proximal-algorithms

The Advanced Proximal Optimization Toolbox

Proximal operators for nonsmooth optimization in Julia

Proximal algorithms for nonsmooth optimization in Julia

A Python convex optimization package using proximal splitting methods

Proximal optimization in pure python

A Matlab convex optimization toolbox using proximal splitting methods

Pycsou is a Python 3 package for solving linear inverse problems with state-of-the-art proximal algorithms. The software implements in a highly modular way the main building blocks -cost functionals, penalty terms and linear operators- of generic penalised convex optimisation problems.

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

Scientific Computational Imaging COde

PyProximal – Proximal Operators and Algorithms in Python

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

Cut-pursuit with preconditioned forward-Douglas-Rachford for regularization of classical functionals by graph total variation

Nonconvex Exterior Point Operator Splitting

A set of notebooks related to convex optimization, variational inference and numerical methods for signal processing, machine learning, deep learning, graph analysis, bayesian programming, statistics or astronomy.

A small library implementing phase retrieval algorithms for 2D images.

Coordinate and Incremental Aggregated Optimization Algorithms

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

Newton-type accelerated proximal gradient method in Julia

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

Proximal operators for use with RegularizedOptimization