Recipes on hard thresholding methods

Abstract

We present and analyze a new set of sparse recovery algorithms within the class of hard thresholding methods. We provide optimal strategies on how to set up these algorithms via basic “ingredients” for different configurations to achieve complexity vs. accuracy tradeoffs. Simulation results demonstrate notable performance improvements compared to state-of-the-art algorithms both in terms of data reconstruction and computational complexity.