We propose and analyze acceleration schemes for hard thresholding methods with applications to sparse approximation
in linear inverse systems. Our acceleration schemes fuse combinatorial, sparse projection algorithms
with convex optimization algebra to provide computationally efficient and robust sparse recovery methods. We
compare and contrast the (dis)advantages of the proposed schemes with the state-of-the-art, not only within
hard thresholding methods, but also within convex sparse recovery algorithms.