Positive definite matrices play a central role in mathematics, physics, statistics and engineering due to their unique properties and widespread applicability. These matrices, which are characterised ...
A new class of large-sample covariance and spectral density matrix estimators is proposed based on the notion of flat-top kernels. The new estimators are shown to be higher-order accurate when ...
Restoring Definiteness via Shrinking, with an Application to Correlation Matrices with a Fixed Block
This is a preview. Log in through your library . Abstract Indefinite approximations of positive semidefinite matrices arise in various data analysis applications involving covariance matrices and ...
Abstract: The goal of this paper is to leverage more information from a single measurement (e.g. an ElectroEncephalo-Graphic (EEG) trial) by representing it as a trajectory of covariance matrices ...
Python proof of concept for BCHOL. Solves for x in Ax = b, using the Recursive Schur Linear Quadratic Regulator explained in the paper A Parallell Linear System Solver for Optimal Control by Brian ...
Covariance Matrix,Proximal Operator,Sample Covariance Matrix,Additive Decomposition,Matrix Factorization,Non-convex,Optimization Problem,Argument In The Proof,Artificial Intelligence ...
This repository illustrates how matrix control barrier functions (MCBFs) can be used to handle semidefinite matrix constraints, using a connectivity maintenance problem in multi-agent systems as a ...
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