Preconditioning PDE-constrained optimization with $\rm L^1$-sparsity and control constraints

TL;DR

This paper develops robust preconditioners for semismooth Newton methods applied to PDE-constrained optimization problems with L1 sparsity and control constraints, improving computational efficiency and robustness.

Contribution

It introduces novel preconditioning strategies for the Newton systems in PDE-constrained optimization with sparsity and control constraints, supported by theoretical analysis and practical numerical experiments.

Findings

Preconditioners significantly improve solver robustness.

The method effectively handles L1 sparsity and control constraints.

Numerical results demonstrate practical feasibility and efficiency.

Abstract

PDE-constrained optimization aims at finding optimal setups for partial differential equations so that relevant quantities are minimized. Including sparsity promoting terms in the formulation of such problems results in more practically relevant computed controls but adds more challenges to the numerical solution of these problems. The needed $\rm L^1$-terms as well as additional inclusion of box control constraints require the use of semismooth Newton methods. We propose robust preconditioners for different formulations of the Newton's equation. With the inclusion of a line-search strategy and an inexact approach for the solution of the linear systems, the resulting semismooth Newton's method is feasible for practical problems. Our results are underpinned by a theoretical analysis of the preconditioned matrix. Numerical experiments illustrate the robustness of the proposed scheme.