Stochastic Approximation and Recursive Algorithms and Applications:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Springer New York
2003
|
| Ausgabe: | Second Edition |
| Schriftenreihe: | Stochastic Modelling and Applied Probability
35 |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | 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. 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 |
| Beschreibung: | 1 Online-Ressource (XXII, 478 p) |
| ISBN: | 9780387217697 9780387008943 |
| ISSN: | 0172-4568 |
| DOI: | 10.1007/b97441 |
Internformat
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| 490 | 0 | |a Stochastic Modelling and Applied Probability |v 35 |x 0172-4568 | |
| 500 | |a 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. 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 | ||
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Datensatz im Suchindex
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| dewey-ones | 519 - Probabilities and applied mathematics |
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| dewey-search | 519.2 |
| dewey-sort | 3519.2 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| doi_str_mv | 10.1007/b97441 |
| edition | Second Edition |
| format | Electronic eBook |
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| id | DE-604.BV042418987 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T01:21:03Z |
| institution | BVB |
| isbn | 9780387217697 9780387008943 |
| issn | 0172-4568 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027854404 |
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| physical | 1 Online-Ressource (XXII, 478 p) |
| psigel | ZDB-2-SMA ZDB-2-BAE ZDB-2-SMA_Archive |
| publishDate | 2003 |
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| publisher | Springer New York |
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| series2 | Stochastic Modelling and Applied Probability |
| spelling | Kushner, Harold J. 1933- (DE-588)11559163X aut Stochastic Approximation and Recursive Algorithms and Applications by Harold J. Kushner, G. George Yin Second Edition New York, NY Springer New York 2003 1 Online-Ressource (XXII, 478 p) txt rdacontent c rdamedia cr rdacarrier Stochastic Modelling and Applied Probability 35 0172-4568 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. 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 Mathematics Algorithms Distribution (Probability theory) Probability Theory and Stochastic Processes Approximations and Expansions Applications of Mathematics Mathematik Stochastische Approximation (DE-588)4183371-5 gnd rswk-swf Rekursiver Algorithmus (DE-588)4502357-8 gnd rswk-swf Rekursiver Algorithmus (DE-588)4502357-8 s 1\p DE-604 Stochastische Approximation (DE-588)4183371-5 s 2\p DE-604 Yin, George 1954- (DE-588)115596798 aut https://doi.org/10.1007/b97441 Verlag Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Kushner, Harold J. 1933- Yin, George 1954- Stochastic Approximation and Recursive Algorithms and Applications Mathematics Algorithms Distribution (Probability theory) Probability Theory and Stochastic Processes Approximations and Expansions Applications of Mathematics Mathematik Stochastische Approximation (DE-588)4183371-5 gnd Rekursiver Algorithmus (DE-588)4502357-8 gnd |
| subject_GND | (DE-588)4183371-5 (DE-588)4502357-8 |
| title | Stochastic Approximation and Recursive Algorithms and Applications |
| title_auth | Stochastic Approximation and Recursive Algorithms and Applications |
| title_exact_search | Stochastic Approximation and Recursive Algorithms and Applications |
| title_full | Stochastic Approximation and Recursive Algorithms and Applications by Harold J. Kushner, G. George Yin |
| title_fullStr | Stochastic Approximation and Recursive Algorithms and Applications by Harold J. Kushner, G. George Yin |
| title_full_unstemmed | Stochastic Approximation and Recursive Algorithms and Applications by Harold J. Kushner, G. George Yin |
| title_short | Stochastic Approximation and Recursive Algorithms and Applications |
| title_sort | stochastic approximation and recursive algorithms and applications |
| topic | Mathematics Algorithms Distribution (Probability theory) Probability Theory and Stochastic Processes Approximations and Expansions Applications of Mathematics Mathematik Stochastische Approximation (DE-588)4183371-5 gnd Rekursiver Algorithmus (DE-588)4502357-8 gnd |
| topic_facet | Mathematics Algorithms Distribution (Probability theory) Probability Theory and Stochastic Processes Approximations and Expansions Applications of Mathematics Mathematik Stochastische Approximation Rekursiver Algorithmus |
| url | https://doi.org/10.1007/b97441 |
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