Sparse linear models have received a lot of attention in statistics, machine learning, signal processing, computer vision, bio-informatics, and neuroscience. Regularization functions encouraging the solution of a problem to be sparse---that is, to have many zero entries, have proven to be useful for different (equally important) reasons. Parcimony can be a good a priori when the ``true'' solution...