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dc.contributor.authorBaryamureeba, V.
dc.contributor.authorSteihaug, T.
dc.date.accessioned2022-07-18T10:33:20Z
dc.date.available2022-07-18T10:33:20Z
dc.date.issued2000
dc.identifier.citationBaryamureeba, V., & Steihaug, T. (2000). On the properties of preconditioners for robust linear regression. Department of Informatics, University of Bergen. Fountain Publishers. ISBN 978-9970-02-730-9en_US
dc.identifier.isbn978-9970-02-730-9
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/4220
dc.description.abstractIn 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.en_US
dc.language.isoenen_US
dc.publisherFountain Publishersen_US
dc.subjectPropertiesen_US
dc.subjectPreconditionersen_US
dc.subjectRobust Linear Regressionen_US
dc.titleProperties of Preconditioners for Robust Linear Regressionen_US
dc.typeBook chapteren_US


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