Enregistré dans:
Détails bibliographiques
Auteurs principaux: Shah, Rikhav, Detherage, Isabel
Format: Preprint
Publié: 2024
Sujets:
Accès en ligne:https://arxiv.org/abs/2411.16101
Tags: Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
_version_ 1866912231148486656
author Shah, Rikhav
Detherage, Isabel
author_facet Shah, Rikhav
Detherage, Isabel
contents This paper asks if the following iterative procedure approximately orthogonalizes a set of $n$ linearly independent unit vectors while preserving their span: in each iteration, access a random pair of vectors and replace one with the component perpendicular to the other, renormalized to be a unit vector. We provide a positive answer: any given set of starting vectors converges almost surely to an orthonormal basis of their span. We specifically argue that the $n$-volume of the parallelepiped generated by the vectors approaches 1 (i.e. the parallelepiped approaches a hypercube). If $A$ is the matrix formed by taking these vectors as columns, this volume is simply $\det(|A|)$ where $|A|=(A^*A)^{1/2}$. We show that $O(n^2\log(1/(\det(|A|)\varepsilon)))$ iterations suffice to bring ${\det(|A|)}$ above $1-\varepsilon$ with constant probability.
format Preprint
id arxiv_https___arxiv_org_abs_2411_16101
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle A Kaczmarz-Inspired Method for Orthogonalization
Shah, Rikhav
Detherage, Isabel
Probability
65F25, 15A12, 60
This paper asks if the following iterative procedure approximately orthogonalizes a set of $n$ linearly independent unit vectors while preserving their span: in each iteration, access a random pair of vectors and replace one with the component perpendicular to the other, renormalized to be a unit vector. We provide a positive answer: any given set of starting vectors converges almost surely to an orthonormal basis of their span. We specifically argue that the $n$-volume of the parallelepiped generated by the vectors approaches 1 (i.e. the parallelepiped approaches a hypercube). If $A$ is the matrix formed by taking these vectors as columns, this volume is simply $\det(|A|)$ where $|A|=(A^*A)^{1/2}$. We show that $O(n^2\log(1/(\det(|A|)\varepsilon)))$ iterations suffice to bring ${\det(|A|)}$ above $1-\varepsilon$ with constant probability.
title A Kaczmarz-Inspired Method for Orthogonalization
topic Probability
65F25, 15A12, 60
url https://arxiv.org/abs/2411.16101