Matlab and Python toolbox for fast Total Variation proximity operators
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Updated
Feb 20, 2020 - C++
Matlab and Python toolbox for fast Total Variation proximity operators
Proximal operators for nonsmooth optimization in Julia
Proximal algorithms for nonsmooth optimization in Julia
Scientific Computational Imaging COde
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
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.
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
Test Cases for Regularized Optimization
Hybrid Approach to Sparse Group Fused Lasso
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.
Nonlinear Power Method for Computing Eigenvectors of Proximal Operators and Neural Networks
CoCaIn BPG escapes Spurious Stationary Points
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