Greedy approximation
 Responsibility
 Vladimir Temlyakov.
 Language
 English.
 Imprint
 Cambridge ; New York : Cambridge University Press, 2011.
 Physical description
 xiv, 418 p. ; 24 cm.
 Series
 Cambridge monographs on applied and computational mathematics ; 20.
Access
Creators/Contributors
 Author/Creator
 Temlyakov, Vladimir, 1953
Contents/Summary
 Bibliography
 Includes bibliographical references (p. 405414)and index.
 Contents

 Preface 1. Greedy approximation with respect to bases 2. Greedy approximation with respect to dictionaries: Hilbert spaces 3. The entropy 4. Approximation in learning theory 5. Approximation in compressed sensing 6. Greedy approximation with respect to dictionaries: Banach spaces References Index.
 (source: Nielsen Book Data)
 Publisher's Summary
 This first book on greedy approximation gives a systematic presentation of the fundamental results. It also contains an introduction to two hot topics in numerical mathematics: learning theory and compressed sensing. Nonlinear approximation is becoming increasingly important, especially since two types are frequently employed in applications: adaptive methods are used in PDE solvers, while mterm approximation is used in image/signal/data processing, as well as in the design of neural networks. The fundamental question of nonlinear approximation is how to devise good constructive methods (algorithms) and recent results have established that greedy type algorithms may be the solution. The author has drawn on his own teaching experience to write a book ideally suited to graduate courses. The reader does not require a broad background to understand the material. Important open problems are included to give students and professionals alike ideas for further research.
(source: Nielsen Book Data)  Supplemental links
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Subjects
 Subject
 Approximation theory.
Bibliographic information
 Publication date
 2011
 Series
 Cambridge monographs on applied and computational mathematics ; 20
 ISBN
 9781107003378 (hardback)
 1107003377 (hardback)