Provides proximal operator evaluation routines and proximal optimization algorithms, such as (accelerated) proximal gradient methods and alternating direction method of multipliers (ADMM), for non-smooth/non-differentiable objective functions.
linearized ADMM is able to solve problems of the form $\min f(x) + g(Ax)$ by means of linearizing the augmented Lagrangian. These type of optimizations occur e.g. in generalized Lasso problems.
linearized ADMM is able to solve problems of the form $\min f(x) + g(Ax)$ by means of linearizing the augmented Lagrangian. These type of optimizations occur e.g. in generalized Lasso problems.