Fast Approximate Determinants Using Rational Functions

Fuente: arXiv
Enregistré dans:
Détails bibliographiques
Auteurs principaux: Colthurst, Thomas, Vasudevan, Srinivas, Lottes, James, Patton, Brian
Format: Preprint
Publié: 2024
Sujets:
Accès en ligne:
Tags: Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
_version_ 1866910436288364544
author Colthurst, Thomas
Vasudevan, Srinivas
Lottes, James
Patton, Brian
author_facet Colthurst, Thomas
Vasudevan, Srinivas
Lottes, James
Patton, Brian
contents We show how rational function approximations to the logarithm, such as $\log z \approx (z^2 - 1)/(z^2 + 6z + 1)$, can be turned into fast algorithms for approximating the determinant of a very large matrix. We empirically demonstrate that when combined with a good preconditioner, the third order rational function approximation offers a very good trade-off between speed and accuracy when measured on matrices coming from Matérn-$5/2$ and radial basis function Gaussian process kernels. In particular, it is significantly more accurate on those matrices than the state-of-the-art stochastic Lanczos quadrature method for approximating determinants while running at about the same speed.
format Preprint
id arxiv_https___arxiv_org_abs_2405_03474
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Fast Approximate Determinants Using Rational Functions
Colthurst, Thomas
Vasudevan, Srinivas
Lottes, James
Patton, Brian
Data Structures and Algorithms
Numerical Analysis
G.1.3
We show how rational function approximations to the logarithm, such as $\log z \approx (z^2 - 1)/(z^2 + 6z + 1)$, can be turned into fast algorithms for approximating the determinant of a very large matrix. We empirically demonstrate that when combined with a good preconditioner, the third order rational function approximation offers a very good trade-off between speed and accuracy when measured on matrices coming from Matérn-$5/2$ and radial basis function Gaussian process kernels. In particular, it is significantly more accurate on those matrices than the state-of-the-art stochastic Lanczos quadrature method for approximating determinants while running at about the same speed.
title Fast Approximate Determinants Using Rational Functions
topic Data Structures and Algorithms
Numerical Analysis
G.1.3
url https://arxiv.org/abs/2405.03474