Stochastic Approximation Methods for Constrained and Unconstrained Systems PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Stochastic Approximation Methods for Constrained and Unconstrained Systems PDF full book. Access full book title Stochastic Approximation Methods for Constrained and Unconstrained Systems by H.J. Kushner. Download full books in PDF and EPUB format.
Author: H.J. Kushner Publisher: Springer Science & Business Media ISBN: Category : Mathematics Languages : en Pages : 282
Book Description
The book deals with a powerful and convenient approach to a great variety of types of problems of the recursive monte-carlo or stochastic approximation type. Such recu- sive algorithms occur frequently in stochastic and adaptive control and optimization theory and in statistical esti- tion theory. Typically, a sequence {X } of estimates of a n parameter is obtained by means of some recursive statistical th st procedure. The n estimate is some function of the n_l estimate and of some new observational data, and the aim is to study the convergence, rate of convergence, and the pa- metric dependence and other qualitative properties of the - gorithms. In this sense, the theory is a statistical version of recursive numerical analysis. The approach taken involves the use of relatively simple compactness methods. Most standard results for Kiefer-Wolfowitz and Robbins-Monro like methods are extended considerably. Constrained and unconstrained problems are treated, as is the rate of convergence problem. While the basic method is rather simple, it can be elaborated to allow a broad and deep coverage of stochastic approximation like problems. The approach, relating algorithm behavior to qualitative properties of deterministic or stochastic differ ential equations, has advantages in algorithm conceptualiza tion and design. It is often possible to obtain an intuitive understanding of algorithm behavior or qualitative dependence upon parameters, etc., without getting involved in a great deal of deta~l.
Author: H.J. Kushner Publisher: Springer Science & Business Media ISBN: 1468493523 Category : Mathematics Languages : en Pages : 273
Book Description
The book deals with a powerful and convenient approach to a great variety of types of problems of the recursive monte-carlo or stochastic approximation type. Such recu- sive algorithms occur frequently in stochastic and adaptive control and optimization theory and in statistical esti- tion theory. Typically, a sequence {X } of estimates of a n parameter is obtained by means of some recursive statistical th st procedure. The n estimate is some function of the n_l estimate and of some new observational data, and the aim is to study the convergence, rate of convergence, and the pa- metric dependence and other qualitative properties of the - gorithms. In this sense, the theory is a statistical version of recursive numerical analysis. The approach taken involves the use of relatively simple compactness methods. Most standard results for Kiefer-Wolfowitz and Robbins-Monro like methods are extended considerably. Constrained and unconstrained problems are treated, as is the rate of convergence problem. While the basic method is rather simple, it can be elaborated to allow a broad and deep coverage of stochastic approximation like problems. The approach, relating algorithm behavior to qualitative properties of deterministic or stochastic differ ential equations, has advantages in algorithm conceptualiza tion and design. It is often possible to obtain an intuitive understanding of algorithm behavior or qualitative dependence upon parameters, etc., without getting involved in a great deal of deta~l.
Author: H.J. Kushner Publisher: Springer ISBN: 9780387903415 Category : Mathematics Languages : en Pages : 263
Book Description
The book deals with a powerful and convenient approach to a great variety of types of problems of the recursive monte-carlo or stochastic approximation type. Such recu- sive algorithms occur frequently in stochastic and adaptive control and optimization theory and in statistical esti- tion theory. Typically, a sequence {X } of estimates of a n parameter is obtained by means of some recursive statistical th st procedure. The n estimate is some function of the n_l estimate and of some new observational data, and the aim is to study the convergence, rate of convergence, and the pa- metric dependence and other qualitative properties of the - gorithms. In this sense, the theory is a statistical version of recursive numerical analysis. The approach taken involves the use of relatively simple compactness methods. Most standard results for Kiefer-Wolfowitz and Robbins-Monro like methods are extended considerably. Constrained and unconstrained problems are treated, as is the rate of convergence problem. While the basic method is rather simple, it can be elaborated to allow a broad and deep coverage of stochastic approximation like problems. The approach, relating algorithm behavior to qualitative properties of deterministic or stochastic differ ential equations, has advantages in algorithm conceptualiza tion and design. It is often possible to obtain an intuitive understanding of algorithm behavior or qualitative dependence upon parameters, etc., without getting involved in a great deal of deta~l.
Author: Harold Kushner Publisher: Springer Science & Business Media ISBN: 038721769X Category : Mathematics Languages : en Pages : 485
Book Description
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.
Author: Harold Joseph Kushner Publisher: ISBN: Category : Mathematical optimization Languages : en Pages : 61
Book Description
The aim of the paper is the development of a structure for stochastic optimization algorithms (of the Monte-Carlo or stochastic approximation type) which is analogous to that used in non-linear programming. The developed structure is quite versatile, and seems to consider the elements of the problem in a very natural manner from both the theoretical and practical viewpoints. A second paper is also included in the report, titled: Stochastic Approximation Algorithms for the Local Optimization of Functions With Non-Unique Stationary Points.
Author: Kurt Marti Publisher: Springer Science & Business Media ISBN: 9783540222729 Category : Business & Economics Languages : en Pages : 332
Book Description
This text provides a concise overview of stochastic optimization and considers nonlinear optimization problems. Optimization problems arising in practice involve random parameters. For the computation of robust optimal solutions, deterministic substitute problems are needed. Based on the distribution of the random data, and using decision theoretical concepts, optimization problems under stochastic uncertainty are converted into deterministic substitute problems.
Author: H.J. Kushner
Author: Thomas Littlewood Gavin
Author: H.J. Kushner
Author: Harold Joseph Kushner
Author: H.J. Kushner
Author: H.J. Kushner
Author: Harold Kushner
Author: 
Author: Harold Joseph Kushner
Author: Kurt Marti
Author: Emilio Sanvincente Gargallo