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

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.

Accepted for publication in IEEE Control Systems Letters

References in corpus (4)

• Embedded nonlinear model predictive control for obstacle avoidance using PANOC
• Reactive Navigation of an Unmanned Aerial Vehicle with Perception-based Obstacle Avoidance Constraints
• Proximal Gradient Algorithms under Local Lipschitz Gradient Continuity: A Convergence and Robustness Analysis of PANOC
• Gauss-Newton meets PANOC: A fast and globally convergent algorithm for nonlinear optimal control