  We consider the problem of designing efficient regularization algorithms when
regularization is encoded by a (strongly) convex functional. Unlike classical
penalization methods based on a relaxation approach, we propose an iterative
method where regularization is achieved via early stopping. Our results show
that the proposed procedure achieves the same recovery accuracy as penalization
methods, while naturally integrating computational considerations. An empirical
analysis on a number of problems provides promising results with respect to the
state of the art.
