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Stochastic Approximation and Recursive Algorithms and Applications - Kushner, Harold J.; Yin, G. George
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Kushner, Harold J.; Yin, G. George:
Stochastic Approximation and Recursive Algorithms and Applications - Paperback

2009, ISBN: 0387891196, Lieferbar binnen 4-6 Wochen Shipping costs:Versandkostenfrei innerhalb der BRD

ID: 9780387891194

Internationaler Buchtitel. In englischer Sprache. Verlag: SPRINGER VERLAG GMBH, 500 Seiten, L=156mm, B=234mm, H=26mm, Gew.=694gr, [GR: 26270 - TB/Mathematik/Analysis], [SW: - Mathematics], Kartoniert/Broschiert, [Ausgabe: 0002] The book 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, with which the asymptotic behavior is characterized by the limit behavior of a mean ODE. 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, general correlated and state-dependent noise, perturbed test function methods, and large devitations methods, are covered. Many motivational examples from learning theory, ergodic cost problems for discrete event systems, wireless communications, adaptive control, signal processing, and elsewhere, illustrate the application of the theory. This second edition is a thorough revision, although the main features and the structure remain unchanged. It contains many additional applications and results, and more detailed discussion. Harold J. Kushner is a University Professor and Professor of Applied Mathematics at Brown University. He has written numerous books and articles on virtually all aspects of stochastic systems theory, and has received various awards including the IEEE Control Systems Field Award. TOC:Introduction: 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

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Stochastic Approximation and Recursive Algorithms and Applications - Kushner, Harold J. Yin, G. George
book is out-of-stock
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Kushner, Harold J. Yin, G. George:
Stochastic Approximation and Recursive Algorithms and Applications - Paperback

ISBN: 9780387891194

[ED: Taschenbuch], [PU: SPRINGER VERLAG GMBH], The book 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, with which the asymptotic behavior is characterized by the limit behavior of a mean ODE. 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, general correlated and state-dependent noise, perturbed test function methods, and large devitations methods, are covered. Many motivational examples from learning theory, ergodic cost problems for discrete event systems, wireless communications, adaptive control, signal processing, and elsewhere, illustrate the application of the theory. This second edition is a thorough revision, although the main features and the structure remain unchanged. It contains many additional applications and results, and more detailed discussion. Harold J. Kushner is a University Professor and Professor of Applied Mathematics at Brown University. He has written numerous books and articles on virtually all aspects of stochastic systems theory, and has received various awards including the IEEE Control Systems Field Award. TOC:Introduction: 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 AlgorithmsVersandfertig in über 4 Wochen, [SC: 0.00]

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Stochastic Approximation and Recursive Algorithms and Applications - Kushner, Harold J. Yin, G. George
book is out-of-stock
(*)
Kushner, Harold J. Yin, G. George:
Stochastic Approximation and Recursive Algorithms and Applications - Paperback

ISBN: 9780387891194

[ED: Taschenbuch], [PU: SPRINGER VERLAG GMBH], The book 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, with which the asymptotic behavior is characterized by the limit behavior of a mean ODE. 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, general correlated and state-dependent noise, perturbed test function methods, and large devitations methods, are covered. Many motivational examples from learning theory, ergodic cost problems for discrete event systems, wireless communications, adaptive control, signal processing, and elsewhere, illustrate the application of the theory. This second edition is a thorough revision, although the main features and the structure remain unchanged. It contains many additional applications and results, and more detailed discussion. Harold J. Kushner is a University Professor and Professor of Applied Mathematics at Brown University. He has written numerous books and articles on virtually all aspects of stochastic systems theory, and has received various awards including the IEEE Control Systems Field Award. TOC:Introduction: 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 AlgorithmsVersandfertig in über 4 Wochen, [SC: 0.00]

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Stochastic Approximation and Recursive Algorithms and Applications - Kushner, Harold J. Yin, G. George
book is out-of-stock
(*)
Kushner, Harold J. Yin, G. George:
Stochastic Approximation and Recursive Algorithms and Applications - Paperback

ISBN: 9780387891194

[ED: Taschenbuch], [PU: SPRINGER VERLAG GMBH], The book 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, with which the asymptotic behavior is characterized by the limit behavior of a mean ODE. 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, general correlated and state-dependent noise, perturbed test function methods, and large devitations methods, are covered. Many motivational examples from learning theory, ergodic cost problems for discrete event systems, wireless communications, adaptive control, signal processing, and elsewhere, illustrate the application of the theory. This second edition is a thorough revision, although the main features and the structure remain unchanged. It contains many additional applications and results, and more detailed discussion. Harold J. Kushner is a University Professor and Professor of Applied Mathematics at Brown University. He has written numerous books and articles on virtually all aspects of stochastic systems theory, and has received various awards including the IEEE Control Systems Field Award. TOC:Introduction: 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 AlgorithmsVersandfertig in über 4 Wochen, [SC: 0.00]

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Details of the book

The book 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, with which the asymptotic behavior is characterized by the limit behavior of a mean ODE. 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, general correlated and state-dependent noise, perturbed test function methods, and large devitations methods, are covered. Many motivational examples from learning theory, ergodic cost problems for discrete event systems, wireless communications, adaptive control, signal processing, and elsewhere, illustrate the application of the theory. This second edition is a thorough revision, although the main features and the structure remain unchanged. It contains many additional applications and results, and more detailed discussion. Harold J. Kushner is a University Professor and Professor of Applied Mathematics at Brown University. He has written numerous books and articles on virtually all aspects of stochastic systems theory, and has received various awards including the IEEE Control Systems Field Award. TOC:Introduction: 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

Details of the book - Stochastic Approximation and Recursive Algorithms and Applications


EAN (ISBN-13): 9780387891194
ISBN (ISBN-10): 0387891196
Paperback
Publishing year: 2009
Publisher: SPRINGER VERLAG GMBH
500 Pages
Weight: 0,694 kg
Language: eng/Englisch

Book in our database since 08.06.2011 10:21:41
Book found last time on 04.01.2012 02:11:37
ISBN/EAN: 9780387891194

ISBN - alternate spelling:
0-387-89119-6, 978-0-387-89119-4


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