Quasi-best approximation in optimization with PDE constraints

dc.contributor.authorGaspoz, Fernando
dc.contributor.authorKreuzer, Christian
dc.contributor.authorVeeser, Andreas
dc.contributor.authorWollner, Winnifried
dc.date.accessioned2019-04-16T16:11:18Z
dc.date.available2019-04-16T16:11:18Z
dc.date.issued2019-04
dc.description.abstractWe consider finite element solutions to quadratic optimization problems, where the state depends on the control via a well-posed linear partial differential equation. Exploiting the structure of a suitably reduced optimality system, we prove that the combined error in the state and adjoint state of the variational discretization is bounded by the best approximation error in the underlying discrete spaces. The constant in this bound depends on the inverse square-root of the Tikhonov regularization parameter. Furthermore, if the operators of control-action and observation are compact, this quasibest-approximation constant becomes independent of the Tikhonov parameter as the meshsize tends to 0 and we give quantitative relationships between meshsize and Tikhonov parameter ensuring this independence. We also derive generalizations of these results when the control variable is discretized or when it is taken from a convex set.en
dc.identifier.issn2190-1767
dc.identifier.urihttp://hdl.handle.net/2003/38018
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-20001
dc.language.isoen
dc.relation.ispartofseriesErgebnisberichte des Instituts für Angewandte Mathematik;602
dc.subject.ddc610
dc.titleQuasi-best approximation in optimization with PDE constraintsen
dc.typeText
dc.type.publicationtypepreprint
dcterms.accessRightsopen access
eldorado.secondarypublicationfalse

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