Temlyakov (14 Ergebnisse)

Hardcover. Zustand: Like New. First Edition. Firm and square with strong joints, just showing a few very minor rubs and bumps. Hence a non-text page is stamped 'damaged'. Despite such this book is actually in nearly new condition. Thus it looks and feels unread with contents that are crisp, fresh and tight. Size: 250mm x 180mm. Pages: xvi, 534. Fully indexed. This self-contained, systematic treatment of multivariate approximation begins with classical linear approximation, and moves on to contemporary nonlinear approximation. It covers substantial new developments in the linear approximation theory of classes with mixed smoothness, and shows how it is directly related to deep problems in other areas of mathematics.…

Hardcover. Zustand: Very Good. Unread. In stock ready to dispatch from the UK.

Zustand: Very Good.

Zustand: New. In English.

Paperback. Zustand: Brand New. 232 pages. 9.45x6.61x0.51 inches. In Stock.

Zustand: New. In English.

Zustand: New.

Zustand: New. Provides the theoretical foundations for algorithms widely used in numerical mathematics. Includes classical results, as well as the latest advances. Series: Cambridge Monographs on Applied and Computational Mathematics. Num Pages: 432 pages, illustrations. BIC Classification: PBKS. Category: (U) Tertiary Education (US: College). Dimension: 160 x 231 x 26. Weight in Grams: 754. . 2011. hardcover. . . . . Books ship from the US and Ireland. …

Hardcover. Zustand: Brand New. 1st edition. 432 pages. 9.25x6.25x1.25 inches. In Stock.

Zustand: New. In English.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 m-term 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. Vladimir Temlyakov guides the reader through the fundamental results and introduces two other hot topics in numerical mathematics: learning theory and compressed sensing. Researchers will welcome this first book on the subject. It is ideal for graduate courses and includes many important open problems that provide ideas for further research.…

Zustand: New.

Hardcover. Zustand: Brand New. 534 pages. 9.75x7.00x1.50 inches. In Stock.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This self-contained, systematic treatment of multivariate approximation begins with classical linear approximation, and moves on to contemporary nonlinear approximation. It covers substantial new developments in the linear approximation theory of classes with mixed smoothness, and shows how it is directly related to deep problems in other areas of mathematics. For example, numerical integration of these classes is closely related to discrepancy theory and to nonlinear approximation with respect to special redundant dictionaries, and estimates of the entropy numbers of classes with mixed smoothness are closely related to (in some cases equivalent to) the Small Ball Problem from probability theory. The useful background material included in the book makes it accessible to graduate students. Researchers will find that the many open problems in the theory outlined in the book provide helpful directions and guidance for their own research in this exciting and active area.…