An a posteriori error analysis of an elliptic optimal control problem in measure space

TL;DR

This paper develops an a posteriori error estimator for a sparse elliptic optimal control problem with measure space controls, combining error estimates for the adjoint and state equations, and validates it through numerical examples.

Contribution

It introduces a novel a posteriori error estimator tailored for measure space controls in elliptic optimal control problems, with proven efficiency and reliability.

Findings

The estimator effectively guides adaptive refinement.

It is locally efficient and reliable in 2D and 3D.

Numerical tests confirm the theoretical properties.

Abstract

We propose an a posteriori error estimator for a sparse optimal control problem: the control variable lies in the space of regular Borel measures. We consider a solution technique that relies on the discretization of the control variable as a linear combination of Dirac measures. The proposed a posteriori error estimator can be decomposed into the sum of two contributions: an error estimator in the maximum norm for the discretization of the adjoint equation and an estimator in the $L^2$-norm that accounts for the approximation of the state equation. We prove that the designed error estimator is locally efficient and we explore its reliability properties. The analysis is valid for two and three-dimensional domains. We illustrate the theory with numerical examples.