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dc.contributor.authorHeinkenschloss, Matthias
Ridzal, Denis
dc.date.accessioned 2016-02-02T21:22:53Z
dc.date.available 2016-02-02T21:22:53Z
dc.date.issued 2014
dc.identifier.citation Heinkenschloss, Matthias and Ridzal, Denis. "A Matrix-Free Trust-Region SQP Method for Equality Constrained Optimization." SIAM Journal on Optimization, 24, no. 3 (2014) 1507-1541. http://dx.doi.org/10.1137/130921738.
dc.identifier.urihttps://hdl.handle.net/1911/88308
dc.description.abstract We develop and analyze a trust-region sequential quadratic programming (SQP) method for the solution of smooth equality constrained optimization problems, which allows the inexact and hence iterative solution of linear systems. Iterative solution of linear systems is important in large-scale applications, such as optimization problems with partial differential equation constraints, where direct solves are either too expensive or not applicable. Our trust-region SQP algorithm is based on a composite-step approach that decouples the step into a quasi-normal and a tangential step. The algorithm includes critical modifications of substep computations needed to cope with the inexact solution of linear systems. The global convergence of our algorithm is guaranteed under rather general conditions on the substeps. We propose algorithms to compute the substeps and prove that these algorithms satisfy global convergence conditions. All components of the resulting algorithm are specified in such a way that they can be directly implemented. Numerical results indicate that our algorithm converges even for very coarse linear system solves.
dc.language.iso eng
dc.rights Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
dc.title A Matrix-Free Trust-Region SQP Method for Equality Constrained Optimization
dc.type Journal article
dc.contributor.funder National Science Foundation
dc.citation.journalTitle SIAM Journal on Optimization
dc.subject.keywordsequential quadratic programming
trust-region
large-scale optimization
matrix free
inexact linear system solvers
PDE-constrained optimization
Krylov subspace methods
dc.citation.volumeNumber 24
dc.citation.issueNumber 3
dc.contributor.publisher SIAM
dc.type.dcmi Text
dc.identifier.doihttp://dx.doi.org/10.1137/130921738
dc.identifier.grantID ACI-0121360 (National Science Foundation)
dc.identifier.grantID DMS-0511624 (National Science Foundation)
dc.identifier.grantID DMS-0915238 (National Science Foundation)
dc.type.publication publisher version
dc.citation.firstpage 1507
dc.citation.lastpage 1541


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