Proximal Gradient

The proximal gradient method is used especially when an objective function contains both a smooth differentiable part and a sharp or non-smooth component. Terms such as L1 regularization, which encourage sparsity, are classic examples. The method combines a standard gradient step with a proximal operator to produce solutions. This allows optimization while also preserving certain structural properties. It is particularly valuable in sparse modeling and modern regularized optimization problems.