Theoretical Foundations and Numerical Methods for Sparse Recovery
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Edited by:
Massimo Fornasier
About this book
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
Author / Editor information
Massimo Fornasier, Johann Radon Institute for Computational and Applied Mathematics, Linz, Austria.
Topics
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Frontmatter
I -
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Table of Contents
VII -
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Compressive Sensing and Structured Random Matrices
1 -
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Numerical Methods for Sparse Recovery
93 -
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Sparse Recovery in Inverse Problems
201 -
Requires Authentication UnlicensedLicensed
An Introduction to Total Variation for Image Analysis
263
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Manufacturer information:
Walter de Gruyter GmbH
Genthiner Straße 13
10785 Berlin
productsafety@degruyterbrill.com
