This repository contains quadratic programs (QPs) arising from model predictive control in robotics, in a format suitable for qpbenchmark. Here is the report produced by this benchmarking tool:
The recommended process is to install the benchmark and all solvers in an isolated environment using conda:
conda env create -f environment.yaml
conda activate qpbenchmarkIt is also possible to install the benchmark from PyPI.
Run the test set as follows:
python ./mpc_qpbenchmark.py run
The outcome is a standardized report comparing all available solvers against the different benchmark metrics. You can check out and post your own results in the Results forum.
The problems in this test set have been contributed by:
| Problems | Contributor | Details |
|---|---|---|
QUADCMPC* |
@paLeziart | Proposed in #1, details in this thesis |
LIPMWALK* |
@stephane-caron | Proposed in #3, details in this paper |
WHLIPBAL* |
@stephane-caron | Proposed in #4, details in this paper |
If you use qpbenchmark in your works, please cite all its contributors as follows:
@software{qpbenchmark2024,
title = {{qpbenchmark: Benchmark for quadratic programming solvers available in Python}},
author = {Caron, Stéphane and Zaki, Akram and Otta, Pavel and Arnström, Daniel and Carpentier, Justin and Yang, Fengyu and Leziart, Pierre-Alexandre},
url = {https://github.com/qpsolvers/qpbenchmark},
license = {Apache-2.0},
version = {2.3.0},
year = {2024}
}Related test sets that may be relevant to your use cases:
- Free-for-all: community-built test set, new problems welcome!
- Maros-Meszaros test set: a standard test set with problems designed to be difficult.