Splitting Conic Solver

Proximal operators for nonsmooth optimization in Julia

Julia implementation for various Frank-Wolfe and Conditional Gradient variants

PEPit is a package enabling computer-assisted worst-case analyses of first-order optimization methods.

Code of the Performance Estimation Toolbox (PESTO) whose aim is to ease the access to the PEP methodology for performing worst-case analyses of first-order methods in convex and nonconvex optimization. The numerical worst-case analyses from PEP can be performed just by writting the algorithms just as you would implement them.

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

Mixed-Integer Convex Programming: Branch-and-bound with Frank-Wolfe-based convex relaxations

Demonstration of the PIPG algorithm for trajectory optimization

Suite of Predictive Controllers for Industrial Embedded Systems. A Matlab toolbox for automatic code generation of solvers for MPC controllers.

Topics in Signal Processing

Frank--Wolfe algorithms for PDE-constrained optimization

The SLTP Generalized Planning Framework: Sample, Learn, Transform & Plan

Implementation and comparison of zero order vs first order method on the AdaMM (aka AMSGrad) optimizer: analysis of convergence rates and minima shape

Code to reproduce the results presented in the work "Efficient First-order Methods for Convex Minimization: a Constructive Approach" (in Mathematical Programming series A) by Y. Drori and A. Taylor.

Bregman Proximal type algorithms

Optimization Algorithms for educational purposes.

Solver for minimization problems over the l1-ball

Implementation and brief comparison of different First Order and different Proximal gradient methods, comparison of their convergence rates

Approximate Bregman proximal gradient algorithm

An open-source MATLAB benchmark for reduced-precision solver verification