Tools to compute the minimum semidefinite rank of a simple undirected graph
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Updated
Apr 16, 2024 - Python
Tools to compute the minimum semidefinite rank of a simple undirected graph
Code for designing sigma delta modulator loop filters with optimal properties.
The code for large margin metric learning for nearest neighbor classification and its acceleration using triplet mining and stratified sampling
This repo involves research on quantum algorithms for various convex optimization problems.
An accelerated active‑set algorithm for a quadratic semidefinite program with general constraints
Code to output SDP file for use in RDM mechanics.
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.
Parser for CVXR to solve the Gaussian MLE problem with added constraints.
R Package for Density Estimation with Semidefinite Programming
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!)
Computational appendix of arXiv:2403.02376
Codes for the paper: Theoretical bounds on the network community profile from low-rank semi-definite programming
Max Edge Weighted Clique Problem with multiple choice contrants solved with semidefinite programming
An Exact Solver for Semi-supervised Minimum Sum-of-Squares Clustering
An Exact Solver for Cardinality-constrained Minimum Sum-of-Squares Clustering
Convex relaxation techniques applied to clustering
Fix and Bound: An efficient approach for solving large-scale BoxQPs
Solving quantum comb min-entropy semi-definite programs for measurement-based quantum computing applications
Fork of SDPA-GMP allowing usage as a callable library
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
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