© 2017

Compressed Sensing and its Applications

Second International MATHEON Conference 2015

  • Holger Boche
  • Giuseppe Caire
  • Robert Calderbank
  • Maximilian März
  • Gitta Kutyniok
  • Rudolf Mathar

Part of the Applied and Numerical Harmonic Analysis book series (ANHA)

Table of contents

  1. Front Matter
    Pages i-xix
  2. Dmitry Batenkov, Yaniv Romano, Michael Elad
    Pages 1-53
  3. Tamir Bendory, Robert Beinert, Yonina C. Eldar
    Pages 55-91
  4. Ben Adcock, Simone Brugiapaglia, Clayton G. Webster
    Pages 93-124
  5. Naman Agarwal, Afonso S. Bandeira, Konstantinos Koiliaris, Alexandra Kolla
    Pages 125-162
  6. Rizwan Ahmad, Philip Schniter
    Pages 163-195
  7. Afonso S. Bandeira, Dustin G. Mixon, Benjamin Recht
    Pages 197-220
  8. Sara Botelho-Andrade, Peter G. Casazza, Dorsa Ghoreishi, Shani Jose, Janet C. Tremain
    Pages 221-234
  9. Anna Breger, Martin Ehler, Manuel Gräf, Thomas Peter
    Pages 235-259
  10. Benjamin Huber, Reinhold Schneider, Sebastian Wolf
    Pages 261-290
  11. Srđan Kitić, Siouar Bensaid, Laurent Albera, Nancy Bertin, Rémi Gribonval
    Pages 291-332
  12. Felix Krahmer, Christian Kruschel, Michael Sandbichler
    Pages 333-358
  13. Yann Traonmilin, Gilles Puy, Rémi Gribonval, Mike E. Davies
    Pages 359-384
  14. Back Matter
    Pages 385-388

About this book


This contributed volume contains articles written by the plenary and invited speakers from the second international MATHEON Workshop 2015 that focus on applications of compressed sensing. Article authors address their techniques for solving the problems of compressed sensing, as well as connections to related areas like detecting community-like structures in graphs, curbatures on Grassmanians, and randomized tensor train singular value decompositions. Some of the novel applications covered include dimensionality reduction, information theory, random matrices, sparse approximation, and sparse recovery. 

This book is aimed at both graduate students and researchers in the areas of applied mathematics, computer science, and engineering, as well as other applied scientists exploring the potential applications for the novel methodology of compressed sensing. An introduction to the subject of compressed sensing is also provided for researchers interested in the field who are not as familiar with it. 


Compressed Sensing Dimensionality Reduction Information Theory Random Matrices Sparse Approximation Sparse Recovery Fourier phase retrieval Hilbert spaces stochastic block model sparse probability measures

Editors and affiliations

  • Holger Boche
    • 1
  • Giuseppe Caire
    • 2
  • Robert Calderbank
    • 3
  • Maximilian März
    • 4
  • Gitta Kutyniok
    • 5
  • Rudolf Mathar
    • 6
  1. 1.Fakultät für Elektrotechnik und InformationstechnikTechnische Universität MünchenMunich, BavariaGermany
  2. 2.Institut für TelekommunikationssystemeTechnische Universität BerlinBerlinGermany
  3. 3.Department of Electrical & Computer EngineeringDuke UniversityDurhamUSA
  4. 4.Institut für MathematikTechnische Universität BerlinBerlinGermany
  5. 5.Institut für MathematikTechnische Universität BerlinBerlinGermany
  6. 6.Lehrstuhl und Institute für StatistikRWTH AachenAachenGermany

Bibliographic information