Matlab and Python toolbox for fast Total Variation proximity operators

Proximal operators for nonsmooth optimization in Julia

Proximal algorithms for nonsmooth optimization in Julia

A Python convex optimization package using proximal splitting methods

Scientific Computational Imaging COde

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

A Matlab convex optimization toolbox using proximal splitting methods

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

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

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

A Julia package for manipulation of univariate piecewise quadratic functions.

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

Primal-Dual Solver for Inverse Problems

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

Test Cases for Regularized Optimization

Hybrid Approach to Sparse Group Fused Lasso

Nonlinear Power Method for Computing Eigenvectors of Proximal Operators and Neural Networks

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

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

CoCaIn BPG escapes Spurious Stationary Points