3 papers
math.OC2026
Mesh-dependent iteration count growth in primal-dual active set strategies
Ioannis P. A. Papadopoulos, Michael Hintermüller
Primal-dual active set strategies (PDAS) are popular iterative solvers for mixed complementarity problems such as constrained optimization problems with pointwise inequality constr…
math.OC2026
Layerwise goal-oriented adaptivity for neural ODEs: an optimal control perspective
Michael Hintermüller, Michael Hinze, Denis Korolev
In this work, we propose a novel layerwise adaptive construction method for neural network architectures. Our approach is based on a goal--oriented dual-weighted residual technique…
math.OC2024
A neural network approach to learning solutions of a class of elliptic variational inequalities
Amal Alphonse, Michael Hintermüller, Alexander Kister +2
We develop a weak adversarial approach to solving obstacle problems using neural networks. By employing (generalised) regularised gap functions and their properties we rewrite the…