Stochastic Approximation and Recursive Algorithms and Applications

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Focusing on stochastic approximation algorithms, this book delves into their theoretical and applied aspects, stemming from foundational work by Robbins, Monro, Kiefer, and Wolfowitz in the 1950s. It explores the dynamics of stochastic processes through recursive adjustments of parameters based on noise-corrupted observations. The text emphasizes qualitative and asymptotic properties of these algorithms, including their continuous time counterparts, and discusses their application in root-finding problems where functions are not explicitly known.