C package that solves convex cone problems via operator splitting

COSMO: An ADMM-based solver for convex conic optimisation problems (LP, QP, SOCP, SDP, ExpCP, PowCP). Implemented in Julia

An implementation of the SE-Sync algorithm for synchronization over the special Euclidean group.

Bayesian Optimization of Combinatorial Structures

Semidefinite programming optimization solver

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.

LipSDP - Lipschitz Estimation for Neural Networks

Polynomial optimization problem solver. Uses relaxation to convert the problem into Semidefinite programming. Can be also used just as Semidefinite programming solver.

MICO: Mutual Information and Conic Optimization for feature selection

Representability is a library to work with linearly constrained mathematical programs over tensors.

Irene is a python package that aims to be a toolkit for global optimization problems that can be realized algebraically. It generalizes Lasserre's Relaxation method to handle theoretically any optimization problem with bounded feasibility set. The method is based on solutions of generalized truncated moment problems over commutative real algebras.

A Coq tactic for proving multivariate inequalities using SDP solvers

An open-source add-on for YALMIP to solve optimisation problems with polynomial quadratic integral inequality constraints.

Master's thesis "Semidefinite Programming for Geometric Problems in Computer Vision".

An open-source interface to use the multiple-precision solver SDPA-GMP with YALMIP

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.

Compressed Sensing project - ENSAE ParisTech

A collection of semidefinite programs that can be randomly generated. Taken from various applications.

This code can be used to reproduce most results from the paper " Exact Worst-case Performance of First-order Methods for Composite Convex Optimization" (Published in SIAM Journal on Optimization). (newer version available in the PESTO toolbox!)

Code for symbolic validations of the PEP-based proofs for the article " Worst-case convergence analysis of gradient and Newton methods through semidefinite programming performance estimation" authored by E. de Klerk, F. Glineur and A. Taylor

Code for paper "Searching for polarization in signed graphs: a local spectral approach" (published in WebConf 2020)

Max Edge Weighted Clique Problem with multiple choice contrants solved with semidefinite programming

This code can be used to reproduce all results from the paper "Smooth strongly convex interpolation and exact worst-case performance of first-order methods" (published in Mathematical Programming). (newer version available in the PESTO toolbox)

Code to output SDP file for use in RDM mechanics.