Sparse Recovery with Random Matrices

  • Simon Foucart
  • Holger Rauhut
Part of the Applied and Numerical Harmonic Analysis book series (ANHA)


In this chapter, the restricted isometry property, which guarantees the uniform recovery of sparse vectors via a variety of methods, is proved to hold with high probability for subgaussian random matrices provided the number of rows (i.e., measurements) scales like the sparsity times a logarithmic factor. For Gaussian matrices, precise estimates for the required number of measurements (including optimal or at least small values of the constants) are given both in the setting of nonuniform recovery and of uniform recovery. In the latter case, this is first done via an estimate of the restricted isometry constants and then directly through the null space property. Finally, a close relation between the restricted isometry property and the Johnson–Lindenstrauss lemma is uncovered.


isotropic subgaussian vectors subgaussian matrices concentration inequality restricted isometry property universality uniform recovery 1-minimization null space property nonuniform recovery Gaussian width Gordon’s escape through the mesh Johnson–Lindenstrauss lemma 


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Simon Foucart
    • 1
  • Holger Rauhut
    • 2
  1. 1.Department of MathematicsDrexel UniversityPhiladelphiaUSA
  2. 2.Lehrstuhl C für Mathematik (Analysis)RWTH Aachen UniversityAachenGermany

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