proximal-operators

Matlab and Python toolbox for fast Total Variation proximity operators

Scientific Computational Imaging COde

Proximal operators for nonsmooth optimization in Julia

Proximal algorithms for nonsmooth optimization in Julia

A Python convex optimization package using proximal splitting methods

A Matlab convex optimization toolbox using proximal splitting methods

MATLAB library of gradient descent algorithms for sparse modeling: Version 1.0.3

Python routines to compute the Total Variation (TV) of 2D, 3D and 4D images on CPU & GPU. Compatible with proximal algorithms (ADMM, Chambolle & Pock, ...)

Implementation of "Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems"

A Julia package that solves Linearly Constrained Separable Optimization Problems using ADMM.

A Julia package for manipulation of univariate piecewise quadratic functions.

Primal-Dual Solver for Inverse Problems

MATLAB implementations of a variety of machine learning/signal processing algorithms.

New Matrix Factorization Algorithms based on Bregman Proximal Gradient: BPG-MF, CoCaIn BPG-MF, BPG-MF-WB

An efficient GPU-compatible library built on PyTorch, offering a wide range of proximal operators and constraints for optimization and machine learning tasks.

Hybrid Approach to Sparse Group Fused Lasso

Test Cases for Regularized Optimization

A Python package which implements the Elastic Net using the (accelerated) proximal gradient method.

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.

CoCaIn BPG escapes Spurious Stationary Points