Stochastic Recursive Algorithms for Optimization

Simultaneous Perturbation Methods

By: S. Bhatnagar, H.L. Prasad, L.A. Prashanth

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Paperback | 12 August 2012

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322 Pages

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23.39 x 15.6 x 1.7

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Stochastic 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 stochastic 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 data.
Chapters on their application in service systems, vehicular traffic control and communications networks illustrate this point. The book is self-contained with necessary mathematical results placed in an appendix.
The text provides easy-to-use, off-the-shelf algorithms that are given detailed mathematical treatment so the material presented will be of significant interest to practitioners, academic researchers and graduate students alike. The breadth of applications makes the book appropriate for reader from similarly diverse backgrounds: workers in relevant areas of computer science, control engineering, management science, applied mathematics, industrial engineering and operations research will find the content of value.

Industry Reviews

From the reviews:

"The book under review summarizes the recent research on simultaneously perturbation problems. ... The book provides a coverage of the known material in stochastic optimizations, such that both researchers and practitioners should find it useful. The text is well understandable, the book is clearly written and impressively printed. Theorems and algorithms are emphasized in coloured frames. Therefore, the book can be used as material for lectures dedicated to master students. ... There are references at the end of every chapter." (Werner H. Schmidt, Zentralblatt MATH, Vol. 1260, 2013)

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