Preconditioning PDE-constrained optimization with $\rm L^1$-sparsity and control constraints
This work addresses the computational bottleneck of solving PDE-constrained optimization with sparsity and control constraints, which is important for practical applications in engineering and science.
The paper develops robust preconditioners for semismooth Newton methods solving PDE-constrained optimization with L1-sparsity and box control constraints, demonstrating robustness through theoretical analysis and numerical experiments.
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.