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Quasi-best approximation in optimization with PDE constraints

Gaspoz, Fernando ; Kreuzer, Christian ; Veeser, Andreas ; Wollner, Winnifried (2021)
Quasi-best approximation in optimization with PDE constraints.
In: Inverse Problems, 2021, 36 (1)
doi: 10.26083/tuprints-00019330
Artikel, Zweitveröffentlichung, Verlagsversion

Kurzbeschreibung (Abstract)

We 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 quasi-best approximation constant becomes independent of the Tikhonov parameter as the mesh size tends to 0 and we give quantitative relationships between mesh size 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.

Typ des Eintrags: Artikel
Erschienen: 2021
Autor(en): Gaspoz, Fernando ; Kreuzer, Christian ; Veeser, Andreas ; Wollner, Winnifried
Art des Eintrags: Zweitveröffentlichung
Titel: Quasi-best approximation in optimization with PDE constraints
Sprache: Englisch
Publikationsjahr: 2021
Publikationsdatum der Erstveröffentlichung: 2021
Verlag: IOP Publishing
Titel der Zeitschrift, Zeitung oder Schriftenreihe: Inverse Problems
Jahrgang/Volume einer Zeitschrift: 36
(Heft-)Nummer: 1
Kollation: 29 Seiten
DOI: 10.26083/tuprints-00019330
URL / URN: https://tuprints.ulb.tu-darmstadt.de/19330
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Herkunft: Zweitveröffentlichung aus gefördertem Golden Open Access
Kurzbeschreibung (Abstract):

We 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 quasi-best approximation constant becomes independent of the Tikhonov parameter as the mesh size tends to 0 and we give quantitative relationships between mesh size 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.

Status: Verlagsversion
URN: urn:nbn:de:tuda-tuprints-193304
Sachgruppe der Dewey Dezimalklassifikatin (DDC): 500 Naturwissenschaften und Mathematik > 510 Mathematik
Fachbereich(e)/-gebiet(e): 04 Fachbereich Mathematik
04 Fachbereich Mathematik > Optimierung
Hinterlegungsdatum: 06 Sep 2021 12:12
Letzte Änderung: 20 Sep 2021 07:07
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