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Theoretical Foundations and Numerical Methods for Sparse Recovery: 9 (Radon Series on Computational and Applied Mathematics, 9) - Tapa dura
Sinopsis
The present collection of four lecture notes 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. This unique collection will be of value for a broad community and may serve as a textbook for graduate courses.
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Massimo Fornasier, Johann Radon Institute for Computational and Applied Mathematics, Linz, Austria.
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Theoretical Foundations and Numerical Methods for Sparse Recovery
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Gebunden. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Massimo Fornasier, Johann Radon Institute for Computational and Applied Mathematics, Linz, Austria. Nº de ref. del artículo: 4455461
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Theoretical Foundations and Numerical Methods for Sparse Recovery
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Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Nº de ref. del artículo: 9783110226140
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Theoretical Foundations and Numerical Methods for Sparse Recovery
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De Gruyter, De Gruyter Jul 2010, 2010
ISBN 10: 3110226146
ISBN 13: 9783110226140
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 352 pp. Englisch. Nº de ref. del artículo: 9783110226140
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Theoretical Foundations and Numerical Methods for Sparse Recovery (Radon Series on Computational and Applied Mathematics, 9)
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Theoretical Foundations and Numerical Methods for Sparse Recovery
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HRD. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Nº de ref. del artículo: L1-9783110226140
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Theoretical Foundations and Numerical Methods for Sparse Recovery
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De Gruyter, De Gruyter Jul 2010, 2010
ISBN 10: 3110226146
ISBN 13: 9783110226140
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Buch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 PockWalter de Gruyter GmbH, Genthiner Strasse 13, 10785 Berlin 352 pp. Englisch. Nº de ref. del artículo: 9783110226140
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Theoretical Foundations and Numerical Methods for Sparse Recovery
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Theoretical Foundations and Numerical Methods for Sparse Recovery (Radon Series on Computational and Applied Mathematics, 9)
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Walter de Gruyter, 2010
ISBN 10: 3110226146
ISBN 13: 9783110226140
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Theoretical Foundations and Numerical Methods for Sparse Recovery
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Hardback. Condición: New. 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. Nº de ref. del artículo: LU-9783110226140
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Theoretical Foundations and Numerical Methods for Sparse Recovery
Librería: Rarewaves USA United, OSWEGO, IL, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Hardback. Condición: New. 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. Nº de ref. del artículo: LU-9783110226140
Cantidad disponible: Más de 20 disponibles