Dedicated to Vladimir Vapnik with esteem and gratitude for his fundamental contribution to Machine Learning. Abstract During the past years there has been an explosion of interest in learning method based on sparsity regularization. In this paper, we discuss a general class of such methods, in which the regularizer can be expressed as the composition of a convex function ω with a linear function. This setting includes several methods such the group Lasso, the Fused Lasso, multi-task learning

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... many more. We present a general approach for solving regularization problems of this kind, under the assumption that the proximity operator of the function ω is available. Furthermore, we comment on the application of this approach to support vector machines, a technique pioneered by the groundbreaking work of Vladimir Vapnik.