper, we describe an approach to producing such confidence regions that avoids the necessity of having to explicitly estimate the covariance structure of the limiting normal distribution. This procedure offers an easy way for the simulationist to provide confidence regions in the stochastic optimization setting.