Proximal gradient methods for learning
= Proximal gradient methods for learning
{wiki}
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.