On multigrid methods in convex optimization

25 February 2016
Michal Kocvara

The aim of this talk is to design an efficient multigrid method for constrained convex optimization problems arising from discretization  of  some  underlying  infinite  dimensional  problems. Due  to problem  dependency  of this approach, we only consider bound constraints with (possibly) a linear equality constraint. As our aim is to target large-scale problems, we want to avoid computation of second 
derivatives of the objective function, thus excluding Newton like methods. We propose a smoothing operator that only uses first-order information and study the computational efficiency of the resulting method. In the second part, we consider application of multigrid techniques to more general optimization problems, in particular, the topology design problem.

  • Computational Mathematics and Applications Seminar