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Article: Generalization of Strang's preconditioner with applications to toeplitz least squares problems

TitleGeneralization of Strang's preconditioner with applications to toeplitz least squares problems
Authors
KeywordsMedical imaging
Toeplitz least squares problems
Atmospheric imaging
Circulant preconditioned conjugate gradient method
Deconvolution
Image restoration
Issue Date1996
Citation
Numerical Linear Algebra with Applications, 1996, v. 3, n. 1, p. 45-64 How to Cite?
AbstractIn this paper, we propose a method to generalize Strang's circulant preconditioner for arbitrary n-by-n matrices An. The [n/2]th column of our circulant preconditioner Sn is equal to the [n/2]th column of the given matrix An. Thus if An is a square Toeplitz matrix, then Sn is just the Strang circulant preconditioner. When Sn is not Hermitian, our circulant preconditioner can be defined as (Sn*Sn)1/2. This construction is similar to the forward-backward projection method used in constructing preconditioners for tomographic inversion problems in medical imaging. We show that if the matrix An has decaying coefficients away from the main diagonal, then (Sn*Sn)1/2 is a good preconditioner for An. Comparisons of our preconditioner with other circulant-based preconditioners are carried out for some 1-D Toeplitz least squares problems: min ||b-Ax||2-Preliminary numerical results show that our preconditioner performs quite well, in comparison to other circulant preconditioners. Promising test results are also reported for a 2-D deconvolution problem arising in ground-based atmospheric imaging. ©1996 by John Wiley & Sons, Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/276660
ISSN
2023 Impact Factor: 1.8
2023 SCImago Journal Rankings: 0.932
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorChan, Raymond H.-
dc.contributor.authorNg, Michael K.-
dc.contributor.authorPlemmons, Robert J.-
dc.date.accessioned2019-09-18T08:34:16Z-
dc.date.available2019-09-18T08:34:16Z-
dc.date.issued1996-
dc.identifier.citationNumerical Linear Algebra with Applications, 1996, v. 3, n. 1, p. 45-64-
dc.identifier.issn1070-5325-
dc.identifier.urihttp://hdl.handle.net/10722/276660-
dc.description.abstractIn this paper, we propose a method to generalize Strang's circulant preconditioner for arbitrary n-by-n matrices An. The [n/2]th column of our circulant preconditioner Sn is equal to the [n/2]th column of the given matrix An. Thus if An is a square Toeplitz matrix, then Sn is just the Strang circulant preconditioner. When Sn is not Hermitian, our circulant preconditioner can be defined as (Sn*Sn)1/2. This construction is similar to the forward-backward projection method used in constructing preconditioners for tomographic inversion problems in medical imaging. We show that if the matrix An has decaying coefficients away from the main diagonal, then (Sn*Sn)1/2 is a good preconditioner for An. Comparisons of our preconditioner with other circulant-based preconditioners are carried out for some 1-D Toeplitz least squares problems: min ||b-Ax||2-Preliminary numerical results show that our preconditioner performs quite well, in comparison to other circulant preconditioners. Promising test results are also reported for a 2-D deconvolution problem arising in ground-based atmospheric imaging. ©1996 by John Wiley & Sons, Ltd.-
dc.languageeng-
dc.relation.ispartofNumerical Linear Algebra with Applications-
dc.subjectMedical imaging-
dc.subjectToeplitz least squares problems-
dc.subjectAtmospheric imaging-
dc.subjectCirculant preconditioned conjugate gradient method-
dc.subjectDeconvolution-
dc.subjectImage restoration-
dc.titleGeneralization of Strang's preconditioner with applications to toeplitz least squares problems-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1002/(SICI)1099-1506(199601/02)3:1<45::AID-NLA70>3.0.CO;2-T-
dc.identifier.scopuseid_2-s2.0-21844500518-
dc.identifier.volume3-
dc.identifier.issue1-
dc.identifier.spage45-
dc.identifier.epage64-
dc.identifier.isiWOS:A1996TW56300003-
dc.identifier.issnl1070-5325-

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