  Finding the most sparse solution to the underdetermined system
$\mathbf{y}=\mathbf{Ax}$, given a tolerance, is known to be NP-hard. A popular
way to approximate a sparse solution is by using Greedy Pursuit algorithms, and
Orthogonal Matching Pursuit (OMP) is one of the most widely used such
solutions. For this paper, we implemented an efficient implementation of OMP
that leverages Cholesky inverse properties as well as the power of Graphics
Processing Units (GPUs) to deliver up to 200x speedup over the OMP
implementation found in Scikit-Learn.
