Adaptive finite element methods for sparse PDE-constrained optimization

TL;DR

This paper develops reliable a posteriori error estimators for sparse PDE-constrained optimization problems, enabling adaptive finite element methods that achieve optimal convergence rates for various control discretizations.

Contribution

It introduces novel error estimators tailored for different control discretizations in PDE-constrained optimization, facilitating effective adaptive algorithms.

Findings

Error estimators effectively decompose discretization errors.

Adaptive strategies achieve optimal convergence rates.

Method applicable to various control discretizations.

Abstract

We propose and analyze reliable and efficient a posteriori error estimators for an optimal control problem that involves a nondifferentiable cost functional, the Poisson problem as state equation and control constraints. To approximate the solution to the state and adjoint equations we consider a piecewise linear finite element method whereas three different strategies are used to approximate the control variable: piecewise constant discretization, piecewise linear discretization and the so-called variational discretization approach. For the first two aforementioned solution techniques we devise an error estimator that can be decomposed as the sum of four contributions: two contributions that account for the discretization of the control variable and the associated subgradient, and two contributions related to the discretization of the state and adjoint equations. The error estimator for the variational discretization approach is decomposed only in two contributions that are related to the discretization of the state and adjoint equations. On the basis of the devised a posteriori error estimators, we design simple adaptive strategies that yield optimal rates of convergence for the numerical examples that we perform.