MARC Record from marc_columbia

LEADER: 04373cam a2200613Ia 4500
001 15923680
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007 cr cnu|||unuuu
008 040707s2003 nyua ob 001 0 eng d
035 $a(OCoLC)ocm55856272
035 $a(NNC)15923680
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016 7 $a010977147$2Uk
019 $a992061078
020 $a038721769X$q(electronic bk.)
020 $a9780387217697$q(electronic bk.)
035 $a(OCoLC)55856272$z(OCoLC)992061078
050 4 $aQA274.2$b.K88 2003eb
072 7 $aMAT$x029000$2bisacsh
082 04 $a519.2$222
049 $aZCUA
100 1 $aKushner, Harold J.$q(Harold Joseph),$d1933-$eauthor.
245 10 $aStochastic approximation and recursive algorithms and applications /$cHarold J. Kushner, G. George Yin.
250 $aSecond edition.
260 $aNew York :$bSpringer,$c©2003.
300 $a1 online resource (xxii, 474 pages) :$billustrations
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
490 1 $aApplications of mathematics ;$v35
500 $aRevised edition of: Stochastic approximation algorithms and applications. c1997.
504 $aIncludes bibliographical references (pages 443-463) and indexes.
505 0 $aIntroduction: Applications and Issues -- Applications to Learning, Repeated Games, State Dependent Noise, and Queue Optimization -- Applications to Signal Processing, Communications, and Adaptive Control -- Mathematical Background -- Convergence w.p.1: Martingale Difference Noise -- Convergence w.p.1: Correlated Noise -- Weak Convergence: Introduction -- Weak Convergence Methods for General Algorithms -- Applications: Proofs of Convergence -- Rate of Convergence -- Averaging of the Iterates -- Distributed/Decentralized and Asynchronous Algorithms.
520 $aThis 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. There is a complete development of both probability one and weak convergence methods for very general noise processes. The proofs of convergence use the ODE method, the most powerful to date. The assumptions and proof methods are designed to cover the needs of recent applications. The development proceeds from simple to complex problems, allowing the underlying ideas to be more easily understood. Rate of convergence, iterate averaging, high-dimensional problems, stability-ODE methods, two time scale, asynchronous and decentralized algorithms, state-dependent noise, stability methods for correlated noise, perturbed test function methods, and large deviations methods are covered. 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.
588 0 $aPrint version record.
650 0 $aStochastic approximation.
650 0 $aRecursive functions.
650 0 $aApproximation algorithms.
650 6 $aApproximation stochastique.
650 6 $aFonctions récursives.
650 6 $aAlgorithmes d'approximation.
650 7 $aMATHEMATICS$xProbability & Statistics$xGeneral.$2bisacsh
650 7 $aApproximation algorithms.$2fast$0(OCoLC)fst01749725
650 7 $aRecursive functions.$2fast$0(OCoLC)fst01091984
650 7 $aStochastic approximation.$2fast$0(OCoLC)fst01133501
655 0 $aElectronic books.
655 4 $aElectronic books.
700 1 $aYin, George,$d1954-$eauthor.
700 1 $aKushner, Harold J.$q(Harold Joseph),$d1933-$tStochastic approximation algorithms and applications.
776 08 $iPrint version:$aKushner, Harold J. (Harold Joseph), 1933-$tStochastic approximation and recursive algorithms and applications.$b2nd ed.$dNew York : Springer, ©2003$z0387008942$w(DLC) 2003045459$w(OCoLC)51861927
830 0 $aApplications of mathematics ;$v35.
856 40 $uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio15923680$zAll EBSCO eBooks
852 8 $blweb$hEBOOKS