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
Hybrid Approach to Sparse Group Fused Lasso
Test Cases for Regularized Optimization
Nonlinear Power Method for Computing Eigenvectors of Proximal Operators and Neural Networks
CoCaIn BPG escapes Spurious Stationary Points
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.
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