This repository contains quadratic programs (QPs) arising from differential inverse kinematics in robotics, in a format suitable for qpbenchmark. Here is the report produced by this benchmarking tool:
- 📈 IK test set results (
⚠️ work in progress: still crunching the 2,592,000 solver calls of the test set)
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 ik_qpbenchmarkIt is also possible to install the benchmark from PyPI.
Run the test set as follows:
python ./ik_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.
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 = {2.4.0},
year = {2024}
}If you contribute to this repository, don't forget to add yourself to the BibTeX above and to CITATION.cff.
Quadratic programs in this test set were generated with pink bench.
There are other test sets in qpbenchmark 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.
- Model predictive control: model predictive control problems arising e.g. in robotics.