 Proximal gradient methods for learning

These methods optimize a smooth loss plus a term with a tractable proximal operator, such as a sparsity regularizer or a convex-set indicator. The basic proximal gradient method combines a gradient step with a proximal step; projected gradient and accelerated variants are related members of the family.