Simulation optimization of air traffic delay cost
Proceedings of the 30th conference on Winter simulation
Stochastic optimization and the simultaneous perturbation method
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
ACM Transactions on Modeling and Computer Simulation (TOMACS)
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Universal parameter optimisation in games based on SPSA
Machine Learning
Selection Procedures with Frequentist Expected Opportunity Cost Bounds
Operations Research
Advances in Engineering Software
RSPSA: enhanced parameter optimization in games
ACG'05 Proceedings of the 11th international conference on Advances in Computer Games
Random search for hyper-parameter optimization
The Journal of Machine Learning Research
Simulation-based optimal Bayesian experimental design for nonlinear systems
Journal of Computational Physics
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The method of Common Random Numbers is a technique used to reduce the variance of difference estimates in simulation optimization problems. These differences are commonly used to estimate gradients of objective functions as part of the process of determining optimal values for parameters of a simulated system. Asymptotic results exist which show that using the Common Random Numbers method in the iterative Finite Difference Stochastic Approximation optimization algorithm (FDSA) can increase the optimal rate of convergence of the algorithm from the typical rate of k-1/3 to the faster k-1/2, where k is the algorithm's iteration number. Simultaneous Perturbation Stochastic Approximation (SPSA) is a newer and often much more efficient optimization algorithm, and we will show that this algorithm, too, converges faster when the Common Random Numbers method is used. We will also provide multivariate asymptotic covariance matrices for both the SPSA and FDSA errors.