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Wednesday, February 5, 2020 | History

5 edition of Stochastic approximation algorithms and applications found in the catalog.

Stochastic approximation algorithms and applications

  • 136 Want to read
  • 31 Currently reading

Published by Springer in New York .
Written in English

    Subjects:
  • Stochastic approximation.

  • Edition Notes

    Includes bibliographical references (p. [393]-407) and indexes.

    StatementHarold J. Kushner, G. George Yin.
    SeriesApplications of mathematics ;, 35
    ContributionsYin, George, 1954-
    Classifications
    LC ClassificationsQA274.2 .K88 1997
    The Physical Object
    Paginationxxi, 417 p. :
    Number of Pages417
    ID Numbers
    Open LibraryOL1008855M
    ISBN 10038794916X
    LC Control Number96048847

    There is a useful plethora of applications, each with concrete examples from engineering and economics. Reinforcement Learning and Formal Requirements. Many motivating examples from learning theory, ergodic cost problems for discrete event systems, wireless communications, adaptive control, signal processing, and elsewhere illustrate the applications of the theory. European Journal of Operational Research

    Neural Information Processing. Some experience with computer simulation. Crossref Structure-aware stochastic load management in smart grids. Crossref Stability and convergence of stochastic approximation using the ODE method. Crossref Implicit incremental natural actor critic algorithm. Parts of the material were developed earlier on from toas part of a course in Stochastic Modelling and Simulation that I taught at the Computer Science Department at the University of Montreal, with students in various joint programs in Computer Science, Management, Industrial Engineering, Finance, and Mathematics.

    It contains many additional applications and results as well as more detailed discussion. Applied Physics Letters Understand the convergence properties of various methods. European Journal of Operational Research


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Stochastic approximation algorithms and applications book

Crossref Consumer-aware load control to provide contingency reserves using frequency measurements and inter-load communication. Crossref Cooperative dynamics and Wardrop equilibria. Crossref On stochastic gradient and subgradient methods with adaptive steplength sequences.

Stochastic optimization

Our pedagogical formula focuses on individual needs and goals, and we will emphasize understanding through hands-on experience with examples and computer exercises. Crossref Spiral bacterial foraging optimization method: Algorithm, evaluation and convergence analysis. Sensors Among the problems that we will discuss are the following.

We note that the HCRF framework is easily extensible to recognition since it is a state and label sequence modeling technique. Computational Optimization and Applications Crossref A novel Q-learning algorithm with function approximation for constrained Markov decision processes.

Crossref Scalable statistical inference for averaged implicit stochastic gradient descent. The aim here is to put such algorithms on a sure mathematical footing and analyze their behavior.

Students have expressed in their evaluations that they have enjoyed and appreciated the learning process just as much as the acquired knowledge, particularly through the research projects.

Stochastic Approximation and Recursive Algorithms and Applications

Crossref Mathkar and Vivek S. Keywords Averaging Markov chain Martingale algorithms diffusion process jump diffusion linear optimization numerical methods optimization programming random walk stability Authors and affiliations.

Crossref Stochastic quasi-Newton methods for non-strongly convex problems: Convergence and rate analysis. International Journal of Theoretical and Applied Finance There is a complete development of both probability one and weak convergence methods for very general noise processes.

Journal of Scientific Computing The proofs of convergence use the ODE method, the most powerful to date. Computer Networks We will illustrate dicult concepts with actual problems for students to code and see the methods at work.Get this from a library!

Stochastic approximation and recursive algorithms and applications. [Harold J Kushner; George Yin] -- This book presents a thorough development of the modern theory of stochastic approximation or recursive stochastic algorithms for both constrained and unconstrained problems.

This second edition is a. This book presents a thorough development of the modern theory of stochastic approximation or recursive stochastic algorithms for both constrained and unconstrained problems.

This second edition is a thorough revision, although the main features and structure remain unchanged. It contains many additional applications and results as well as more detailed discussion.

In this paper we study new stochastic approximation (SA) type algorithms, namely, the accelerated SA (AC-SA), for solving strongly convex stochastic composite optimization (SCO) problems. Specifica Cited by: This revised and expanded second edition presents a thorough development of the modern theory of stochastic approximation or recursive stochastic algorithms for both constrained and unconstrained problems.

Stochastic Approximation and Its Applications

There is a complete development of both probability one and weak convergence methods for very general noise processes. I believe that Kushner and Yin in their book Stochastic Approximation and Recursive Algorithms and Applications suggest that the notion had been used in control theory as far back as the 40's, but I don't recall if they had a citation for that or if it was anecdotal, nor do I have access to their book.

This book is a great reference book, and if you are patient, it is also a very good self-study book in the field of stochastic approximation. The book is written in Cited by: