A new approach to nonlinear constrained Tikhonov regularization

Handle URI:
http://hdl.handle.net/10754/597333
Title:
A new approach to nonlinear constrained Tikhonov regularization
Authors:
Ito, Kazufumi; Jin, Bangti
Abstract:
We present a novel approach to nonlinear constrained Tikhonov regularization from the viewpoint of optimization theory. A second-order sufficient optimality condition is suggested as a nonlinearity condition to handle the nonlinearity of the forward operator. The approach is exploited to derive convergence rate results for a priori as well as a posteriori choice rules, e.g., discrepancy principle and balancing principle, for selecting the regularization parameter. The idea is further illustrated on a general class of parameter identification problems, for which (new) source and nonlinearity conditions are derived and the structural property of the nonlinearity term is revealed. A number of examples including identifying distributed parameters in elliptic differential equations are presented. © 2011 IOP Publishing Ltd.
Citation:
Ito K, Jin B (2011) A new approach to nonlinear constrained Tikhonov regularization. Inverse Problems 27: 105005. Available: http://dx.doi.org/10.1088/0266-5611/27/10/105005.
Publisher:
IOP Publishing
Journal:
Inverse Problems
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
16-Sep-2011
DOI:
10.1088/0266-5611/27/10/105005
Type:
Article
ISSN:
0266-5611; 1361-6420
Sponsors:
The authors are grateful to two anonymous referees whose constructive comments have led to an improved presentation. The work of BJ was supported by Award no KUS-C1-016-04, made by the King Abdullah University of Science and Technology (KAUST). A part of the work was carried out during his visit at Graduate School of Mathematical Sciences, The University of Tokyo, and he would like to thank Professor Masahiro Yamamoto for the kind invitation and hospitality.
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorIto, Kazufumien
dc.contributor.authorJin, Bangtien
dc.date.accessioned2016-02-25T12:30:54Zen
dc.date.available2016-02-25T12:30:54Zen
dc.date.issued2011-09-16en
dc.identifier.citationIto K, Jin B (2011) A new approach to nonlinear constrained Tikhonov regularization. Inverse Problems 27: 105005. Available: http://dx.doi.org/10.1088/0266-5611/27/10/105005.en
dc.identifier.issn0266-5611en
dc.identifier.issn1361-6420en
dc.identifier.doi10.1088/0266-5611/27/10/105005en
dc.identifier.urihttp://hdl.handle.net/10754/597333en
dc.description.abstractWe present a novel approach to nonlinear constrained Tikhonov regularization from the viewpoint of optimization theory. A second-order sufficient optimality condition is suggested as a nonlinearity condition to handle the nonlinearity of the forward operator. The approach is exploited to derive convergence rate results for a priori as well as a posteriori choice rules, e.g., discrepancy principle and balancing principle, for selecting the regularization parameter. The idea is further illustrated on a general class of parameter identification problems, for which (new) source and nonlinearity conditions are derived and the structural property of the nonlinearity term is revealed. A number of examples including identifying distributed parameters in elliptic differential equations are presented. © 2011 IOP Publishing Ltd.en
dc.description.sponsorshipThe authors are grateful to two anonymous referees whose constructive comments have led to an improved presentation. The work of BJ was supported by Award no KUS-C1-016-04, made by the King Abdullah University of Science and Technology (KAUST). A part of the work was carried out during his visit at Graduate School of Mathematical Sciences, The University of Tokyo, and he would like to thank Professor Masahiro Yamamoto for the kind invitation and hospitality.en
dc.publisherIOP Publishingen
dc.titleA new approach to nonlinear constrained Tikhonov regularizationen
dc.typeArticleen
dc.identifier.journalInverse Problemsen
dc.contributor.institutionNorth Carolina State University, Raleigh, United Statesen
dc.contributor.institutionTexas A and M University, College Station, United Statesen
kaust.grant.numberKUS-C1-016-04en
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