A Python convex optimization package using proximal splitting methods

Proximal operators for nonsmooth optimization in Julia

Proximal algorithms for nonsmooth optimization in Julia

Proximal optimization in pure python

A Matlab convex optimization toolbox using proximal splitting methods

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

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

Nonconvex Exterior Point Operator Splitting

Newton-type accelerated proximal gradient method in Julia

A Python implementation of Goldstein et. al's FASTA algorithm for convex optimization.

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

Second-Order Convergence of Alternating Minimizations

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

CoCaIn BPG escapes Spurious Stationary Points

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

Q. Yao, J. Xu, W. Tu, Z. Zhu. Efficient Neural Architecture Search via Proximal Iterations. AAAI 2020.

Fast Inertial Algorithm for Phase Retrieval

Implementation of Collective Matrix Completion by Mokhtar Z. Alaya and Olga Klopp https://arxiv.org/abs/1807.09010

Selected machine learning projects from courses I have taken during my PhD

PNPG algorithm implementation and examples

Symbolic code (using symbolic computation toolbox of matlab) for verifying the analytical convergence rate for the proximal gradient method of the preprint "Exact worst-case convergence rates of the proximal gradient method for composite convex minimization"

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.

Coordinate descent