Hard thresholding with norm constraints

Anastasios Kyrillidis, Gilles Puy, Volkan Cevher

Abstract: We introduce a new sparse recovery paradigm, called NORMED PURSUITS, where efficient algorithms from combinatorial and convex optimization interface for interpretable and model-based solutions. Synthetic and real data experiments illustrate that NORMED PURSUITS can significantly enhance the performance of both hard thresholding methods and convex solvers in sparse recovery.