An IHT Algorithm for Sparse Recovery From Subexponential Measurements Academic Article uri icon


  • © 1994-2012 IEEE. A matrix whose entries are independent subexponential random variables is not likely to satisfy the classical restricted isometry property in the optimal regime of parameters. However, it is known that uniform sparse recovery is still possible with high probability in the optimal regime if ones uses ℓ1-minimization as a recovery algorithm. We show in this letter that such a statement remains valid if one uses a new variation of iterative hard thresholding as a recovery algorithm. The argument is based on a modified restricted isometry property featuring the ℓ1-norm as the inner norm.

author list (cited authors)

  • Foucart, S., & Lecue, G.

citation count

  • 5

publication date

  • July 2017