Stochastic Recursive Algorithms for Optimization

Simultaneous Perturbation Methods

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Stochastic Recursive Algorithms for Optimization presents algorithms for constrained and unconstrained optimization and for reinforcement learning. Efficient perturbation approaches form a thread unifying all the algorithms considered. Simultaneous perturbation stochastic approximation and smooth fractional estimators for gradient- and Hessian-based methods are presented. These algorithms:- are easily implemented;- do not require an explicit system model; and- work with real or simulated data.Chapters on their application in service systems, vehicular traffic control and communications network...