Polynomial optimization problem solver. Uses relaxation to convert the problem into Semidefinite programming. Can be also used just as Semidefinite programming solver.
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Updated
Jan 3, 2018 - Python
Polynomial optimization problem solver. Uses relaxation to convert the problem into Semidefinite programming. Can be also used just as Semidefinite programming solver.
Master's thesis "Semidefinite Programming for Geometric Problems in Computer Vision".
Code to output SDP file for use in RDM mechanics.
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)
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!)
Representability is a library to work with linearly constrained mathematical programs over tensors.
A collection of semidefinite programs that can be randomly generated. Taken from various applications.
Max Edge Weighted Clique Problem with multiple choice contrants solved with semidefinite programming
Bayesian Optimization of Combinatorial Structures
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
The code for large margin metric learning for nearest neighbor classification and its acceleration using triplet mining and stratified sampling
An open-source interface to use the multiple-precision solver SDPA-GMP with YALMIP
Parser for CVXR to solve the Gaussian MLE problem with added constraints.
LipSDP - Lipschitz Estimation for Neural Networks
This repo involves research on quantum algorithms for various convex optimization problems.
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.
An open-source add-on for YALMIP to solve optimisation problems with polynomial quadratic integral inequality constraints.
Semidefinite Programming with Homotopy Conditional Gradient Method (HCGM) and Vu-Condat methods for solving two problems: Fashion-MNIST classification using k-means clustering and geometric embedding for the Sparsest Cut Problem.
Convex relaxation techniques applied to clustering
Solver for Large-Scale Rank-One Semidefinite Relaxations
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