On the distance to low-rank matrices in the maximum norm

Stanislav Budzinskiy

doi:10.1016/j.laa.2024.02.012

Titel

On the distance to low-rank matrices in the maximum norm

Autor*in

Stanislav Budzinskiy

Fakultät für Mathematik, Universität Wien

Abstract

Every sufficiently big matrix with small spectral norm has a nearby low-rank matrix if the distance is measured in the maximum norm (Udell and Townsend, 2019 [7]). We use the Hanson–Wright inequality to improve the estimate of the distance for matrices with incoherent column and row spaces. In numerical experiments with several classes of matrices we study how well the theoretical upper bound describes the approximation errors achieved with the method of alternating projections.

Stichwort

Low-rank approximationMaximum normJohnson–Lindenstrauss lemmaHanson–Wright inequalityAlternating projections

Objekt-Typ

journal article

Sprache

Englisch [eng]

Persistent identifier

phaidra.univie.ac.at/o:2067285

DOI

10.1016/j.laa.2024.02.012

Erschienen in

Titel