Computational Mathematics and Applications Seminar

5 December 2019
14:00
Clarice Poon
Abstract

I will present an analysis of a continuous version of the compressed sensing problem, where the l^1 norm is replaced by the total variation of measures, and one aims to recover the positions and amplitudes of Dirac masses. We show that provided that the Diracs are sufficiently separated under a Fisher metric (which accounts for the geometry of the problem), stable recovery can be achieved when the number of random samples scales linearly with sparsity (up to log factors). This is joint work with Nicolas Keriven and Gabriel Peyre.

  • Computational Mathematics and Applications Seminar