Randomized Stochastic Approximation

Multidimensional stochastic optimization plays an important role in the analysis and control of many technical systems. Randomized algorithms of stochastic approximation with perturbed input have been suggested for solving the challenging multidimensional

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Oleg Granichin Zeev (Vladimir) Volkovich Dvora Toledano-Kitai

Randomized Algorithms in Automatic Control and Data Mining

Intelligent Systems Reference Library Volume 67

Series editors Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland e-mail: [email protected] Lakhmi C. Jain, University of Canberra, Canberra, Australia e-mail: [email protected]

For further volumes: http://www.springer.com/series/8578

About this Series The aim of this series is to publish a Reference Library, including novel advances and developments in all aspects of Intelligent Systems in an easily accessible and well structured form. The series includes reference works, handbooks, compendia, textbooks, well-structured monographs, dictionaries, and encyclopedias. It contains well integrated knowledge and current information in the field of Intelligent Systems. The series covers the theory, applications, and design methods of Intelligent Systems. Virtually all disciplines such as engineering, computer science, avionics, business, e-commerce, environment, healthcare, physics and life science are included.

Oleg Granichin · Zeev (Vladimir) Volkovich Dvora Toledano-Kitai

Randomized Algorithms in Automatic Control and Data Mining

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Oleg Granichin Department of Mathematics and Mechanics Saint Petersburg State University St. Petersburg Russia

Dvora Toledano-Kitai Department of Software Engineering ORT Braude College Karmiel Israel

Zeev (Vladimir) Volkovich Department of Software Engineering ORT Braude College Karmiel Israel

ISSN 1868-4394 ISBN 978-3-642-54785-0 DOI 10.1007/978-3-642-54786-7

ISSN 1868-4408 (electronic) ISBN 978-3-642-54786-7 (eBook)

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