CS: Sparse Recovery with Pre-Gaussian Random Matrices

Sparse Recovery with Pre-Gaussian Random Matrices by Simon Foucart, Ming-Jun Lai.The abstract reads:

We show that a matrix whose entries are independent copies of a symmetric pre-Gaussian random variable possesses, with overwhelming probability, a Modified Restricted Isometry Property in q-quasinorms for 0 \lt q \le 1/3. We then prove that, if the matrix of an underdetermined linear system of equations satisfies this property, then the sparsest solution of the system can be found using l_q-minimization.

Credit: 808caver, Moonbow over Venus, when light reflected from the Moon shines on rain at night.