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Theoretical foundations and numerical methods for sparse recovery [[electronic resource] /] / edited by Massimo Fornasier



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Titolo: Theoretical foundations and numerical methods for sparse recovery [[electronic resource] /] / edited by Massimo Fornasier Visualizza cluster
Pubblicazione: Berlin ; ; New York, : De Gruyter, c2010
Descrizione fisica: 1 online resource (350 p.)
Disciplina: 512.9/434
Soggetto topico: Sparse matrices
Equations - Numerical solutions
Differential equations, Partial - Numerical solutions
Soggetto non controllato: Image Processing
Numerical Solution of Partial Differential Inverse Problems
Signal Processing
Sparsity
Classificazione: SK 920
Altri autori: FornasierMassimo  
Note generali: Description based upon print version of record.
Nota di bibliografia: Includes bibliographical references and index.
Nota di contenuto: Frontmatter -- Table of Contents -- Compressive Sensing and Structured Random Matrices -- Numerical Methods for Sparse Recovery -- Sparse Recovery in Inverse Problems -- An Introduction to Total Variation for Image Analysis
Sommario/riassunto: The present collection is the very first contribution of this type in the field of sparse recovery. Compressed sensing is one of the important facets of the broader concept presented in the book, which by now has made connections with other branches such as mathematical imaging, inverse problems, numerical analysis and simulation. The book consists of four lecture notes of courses given at the Summer School on "Theoretical Foundations and Numerical Methods for Sparse Recovery" held at the Johann Radon Institute for Computational and Applied Mathematics in Linz, Austria, in September 2009. This unique collection will be of value for a broad community and may serve as a textbook for graduate courses. From the contents: "Compressive Sensing and Structured Random Matrices" by Holger Rauhut "Numerical Methods for Sparse Recovery" by Massimo Fornasier "Sparse Recovery in Inverse Problems" by Ronny Ramlau and Gerd Teschke "An Introduction to Total Variation for Image Analysis" by Antonin Chambolle, Vicent Caselles, Daniel Cremers, Matteo Novaga and Thomas Pock
Titolo autorizzato: Theoretical foundations and numerical methods for sparse recovery  Visualizza cluster
ISBN: 1-282-72302-2
9786612723025
3-11-022615-4
Formato: Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione: Inglese
Record Nr.: 9910784934003321
Lo trovi qui: Univ. Federico II
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Serie: Radon series on computational and applied mathematics ; ; 9.