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PIQP is a Proximal Interior Point Quadratic Programming solver, which can solve dense and sparse quadratic programs of the form
$$ \begin{aligned} \min_{x} \quad & \frac{1}{2} x^\top P x + c^\top x \ \text {s.t.}\quad & Ax=b, \ & Gx \leq h, \ & x_{lb} \leq x \leq x_{ub}, \end{aligned} $$
Combining an infeasible interior point method with the proximal method of multipliers, the algorithm can handle ill-conditioned convex QP problems without the need for linear independence of the constraints.
PIQP support a wide range of interfaces including
PIQP is developed by the following people:
All contributors are affiliated with the Laboratoire d'Automatique and/or the Risk Analytics and Optimization Chair at EPFL, Switzerland.
This work was supported by the Swiss National Science Foundation under the NCCR Automation (grant agreement 51NF40_180545).
PIQP is an adapted implementation of work by Spyridon Pougkakiotis and Jacek Gondzio, and is built on the following open-source libraries:
If you found PIQP useful in your scientific work, we encourage you to cite our accompanying paper:
@INPROCEEDINGS{schwan2023piqp,
author={Schwan, Roland and Jiang, Yuning and Kuhn, Daniel and Jones, Colin N.},
booktitle={2023 62nd IEEE Conference on Decision and Control (CDC)},
title={{PIQP}: A Proximal Interior-Point Quadratic Programming Solver},
year={2023},
volume={},
number={},
pages={1088-1093},
doi={10.1109/CDC49753.2023.10383915}
}
The benchmarks are available in the following repo: piqp_benchmarks
PIQP is licensed under the BSD 2-Clause License.
FAQs
A Proximal Interior Point Quadratic Programming solver
We found that piqp demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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