Alpaqa: A matrix-free solver for nonlinear MPC and large-scale nonconvex optimization
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Abstract (arXiv)
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 .
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 Dec 2021.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 2; 2026: 1.
4 papers.
- 2025Dual Optimistic Ascent (PI Control) is the Augmented Lagrangian Method in Disguisecited by 1
- 2022SPIRAL: A superlinearly convergent incremental proximal algorithm for nonconvex finite sum minimizationcited by 0
- 2025A proximal augmented Lagrangian method for nonconvex optimization with equality and inequality constraintscited by 0
- 2026lapanda: A Matrix-Free Differentiable Solver for Nonconvex Constrained Optimization Layerscited by 0
- 2026lapanda: A Matrix-Free Differentiable Solver for Nonconvex Constrained Optimization Layers27 Sep 2026
- 2025Dual Optimistic Ascent (PI Control) is the Augmented Lagrangian Method in Disguise26 Sep 2025
- 2025A proximal augmented Lagrangian method for nonconvex optimization with equality and inequality constraints2 Sep 2025
- 2022SPIRAL: A superlinearly convergent incremental proximal algorithm for nonconvex finite sum minimization17 Jul 2022