This book presents a thorough development of the modern theory of stochastic approximation or recursive stochastic algorithms for both constrained and unconstra

Stochastic Recursive Algorithms for Optimization presents algorithms for constrained and unconstrained optimization and for reinforcement learning. Efficient pe

There is a thorough treatment of rate of convergence, iterate averaging, high-dimensional problems, ergodic cost problems, stability methods for correlated nois

Control and communications engineers, physicists, and probability theorists, among others, will find this book unique. It contains a detailed development of app