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arXiv · 2306.17119

PANTR: A proximal algorithm with trust-region updates for nonconvex constrained optimization

Alexander Bodard·Pieter Pas·Panagiotis Patrinos
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Abstract

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.

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Alexander Bodard, Pieter Pas, Panagiotis Patrinos. 2023-06-29. PANTR: A proximal algorithm with trust-region updates for nonconvex constrained optimization. https://doi.org/10.1109/lcsys.2023.3286331

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