Andrei Drăgănescu
University of Maryland Baltimore County, Baltimore, USA

The aim of this research is to develop efficient multigrid preconditioners for a classic linear-quadratic optimization problem constrained by an elliptic equation with stochastic coefficients. Using a discretize-then-optimize approach, in previous work we have shown that strategies inherited from the associated deterministic optimal control problem extend to the stochastic version when a stochastic Galerkin discretization is employed. In this talk we show that similar strategies succeed when discretizing the elliptic equation using a sparse grid stochastic collocation approach.