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Preconditioning for partial differential equation constrained optimization with control constraints


Martin Stoll, Oxford Centre for Collaborative Applied Mathematics, Mathematical Institute, 24–29 St Giles', Oxford OX1 3LB, U.K.



Optimal control problems with partial differential equations play an important role in many applications. The inclusion of bound constraints for the control poses a significant additional challenge for optimization methods. In this paper, we propose preconditioners for the saddle point problems that arise when a primal–dual active set method is used. We also show for this method that the same saddle point system can be derived when the method is considered as a semismooth Newton method. In addition, the projected gradient method can be employed to solve optimization problems with simple bounds, and we discuss the efficient solution of the linear systems in question. In the case when an acceleration technique is employed for the projected gradient method, this again yields a semismooth Newton method that is equivalent to the primal–dual active set method. We also consider the Moreau–Yosida regularization method for control constraints and efficient preconditioners for this technique. Numerical results illustrate the competitiveness of these approaches. Copyright © 2011 John Wiley & Sons, Ltd.