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

Margherita Porcelli (margherita.porcelli***at***unifi.it)
Valeria Simoncini (valeria.simoncini***at***unibo.it)
Martin Stoll (stollm***at***mpi-magdeburg.mpg.de)

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 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.

Keywords: PDE-constrained optimization, Saddle point systems, Preconditioning, Krylov subspace solver, Sparsity, Semismooth Newton's method

Category 1: Nonlinear Optimization (Systems governed by Differential Equations Optimization )

Category 2: Applications -- Science and Engineering (Control Applications )

Citation: Computers and Mathematics with Applications, 74:5 (2017), pp. 1059-1075.

Download: [PDF]

Entry Submitted: 11/21/2016
Entry Accepted: 11/21/2016
Entry Last Modified: 08/30/2017

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