An a posteriori error analysis for an optimal control problem with point sources

Alejandro Allendes, Enrique Otárola, Richard Rankin, Abner J. Salgado

Research output: Journal PublicationArticlepeer-review

4 Citations (Scopus)
4 Downloads (Pure)

Abstract

We propose and analyze a reliable and efficient a posteriori error estimator for a control-constrained linear-quadratic optimal control problem involving Dirac measures; the control variable corresponds to the amplitude of forces modeled as point sources. The proposed a posteriori error estimator is defined as the sum of two contributions, which are associated with the state and adjoint equations. The estimator associated with the state equation is based on Muckenhoupt weighted Sobolev spaces, while the one associated with the adjoint is in the maximum norm and allows for unbounded right hand sides. The analysis is valid for two and three-dimensional domains. On the basis of the devised a posteriori error estimator, we design a simple adaptive strategy that yields optimal rates of convergence for the numerical examples that we perform.

Original languageEnglish
Pages (from-to)1617-1650
Number of pages34
JournalESAIM: Mathematical Modelling and Numerical Analysis
Volume52
Issue number5
DOIs
Publication statusPublished - 1 Sep 2018

Keywords

  • A posteriori error analysis
  • Adaptive finite elements
  • Dirac measures
  • Linear-quadratic optimal control problem
  • Maximum norm
  • Muckenhoupt weights
  • Weighted Sobolev spaces

ASJC Scopus subject areas

  • Analysis
  • Numerical Analysis
  • Modelling and Simulation
  • Computational Mathematics
  • Applied Mathematics

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