Scientific Computational Imaging COde
-
Updated
Jul 11, 2024 - Python
Scientific Computational Imaging COde
Test Cases for Regularized Optimization
Proximal algorithms for nonsmooth optimization in Julia
Proximal operators for nonsmooth optimization in Julia
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 Matlab convex optimization toolbox using proximal splitting methods
An efficient GPU-compatible library built on PyTorch, offering a wide range of proximal operators and constraints for optimization and machine learning tasks.
A Python convex optimization package using proximal splitting methods
Fortran code implementing Newton-like algorithms for proximal mapping of total variation.
Codes of `Tensor Robust Principal Component Analysis` expreiments. Besides, this repo is for my own convex optimization assignments, so do not copy for your assignments !!!
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
New Matrix Factorization Algorithms based on Bregman Proximal Gradient: BPG-MF, CoCaIn BPG-MF, BPG-MF-WB
Modeling language and tools for constrained, structured optimization problems
Hybrid Approach to Sparse Group Fused Lasso
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
Nonlinear Power Method for Computing Eigenvectors of Proximal Operators and Neural Networks
Implementation of "Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems"
Add a description, image, and links to the proximal-operators topic page so that developers can more easily learn about it.
To associate your repository with the proximal-operators topic, visit your repo's landing page and select "manage topics."