Speaker
Alexandra Bünger
(TU Chemnitz)
Description
PDE-constrained optimization problems arise in a broad number of applications. The resulting large-scale saddle-point systems are challenging to solve and acquiring a full solution is often infeasible. We present a new framework to find a low-rank approximation to the solution by reformulating the system into a system of Sylvester-like matrix equations. These matrix equations are subsequently projected onto a small subspace via rational Krlyov-subspace iterations and we obtain a reduced problem by imposing a Galerkin condition on its residual. In our presentation we discuss implementation details and dependence on the problem parameters. Numerical experiments will illustrate the performance of the new strategy.
Primary author
Alexandra Bünger
(TU Chemnitz)
Co-authors
Valeria Simoncini
( Alma Mater Studiorum, Universita’ di Bologna)
Martin Stoll
(TU Chemnitz)
