Bayesian Optimization of Combinatorial Structures
-
Updated
Oct 25, 2019 - MATLAB
Bayesian Optimization of Combinatorial Structures
LipSDP - Lipschitz Estimation for Neural Networks
Certifiable Outlier-Robust Geometric Perception
Solver for Large-Scale Rank-One Semidefinite Relaxations
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.
An open-source interface to use the multiple-precision solver SDPA-GMP with YALMIP
An open-source add-on for YALMIP to solve optimisation problems with polynomial quadratic integral inequality constraints.
Standard errors for moment matching estimators given limited knowledge about the moment variance-covariance matrix
The code for large margin metric learning for nearest neighbor classification and its acceleration using triplet mining and stratified sampling
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.
Code for designing sigma delta modulator loop filters with optimal properties.
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
An accelerated active‑set algorithm for a quadratic semidefinite program with general constraints
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)
An Exact Solver for Cardinality-constrained Minimum Sum-of-Squares Clustering
Add a description, image, and links to the semidefinite-programming topic page so that developers can more easily learn about it.
To associate your repository with the semidefinite-programming topic, visit your repo's landing page and select "manage topics."