The Resource A mathematical introduction to compressive sensing, Simon Foucart, Holger Rauhut
A mathematical introduction to compressive sensing, Simon Foucart, Holger Rauhut
Resource Information
The item A mathematical introduction to compressive sensing, Simon Foucart, Holger Rauhut represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.This item is available to borrow from 1 library branch.
Resource Information
The item A mathematical introduction to compressive sensing, Simon Foucart, Holger Rauhut represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.
This item is available to borrow from 1 library branch.
 Summary
 At the intersection of mathematics, engineering, and computer science sits the thriving field of compressive sensing. Based on the premise that data acquisition and compression can be performed simultaneously, compressive sensing finds applications in imaging, signal processing, and many other domains. In the areas of applied mathematics, electrical engineering, and theoretical computer science, an explosion of research activity has already followed the theoretical results that highlighted the efficiency of the basic principles. The elegant ideas behind these principles are also of independent interest to pure mathematicians. A Mathematical Introduction to Compressive Sensing gives a detailed account of the core theory upon which the field is build. Key features include: The first textbook completely devoted to the topic of compressive sensing ; Comprehensive treatment of the subject, including background material from probability theory, detailed proofs of the main theorems, and an outline of possible applications; Numerous exercises designed to help students understand the material; An extensive bibliography with over 500 references that guide researchers through the literature.With only moderate prerequisites, A Mathematical Introduction to Compressive Sensing is an excellent textbook for graduate courses in mathematics, engineering, and computer science. It also serves as a reliable resource for practitioners and researchers in these disciplines who want to acquire a careful understanding of the subject
 Language
 eng
 Extent
 xviii, 625 pages
 Contents

 1. An invitation to compressive sensing
 2. Sparse solutions of underdetermined systems
 3. Basic algorithms
 4. Basis pursuit
 5. Coherence
 6. Restricted isometry property
 7. Basic tools from probability theory
 8. Advanced tools from probability theory
 9. Sparse recovery with random matrices
 10. Gelfand widths of l1balls
 11. Instance optimality and quotient property
 12. Random sampling in bounded orthonormal systems
 13. Lossless expanders in compressive sensing
 14. Recovery of random signals using deterministic matrices
 15. Algorithms for l1minimization
 Appendix A. Matrix analysis
 B. Convex analysis
 C. Miscellanea
 Isbn
 9780817649470
 Label
 A mathematical introduction to compressive sensing
 Title
 A mathematical introduction to compressive sensing
 Statement of responsibility
 Simon Foucart, Holger Rauhut
 Language
 eng
 Summary
 At the intersection of mathematics, engineering, and computer science sits the thriving field of compressive sensing. Based on the premise that data acquisition and compression can be performed simultaneously, compressive sensing finds applications in imaging, signal processing, and many other domains. In the areas of applied mathematics, electrical engineering, and theoretical computer science, an explosion of research activity has already followed the theoretical results that highlighted the efficiency of the basic principles. The elegant ideas behind these principles are also of independent interest to pure mathematicians. A Mathematical Introduction to Compressive Sensing gives a detailed account of the core theory upon which the field is build. Key features include: The first textbook completely devoted to the topic of compressive sensing ; Comprehensive treatment of the subject, including background material from probability theory, detailed proofs of the main theorems, and an outline of possible applications; Numerous exercises designed to help students understand the material; An extensive bibliography with over 500 references that guide researchers through the literature.With only moderate prerequisites, A Mathematical Introduction to Compressive Sensing is an excellent textbook for graduate courses in mathematics, engineering, and computer science. It also serves as a reliable resource for practitioners and researchers in these disciplines who want to acquire a careful understanding of the subject
 Cataloging source
 CDX
 http://library.link/vocab/creatorName
 Foucart, Simon
 Illustrations
 illustrations
 Index
 index present
 LC call number

 TK5102.9
 TK
 LC item number
 .F673 2013
 Literary form
 non fiction
 Nature of contents
 bibliography
 http://library.link/vocab/relatedWorkOrContributorName
 Rauhut, Holger
 Series statement
 Applied and numerical harmonic analysis
 http://library.link/vocab/subjectName

 Signal processing
 Signal processing
 Label
 A mathematical introduction to compressive sensing, Simon Foucart, Holger Rauhut
 Bibliography note
 Includes bibliographical references (pages 593615) and index
 Carrier category
 volume
 Carrier category code
 nc
 Carrier MARC source
 rdacarrier.
 Content category
 text
 Content type code
 txt
 Content type MARC source
 rdacontent.
 Contents
 1. An invitation to compressive sensing  2. Sparse solutions of underdetermined systems  3. Basic algorithms  4. Basis pursuit  5. Coherence  6. Restricted isometry property  7. Basic tools from probability theory  8. Advanced tools from probability theory  9. Sparse recovery with random matrices  10. Gelfand widths of l1balls  11. Instance optimality and quotient property  12. Random sampling in bounded orthonormal systems  13. Lossless expanders in compressive sensing  14. Recovery of random signals using deterministic matrices  15. Algorithms for l1minimization  Appendix A. Matrix analysis  B. Convex analysis  C. Miscellanea
 Control code
 860992971
 Dimensions
 25 cm
 Extent
 xviii, 625 pages
 Isbn
 9780817649470
 Lccn
 2013939591
 Media category
 unmediated
 Media MARC source
 rdamedia.
 Media type code
 n
 Other physical details
 illustrations
 System control number
 (OCoLC)860992971
 Label
 A mathematical introduction to compressive sensing, Simon Foucart, Holger Rauhut
 Bibliography note
 Includes bibliographical references (pages 593615) and index
 Carrier category
 volume
 Carrier category code
 nc
 Carrier MARC source
 rdacarrier.
 Content category
 text
 Content type code
 txt
 Content type MARC source
 rdacontent.
 Contents
 1. An invitation to compressive sensing  2. Sparse solutions of underdetermined systems  3. Basic algorithms  4. Basis pursuit  5. Coherence  6. Restricted isometry property  7. Basic tools from probability theory  8. Advanced tools from probability theory  9. Sparse recovery with random matrices  10. Gelfand widths of l1balls  11. Instance optimality and quotient property  12. Random sampling in bounded orthonormal systems  13. Lossless expanders in compressive sensing  14. Recovery of random signals using deterministic matrices  15. Algorithms for l1minimization  Appendix A. Matrix analysis  B. Convex analysis  C. Miscellanea
 Control code
 860992971
 Dimensions
 25 cm
 Extent
 xviii, 625 pages
 Isbn
 9780817649470
 Lccn
 2013939591
 Media category
 unmediated
 Media MARC source
 rdamedia.
 Media type code
 n
 Other physical details
 illustrations
 System control number
 (OCoLC)860992971
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