  Screening rules were recently introduced as a technique for explicitly
identifying active structures such as sparsity, in optimization problem arising
in machine learning. This has led to new methods of acceleration based on a
substantial dimension reduction. We show that screening rules stem from a
combination of natural properties of subdifferential sets and optimality
conditions, and can hence be understood in a unified way. Under mild
assumptions, we analyze the number of iterations needed to identify the optimal
active set for any converging algorithm. We show that it only depends on its
convergence rate.
