Optimization of controls and parameters coming from realistic full-scale simulation requires enormous computational effort. To make such optimization practical requires optimal multilevel solvers and scalable parallel algorithms. Even in the case where such solvers and algorithms are well understood for the forward problem, adapting them to the optimization context can be interesting and complicated. In this talk we discuss some practical approaches to implementing optimization solvers. We consider optimizing the parameters in non-standard boundary conditions, using parallel computing to handle time-dependent problems, and an algebraic multilevel approach for PDE-constrained optimization that targets very large problems on parallel computers.