The basic stochastic approximation algorithms introduced by Robbins and MonroandbyKieferandWolfowitzintheearly1950shavebeenthesubject of an enormous literature, both theoretical and applied. This is due to the large number of applications and the interesting theoretical issues in the analysis of "dynamically de?ned" stochastic processes. The basic paradigm is a stochastic di?erence equation such as ? = ? + Y , where ? takes n+1 n n n n its values in some Euclidean space, Y is a random variable, and the "step n size" > 0 is ...
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The basic stochastic approximation algorithms introduced by Robbins and MonroandbyKieferandWolfowitzintheearly1950shavebeenthesubject of an enormous literature, both theoretical and applied. This is due to the large number of applications and the interesting theoretical issues in the analysis of "dynamically de?ned" stochastic processes. The basic paradigm is a stochastic di?erence equation such as ? = ? + Y , where ? takes n+1 n n n n its values in some Euclidean space, Y is a random variable, and the "step n size" > 0 is small and might go to zero as n??. In its simplest form, n ? is a parameter of a system, and the random vector Y is a function of n "noise-corrupted" observations taken on the system when the parameter is set to ? . One recursively adjusts the parameter so that some goal is met n asymptotically. Thisbookisconcernedwiththequalitativeandasymptotic properties of such recursive algorithms in the diverse forms in which they arise in applications. There are analogous continuous time algorithms, but the conditions and proofs are generally very close to those for the discrete time case. The original work was motivated by the problem of ?nding a root of a continuous function g ???(?), where the function is not known but the - perimenter is able to take "noisy" measurements at any desired value of ?. Recursive methods for root ?nding are common in classical numerical analysis, and it is reasonable to expect that appropriate stochastic analogs would also perform well.
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Add this copy of Stochastic Approximation and Recursive Algorithms and to cart. $90.46, very good condition, Sold by Zubal Books rated 4.0 out of 5 stars, ships from Cleveland, OH, UNITED STATES, published 2010 by Springer.
Edition:
Softcover reprint of hardcover 2nd edition 2003
Publisher:
Springer-Verlag New York Inc.
Published:
2010
Language:
English
Alibris ID:
18093464838
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2nd edition, 500 pp., paperback, a FAINT bump to upper corner of pages else fine. -If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country.
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Very Good; Hardcover; Light wear to the covers; Unblemished textblock edges; The endpapers and all text pages are clean and unmarked; The binding is excellent with a straight spine; This book will be shipped in a sturdy cardboard box with foam padding; Medium Format (8.5"-9.75" tall); Tan and yellow covers with title in yellow lettering; 2nd Edition; 2003, Springer-Verlag Publishing; 500 pages; "Stochastic Approximation and Recursive Algorithms and Applications (Stochastic Modelling and Applied Probability, 35), " by Harold Kushner & G. George Yin.
Add this copy of Stochastic Approximation and Recursive Algorithms and to cart. $144.21, good condition, Sold by Bonita rated 4.0 out of 5 stars, ships from Newport Coast, CA, UNITED STATES, published 2010 by Springer.
Add this copy of Stochastic Approximation and Recursive Algorithms and to cart. $147.51, good condition, Sold by Bonita rated 4.0 out of 5 stars, ships from Newport Coast, CA, UNITED STATES, published 2003 by Springer.
