  Significant attention has been given to minimizing a penalized least squares
criterion for estimating sparse solutions to large linear systems of equations.
The penalty is responsible for inducing sparsity and the natural choice is the
so-called $l_0$ norm. In this paper we develop a Momentumized Iterative
Shrinkage Thresholding (MIST) algorithm for minimizing the resulting non-convex
criterion and prove its convergence to a local minimizer. Simulations on large
data sets show superior performance of the proposed method to other methods.
