Monte Carlo optimization, simulation, and sensitivity of queueing networks
Monte Carlo optimization, simulation, and sensitivity of queueing networks
Convergence properties of infinitesimal perturbation analysis
Management Science
Parallel and distributed computation: numerical methods
Parallel and distributed computation: numerical methods
Importance sampling for stochastic simulations
Management Science
Likelihood ratio gradient estimation for stochastic systems
Communications of the ACM - Special issue on simulation
On line optimization of simulated Markovian processes
Mathematics of Operations Research
A unified view of the IPA, SF, and LR gradient estimation techniques
Management Science
Acceleration of stochastic approximation by averaging
SIAM Journal on Control and Optimization
Simulation: a statistical perspective
Simulation: a statistical perspective
Stochastic quasigradient methods for optimization of discrete event systems
Annals of Operations Research - Special issue on sensitivity analysis and optimization of discrete event systems
Convergence rates for steady-state derivative estimators
Annals of Operations Research - Special issue on sensitivity analysis and optimization of discrete event systems
Gradient estimates for the performance of Markov chains and discrete event processes
Annals of Operations Research - Special issue on sensitivity analysis and optimization of discrete event systems
A stochastic quasigradient algorithm with variable metric
Annals of Operations Research - Special issue on sensitivity analysis and optimization of discrete event systems
Two approaches for estimating the gradient in functional form
WSC '93 Proceedings of the 25th conference on Winter simulation
Methods for selecting the best system
WSC '91 Proceedings of the 23rd conference on Winter simulation
Gradient estimation for ratios
WSC '91 Proceedings of the 23rd conference on Winter simulation
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Budget-Dependent Convergence Rate of Stochastic Approximation
SIAM Journal on Optimization
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In this work, we examine how to combine the score function methodwith the standard crude Monte Carlo and experimental design approaches,in order to evaluate the expected performance of a discrete eventsystem and its associated gradient simultaneously for differentscenarios (combinations of parameter values), as well as to optimizethe expected performance with respect to two parameter sets,which represent parameters of the underlying probability law(for the system‘s evolution) and parameters of the sample performancemeasure, respectively. We explore how the stochastic approximationand stochastic counterpart methods can be combined to performoptimization with respect to both sets of parameters at the sametime. We outline three combined algorithms of that form, onesequential and two parallel, and give a convergence proof forone of them. We discuss a number of issues related to the implementationand convergence of those algorithms, introduce averaging variants,and give numerical illustrations.