Greedy approximation:
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 ty...
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2011
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| Schriftenreihe: | Cambridge monographs on applied and computational mathematics
20 |
| Links: | https://doi.org/10.1017/CBO9780511762291 |
| Zusammenfassung: | 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. |
| Umfang: | 1 Online-Ressource (xiv, 418 Seiten) |
| ISBN: | 9780511762291 |
Internformat
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| spelling | Temlyakov, Vladimir 1953- Greedy approximation Vladimir Temlyakov Cambridge Cambridge University Press 2011 1 Online-Ressource (xiv, 418 Seiten) txt c cr Cambridge monographs on applied and computational mathematics 20 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. Erscheint auch als Druck-Ausgabe 9781107003378 |
| spellingShingle | Temlyakov, Vladimir 1953- Greedy approximation |
| title | Greedy approximation |
| title_auth | Greedy approximation |
| title_exact_search | Greedy approximation |
| title_full | Greedy approximation Vladimir Temlyakov |
| title_fullStr | Greedy approximation Vladimir Temlyakov |
| title_full_unstemmed | Greedy approximation Vladimir Temlyakov |
| title_short | Greedy approximation |
| title_sort | greedy approximation |
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