Issue |
ESAIM: COCV
Volume 27, 2021
Special issue in honor of Enrique Zuazua's 60th birthday
|
|
---|---|---|
Article Number | 4 | |
Number of page(s) | 39 | |
DOI | https://doi.org/10.1051/cocv/2020059 | |
Published online | 20 January 2021 |
New regularity results and finite element error estimates for a class of parabolic optimal control problems with pointwise state constraints*
Technische Universität München, Chair of Optimal Control, Center for Mathematical Sciences, M17,
Boltzmannstraße 3,
85748
Garching, Germany.
** Corresponding author: christof@ma.tum.de
Received:
26
January
2020
Accepted:
15
September
2020
We study first-order necessary optimality conditions and finite element error estimates for a class of distributed parabolic optimal control problems with pointwise state constraints. It is demonstrated that, if the bound in the state constraint and the differential operator in the governing PDE fulfil a certain compatibility assumption, then locally optimal controls satisfy a stationarity system that allows to significantly improve known regularity results for adjoint states and Lagrange multipliers in the parabolic setting. In contrast to classical approaches to first-order necessary optimality conditions for state-constrained problems, the main arguments of our analysis require neither a Slater point, nor uniform control constraints, nor differentiability of the objective function, nor a restriction of the spatial dimension. As an application of the established improved regularity properties, we derive new finite element error estimates for the dG(0) − cG(1)-discretization of a purely state-constrained linear-quadratic optimal control problem governed by the heat equation. The paper concludes with numerical experiments that confirm our theoretical findings.
Mathematics Subject Classification: 35K10 / 49K20 / 49M05 / 65N15 / 65N30
Key words: Optimal control / parabolic partial differential equation / state constraints / first-order necessary optimality condition / regularity result / finite element method / a priori error estimate
© EDP Sciences, SMAI 2021
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