1.

Record Nr.

UNINA9910824322703321

Titolo

Theoretical foundations and numerical methods for sparse recovery / / edited by Massimo Fornasier

Pubbl/distr/stampa

Berlin ; ; New York, : De Gruyter, c2010

ISBN

1-282-72302-2

9786612723025

3-11-022615-4

Edizione

[1st ed.]

Descrizione fisica

1 online resource (350 p.)

Collana

Radon series on computational and applied mathematics ; ; 9

Classificazione

SK 920

Altri autori (Persone)

FornasierMassimo

Disciplina

512.9/434

Soggetti

Sparse matrices

Equations - Numerical solutions

Differential equations, Partial - Numerical solutions

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

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