Published by Cambridge University Press 01/09/2008, 2008
ISBN 10: 0521515920 ISBN 13: 9780521515924
Language: English
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Published by Cambridge University Press 01/09/2008, 2008
ISBN 10: 0521515920 ISBN 13: 9780521515924
Language: English
Seller: Bahamut Media, Reading, United Kingdom
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Add to basketCondition: Very Good. Shipped within 24 hours from our UK warehouse. Clean, undamaged book with no damage to pages and minimal wear to the cover. Spine still tight, in very good condition. Remember if you are not happy, you are covered by our 100% money back guarantee.
Published by Cambridge University Press, 2008
ISBN 10: 0521515920 ISBN 13: 9780521515924
Language: English
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Add to basketHardback. Condition: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.
Published by Cambridge University Press, 2008
ISBN 10: 0521515920 ISBN 13: 9780521515924
Language: English
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Published by CAMBRIDGE U.P., CAMBRIDGE, 2008
ISBN 10: 0521515920 ISBN 13: 9780521515924
Language: English
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Add to basketCartoné (tapa dura cartón). Condition: New. Dust Jacket Condition: Nuevo. 01. LIBRO.
Published by Cambridge University Press, 2008
ISBN 10: 0521515920 ISBN 13: 9780521515924
Language: English
Seller: Toscana Books, AUSTIN, TX, U.S.A.
Hardcover. Condition: new. Excellent Condition.Excels in customer satisfaction, prompt replies, and quality checks.
Published by Hindustan Book Agency, 2022
ISBN 10: 819519611X ISBN 13: 9788195196111
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Add to basketHardcover. Condition: New. Contents: 1. Introduction. 2. Convergence Analysis. 3. Finite time bounds and traps. 4. Stability Criteria. 5. Stochastic Recursive Inclusions. 6. Asynchronous Schemes. 7. A Limit Theorem for Fluctuations. 8. Multiple Timescales. 9. Constant Stepsize Algorithms. 10. General noise models. 11. Stochastic Gradient Schemes. 12. Liapunov and Related Systems. 13. Topics in Analysis. 14. Ordinary Differential Equations. 15. Topics in Probability. This book gives a comprehensive treatment of stochastic approximation algorithms using their differential equation limits, which lays bare its dynamical aspects. Highlights of the book include a streamlined treatment of classical results such as the analysis of asymptotic behavior for decreasing and constant stepsizes and the functional central limit theorem, and inclusion of several important extensions and recent developments such as concentration bounds, avoidance of traps, stability tests, asynchronous implementations, differential inclusion limits, multiple time scales, and general noise models. In addition, major applications are surveyed category-wise, with special focus on stochastic gradient descent. The book will be a valuable resource to students, researchers, and practitioners in statistics, applied probability, control and communication engineering, operations research, machine learning and economic models.
Published by Cambridge University Press, 2008
ISBN 10: 0521515920 ISBN 13: 9780521515924
Language: English
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Published by Cambridge University Press, 2008
ISBN 10: 0521515920 ISBN 13: 9780521515924
Language: English
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Published by Cambridge University Press, 2008
ISBN 10: 0521515920 ISBN 13: 9780521515924
Language: English
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Published by Cambridge University Press, 2008
ISBN 10: 0521515920 ISBN 13: 9780521515924
Language: English
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Published by Cambridge University Press, 2008
ISBN 10: 0521515920 ISBN 13: 9780521515924
Language: English
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Add to basketHardcover. Condition: Brand New. 1st edition. 164 pages. 9.25x6.00x0.50 inches. In Stock.
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Published by Springer Nature Singapore, Springer Nature Singapore, 2025
ISBN 10: 9819982790 ISBN 13: 9789819982790
Language: English
Seller: AHA-BUCH GmbH, Einbeck, Germany
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Add to basketTaschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book serves as an advanced text for a graduate course on stochastic algorithms for the students of probability and statistics, engineering, economics and machine learning. This second edition gives a comprehensive treatment of stochastic approximation algorithms based on the ordinary differential equation (ODE) approach which analyses the algorithm in terms of a limiting ODE. It has a streamlined treatment of the classical convergence analysis and includes several recent developments such as concentration bounds, avoidance of traps, stability tests, distributed and asynchronous schemes, multiple time scales, general noise models, etc., and a category-wise exposition of many important applications. It is also a useful reference for researchers and practitioners in the field.
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Published by Springer Verlag, Singapore, Singapore, 2024
ISBN 10: 9819982766 ISBN 13: 9789819982769
Language: English
Seller: Grand Eagle Retail, Fairfield, OH, U.S.A.
Hardcover. Condition: new. Hardcover. This book serves as an advanced text for a graduate course on stochastic algorithms for the students of probability and statistics, engineering, economics and machine learning. This second edition gives a comprehensive treatment of stochastic approximation algorithms based on the ordinary differential equation (ODE) approach which analyses the algorithm in terms of a limiting ODE. It has a streamlined treatment of the classical convergence analysis and includes several recent developments such as concentration bounds, avoidance of traps, stability tests, distributed and asynchronous schemes, multiple time scales, general noise models, etc., and a category-wise exposition of many important applications. It is also a useful reference for researchers and practitioners in the field. This book serves as an advanced text for a graduate course on stochastic algorithms for the students of probability and statistics, engineering, economics and machine learning. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Published by Springer Nature Singapore, Springer Nature Singapore, 2024
ISBN 10: 9819982766 ISBN 13: 9789819982769
Language: English
Seller: AHA-BUCH GmbH, Einbeck, Germany
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Add to basketBuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book serves as an advanced text for a graduate course on stochastic algorithms for the students of probability and statistics, engineering, economics and machine learning. This second edition gives a comprehensive treatment of stochastic approximation algorithms based on the ordinary differential equation (ODE) approach which analyses the algorithm in terms of a limiting ODE. It has a streamlined treatment of the classical convergence analysis and includes several recent developments such as concentration bounds, avoidance of traps, stability tests, distributed and asynchronous schemes, multiple time scales, general noise models, etc., and a category-wise exposition of many important applications. It is also a useful reference for researchers and practitioners in the field.
Seller: Books Puddle, New York, NY, U.S.A.
Condition: New. 2nd ed. 2023 edition NO-PA16APR2015-KAP.
Seller: Revaluation Books, Exeter, United Kingdom
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Add to basketHardcover. Condition: Brand New. 2nd edition. 288 pages. 9.25x6.10x0.71 inches. In Stock.
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Add to basketHindustan Book Agency, New Delhi 2008. ix, 164, (2) pp. Hardcover. Fine condition. (Text and Readings in Mathematics 48).
Published by Cambridge University Press, 2008
ISBN 10: 0521515920 ISBN 13: 9780521515924
Language: English
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Add to basketHardback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 410.
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Add to basketHardcover. Condition: Brand New. 1st edition. 164 pages. 9.25x6.00x0.50 inches. In Stock. This item is printed on demand.
Published by Springer Nature Singapore, Springer Nature Singapore Feb 2025, 2025
ISBN 10: 9819982790 ISBN 13: 9789819982790
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Add to basketTaschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book serves as an advanced text for a graduate course on stochastic algorithms for the students of probability and statistics, engineering, economics and machine learning. This second edition gives a comprehensive treatment of stochastic approximation algorithms based on the ordinary differential equation (ODE) approach which analyses the algorithm in terms of a limiting ODE. It has a streamlined treatment of the classical convergence analysis and includes several recent developments such as concentration bounds, avoidance of traps, stability tests, distributed and asynchronous schemes, multiple time scales, general noise models, etc., and a category-wise exposition of many important applications. It is also a useful reference for researchers and practitioners in the field. 292 pp. Englisch.
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Published by Springer Nature Singapore, Springer Nature Singapore Feb 2024, 2024
ISBN 10: 9819982766 ISBN 13: 9789819982769
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Add to basketBuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book serves as an advanced text for a graduate course on stochastic algorithms for the students of probability and statistics, engineering, economics and machine learning. This second edition gives a comprehensive treatment of stochastic approximation algorithms based on the ordinary differential equation (ODE) approach which analyses the algorithm in terms of a limiting ODE. It has a streamlined treatment of the classical convergence analysis and includes several recent developments such as concentration bounds, avoidance of traps, stability tests, distributed and asynchronous schemes, multiple time scales, general noise models, etc., and a category-wise exposition of many important applications. It is also a useful reference for researchers and practitioners in the field. 292 pp. Englisch.
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