Vivek S. Borkar Books

This simple, compact toolkit for designing and analyzing stochastic approximation algorithms requires only a basic understanding of probability and differential equations. Although powerful, these algorithms have applications in control and communications engineering, artificial intelligence and economic modeling. Unique topics include finite-time behavior, multiple timescales and asynchronous implementation. There is a useful plethora of applications, each with concrete examples from engineering and economics. Notably it covers variants of stochastic gradient-based optimization schemes, fixed-point solvers, which are commonplace in learning algorithms for approximate dynamic programming, and some models of collective behavior.

Focusing on stochastic approximation algorithms, this advanced text offers a thorough exploration through the lens of ordinary differential equations (ODE). The second edition enhances its coverage of classical convergence analysis while incorporating recent advancements like concentration bounds and stability tests. It also addresses distributed and asynchronous schemes, multiple time scales, and general noise models. With a category-wise exposition of significant applications, it serves as an essential resource for graduate students and professionals in probability, statistics, engineering, economics, and machine learning.

Focusing on stochastic approximation algorithms, this advanced text offers a thorough exploration using the ordinary differential equation (ODE) approach. The second edition enhances the classical convergence analysis and incorporates recent advancements like concentration bounds, stability tests, and distributed schemes. It also addresses multiple time scales and general noise models, making it a valuable resource for graduate students in various disciplines as well as researchers and practitioners seeking a comprehensive reference on the subject.