Randomized Kaczmarz with geometrically smoothed momentum

Fuente: arXiv
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Autores principales: Alderman, Seth J., Luikart, Roan W., Marshall, Nicholas F.
Formato: Preprint
Publicado: 2024
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author Alderman, Seth J.
Luikart, Roan W.
Marshall, Nicholas F.
author_facet Alderman, Seth J.
Luikart, Roan W.
Marshall, Nicholas F.
contents This paper studies the effect of adding geometrically smoothed momentum to the randomized Kaczmarz algorithm, which is an instance of stochastic gradient descent on a linear least squares loss function. We prove a result about the expected error in the direction of singular vectors of the matrix defining the least squares loss. We present several numerical examples illustrating the utility of our result and pose several questions.
format Preprint
id arxiv_https___arxiv_org_abs_2401_09415
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Randomized Kaczmarz with geometrically smoothed momentum
Alderman, Seth J.
Luikart, Roan W.
Marshall, Nicholas F.
Numerical Analysis
Probability
Machine Learning
65F10, 37N30, 60H25, 65F20
This paper studies the effect of adding geometrically smoothed momentum to the randomized Kaczmarz algorithm, which is an instance of stochastic gradient descent on a linear least squares loss function. We prove a result about the expected error in the direction of singular vectors of the matrix defining the least squares loss. We present several numerical examples illustrating the utility of our result and pose several questions.
title Randomized Kaczmarz with geometrically smoothed momentum
topic Numerical Analysis
Probability
Machine Learning
65F10, 37N30, 60H25, 65F20
url https://arxiv.org/abs/2401.09415