Properties of Preconditioners for Robust Linear Regression
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Date
2000
Authors
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Journal ISSN
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Publisher
Fountain Publishers
Abstract
In this paper, we consider solving the robust linear regression problem y = Ax + ∈ by
an inexact Newton method and an iteratively reweighted least squares method. We
show that each of these methods can be combined with the preconditioned conjugate
gradient least square algorithm to solve large, sparse systems of linear equations
efficiently. We consider the constant preconditioner ATA and preconditioners based
on low-rank updates and downdates of existing matrix factorizations. Numerical
results are given to demonstrate the effectiveness of these preconditioners.
Description
Keywords
Properties, Preconditioners, Robust Linear Regression
Citation
Baryamureeba, V., & Steihaug, T. (2000). On the properties of preconditioners for robust linear regression. Department of Informatics, University of Bergen. Fountain Publishers. ISBN 978-9970-02-730-9