Stochastic Recursive Algorithms for Optimization: Simultaneous Perturbation Methods

Shastic 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 shastic 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 da...