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 ...

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 shift splitting concept is introduced and, correspondingly, a shift-splitting iteration scheme and a shift-splitting preconditioner are presented, for solving the large sparse system of linear ...

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 ...

Left: Illustration of different geometric mean properties on the manifold of positive semidefinite matrices implemented in this library. Deviation of the geometric means computed using the ...

Abstract: Log-Euclidean distances are commonly used to quantify the similarity between positive definite matrices using geometric considerations. This paper analyzes the behavior of this distance when ...

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 ...