Constructive Approximation

, Volume 28, Issue 3, pp 253–263

A Simple Proof of the Restricted Isometry Property for Random Matrices

  • Richard Baraniuk
  • Mark Davenport
  • Ronald DeVore
  • Michael Wakin
Article

Abstract

We give a simple technique for verifying the Restricted Isometry Property (as introduced by Candès and Tao) for random matrices that underlies Compressed Sensing. Our approach has two main ingredients: (i) concentration inequalities for random inner products that have recently provided algorithmically simple proofs of the Johnson–Lindenstrauss lemma; and (ii) covering numbers for finite-dimensional balls in Euclidean space. This leads to an elementary proof of the Restricted Isometry Property and brings out connections between Compressed Sensing and the Johnson–Lindenstrauss lemma. As a result, we obtain simple and direct proofs of Kashin’s theorems on widths of finite balls in Euclidean space (and their improvements due to Gluskin) and proofs of the existence of optimal Compressed Sensing measurement matrices. In the process, we also prove that these measurements have a certain universality with respect to the sparsity-inducing basis.

Keywords

Compressed sensing Sampling Random matrices Concentration inequalities 

Mathematics Subject Classification (2000)

15N2 15A52 60F10 94A12 94A20 

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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Richard Baraniuk
    • 1
  • Mark Davenport
    • 1
  • Ronald DeVore
    • 2
  • Michael Wakin
    • 3
  1. 1.Department of Electrical and Computer EngineeringRice UniversityHoustonUSA
  2. 2.Industrial Mathematics Institute, Department of Mathematics and StatisticsUniversity of South CarolinaColumbiaUSA
  3. 3.Department of Electrical Engineering and Computer ScienceUniversity of MichiganAnn ArborUSA

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