Linear Programming and Generalizations, Eric V. Denardo

Автор: Terras, Audrey
Название: Harmonic analysis on symmetric spaces-higher rank spaces, positive definite matrix space and generalizations
ISBN: 1493934066 ISBN-13(EAN): 9781493934065
Издательство: Springer
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Цена: 8384.00 р.
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Описание: This text is an introduction to harmonic analysis on symmetric spaces, focusing on advanced topics such as higher rank spaces, positive definite matrix space and generalizations.

Автор: Radomir Stankovic; Paul Leo Butzer; Ferenc Schipp;
Название: Dyadic Walsh Analysis from 1924 Onwards Walsh-Gibbs-Butzer Dyadic Differentiation in Science Volume 2 Extensions and Generalizations
ISBN: 9462391629 ISBN-13(EAN): 9789462391628
Издательство: Springer
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Цена: 13275.00 р.
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Описание: The second volume of the two volumes book is dedicated to various extensions and generalizations of Dyadic (Walsh) analysis and related applications.

Автор: Agranovich Mikhail
Название: Sobolev Spaces, Their Generalizations and Elliptic Problems
ISBN: 3319146475 ISBN-13(EAN): 9783319146478
Издательство: Springer
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Цена: 13275.00 р.
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Описание: This book, which is based on several courses of lectures given by the author at the Independent University of Moscow, is devoted to Sobolev-type spaces and boundary value problems for linear elliptic partial differential equations.

Автор: Mikhail S. Agranovich
Название: Sobolev Spaces, Their Generalizations and Elliptic Problems in Smooth and Lipschitz Domains
ISBN: 3319351834 ISBN-13(EAN): 9783319351834
Издательство: Springer
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Цена: 13275.00 р.
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Описание: This book, which is based on several courses of lectures given by the author at the Independent University of Moscow, is devoted to Sobolev-type spaces and boundary value problems for linear elliptic partial differential equations.

Автор: M. Schomaker; C. Radhakrishna Rao; Helge Toutenbur
Название: Linear Models and Generalizations
ISBN: 3642093531 ISBN-13(EAN): 9783642093531
Издательство: Springer
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Цена: 13969.00 р.
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Описание: Third Edition explores the theory and applications of linear models. It presents a unified theory of inference from linear models and its generalizations with minimal assumptions, using least squares theory and alternative methods of estimation and testing.