In this thesis we consider optimization problems with complementarity constraints in infinite-dimensional spaces. On the one hand, we deal with the general situation, in which the complementarity constraint is governed by a closed convex cone. We use the local decomposition approach, which is known from finite dimensions, to derive first-order necessary optimality conditions of strongly stationary type. In the non-polyhedric case, stronger conditions are obtained by an additional linearization argument. On the other hand, we consider the optimal control of the obstacle problem. This is a classical example for a problem with complementarity constraints in infinite dimensions. We are concerned with the control-constrained case. Due to the lack of surjectivity, a system of strong stationarity is not necessarily satisfied for all local minimizers. We identify assumptions on the data of the optimal control problem under which strong stationarity of local minimizers can be verified. Moreover, without any additional assumptions on the data, we show that a system of M-stationarity is satisfied provided that some sequence of multipliers converges in capacity. Finally, we also discuss the notion of polyhedric sets. These sets have many applications in infinite-dimensional optimization theory. Since the results concerning polyhedricity are scattered in the literature, we provide a review of the known results. Furthermore, we give some new results concerning polyhedricity of intersections and provide counterexamples which demonstrate that intersections of polyhedric sets may fail to be polyhedric. We also prove a new polyhedricity result for sets in vector-valued Sobolev spaces.

In this thesis we consider optimization problems with complementarity constraints in infinite-dimensional spaces. On the one hand, we deal with the general situation, in which the complementarity constraint is governed by a closed convex cone. We use the local decomposition approach, which is known from finite dimensions, to derive first-order necessary optimality conditions of strongly stationary type. In the non-polyhedric case, stronger conditions are obtained by an additional linearization argument. On the other hand, we consider the optimal control of the obstacle problem. This is a classical example for a problem with complementarity constraints in infinite dimensions. We are concerned with the control-constrained case. Due to the lack of surjectivity, a system of strong stationarity is not necessarily satisfied for all local minimizers. We identify assumptions on the data of the optimal control problem under which strong stationarity of local minimizers can be verified. Moreover, without any additional assumptions on the data, we show that a system of M-stationarity is satisfied provided that some sequence of multipliers converges in capacity. Finally, we also discuss the notion of polyhedric sets. These sets have many applications in infinite-dimensional optimization theory. Since the results concerning polyhedricity are scattered in the literature, we provide a review of the known results. Furthermore, we give some new results concerning polyhedricity of intersections and provide counterexamples which demonstrate that intersections of polyhedric sets may fail to be polyhedric. We also prove a new polyhedricity result for sets in vector-valued Sobolev spaces.