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.

Mathematical Programming in JAX

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.

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

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

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

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

Frank--Wolfe algorithms for PDE-constrained optimization

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

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

Solver for minimization problems over the l1-ball

Optimization Algorithms for educational purposes.

Approximate Bregman proximal gradient algorithm