Stochastic Approximation: A Dynamical Systems Viewpoint

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Focusing on stochastic approximation algorithms, this advanced text offers a comprehensive analysis using the ordinary differential equation (ODE) approach. The second edition enhances classical convergence analysis and introduces recent developments like concentration bounds and stability tests. It covers distributed schemes, multiple time scales, and various noise models, while also categorizing key applications. This book serves as both a graduate course resource and a valuable reference for researchers and practitioners in probability, statistics, engineering, economics, and machine learning.