Theoretical Foundations and Numerical Methods for Sparse Recovery

Massimo Fornasier

ISBN 10: 3110226146 / ISBN 13: 9783110226140

Published by De Gruyter, DE, 2010

About this Item

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.

Seller Inventory # LU-9783110226140

Bibliographic Details

• Title: Theoretical Foundations and Numerical Methods for Sparse Recovery
• Publisher: De Gruyter, DE
• Publication Date: 2010
• Language: English
• ISBN 10: 3110226146
• ISBN 13: 9783110226140
• Binding: Hardback
• Condition: New

About this title

Synopsis

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

About the Author

Massimo Fornasier, Johann Radon Institute for Computational and Applied Mathematics, Linz, Austria.