Alpaqa: A matrix-free solver for nonlinear MPC and large-scale nonconvex optimization
Optimization and Control
2021-12-07 v1 Systems and Control
Systems and Control
Abstract
This paper presents alpaqa, an open-source C++ implementation of an augmented Lagrangian method for nonconvex constrained numerical optimization, using the first-order PANOC algorithm as inner solver. The implementation is packaged as an easy-to-use library that can be used in C++ and Python. Furthermore, two improvements to the PANOC algorithm are proposed and their effectiveness is demonstrated in NMPC applications and on the CUTEst benchmarks for numerical optimization. The source code of the alpaqa library is available at https://github.com/kul-optec/alpaqa and binary packages can be installed from https://pypi.org/project/alpaqa .
Keywords
Cite
@article{arxiv.2112.02370,
title = {Alpaqa: A matrix-free solver for nonlinear MPC and large-scale nonconvex optimization},
author = {Pieter Pas and Mathijs Schuurmans and Panagiotis Patrinos},
journal= {arXiv preprint arXiv:2112.02370},
year = {2021}
}
Comments
Submitted to the 20th European Control Conference (ECC22), London