Domain decomposition methods for linear-quadratic elliptic optimal control problems

This thesis is concerned with the development of domain decomposition (DD) based preconditioners for linear-quadratic elliptic optimal control problems (LQ-EOCPs), their analysis, and numerical studies of their performance on model problems. The solution of LQ-EOCPs arises in many applications, either directly or as subproblems in Newton or Sequential Quadratic Programming methods for the solution of nonlinear elliptic optimal control problems. After a finite element discretization, convex LQ-EOCPs lead to large scale symmetric indefinite linear systems. The solution of these large systems is a very time consuming step and must be done iteratively, typically with a preconditioned Krylov subspace method. Developing good preconditioners for these linear systems is an important part of improving the overall performance of the solution method. The DD preconditioners for LQ-EOCPs studied in this thesis are extensions of overlapping and nonoverlapping Neumann-Neumann DD preconditioners applied to single elliptic partial differential equations (PDEs). In our case, DD is applied on the optimization level. In particular, the proposed preconditioners require the parallel solution of subdomain optimal control problems that are related to restrictions of the original LQ-EOCP to a subdomain. Numerical results on several test problems have shown that the new preconditioners are effective. Their performance relative to decreases in finite element mesh size or increase in number of subdomains seem to be numerically comparable to that of overlapping and Neumann-Neumann preconditioners for single PDEs. Remarkably, the proposed preconditioners seem to be rather insensitive to control regularization parameters. For overlapping methods, theoretical results are provided to support the numerical observations