4 research outputs found
PANTR: A proximal algorithm with trust-region updates for nonconvex constrained optimization
This work presents PANTR, an efficient solver for nonconvex constrained
optimization problems, that is well-suited as an inner solver for an augmented
Lagrangian method. The proposed scheme combines forward-backward iterations
with solutions to trust-region subproblems: the former ensures global
convergence, whereas the latter enables fast update directions. We discuss how
the algorithm is able to exploit exact Hessian information of the smooth
objective term through a linear Newton approximation, while benefiting from the
structure of box-constraints or l1-regularization. An open-source C++
implementation of PANTR is made available as part of the NLP solver library
ALPAQA. Finally, the effectiveness of the proposed method is demonstrated in
nonlinear model predictive control applications.Comment: Accepted for publication in IEEE Control Systems Letter