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Maros-Meszaros test set for QP solvers
This repository contains the Maros-Meszaros test set in a format suitable for qpbenchmark. Maros-Meszaros is a standard test set containing 138 quadratic programs that are designed to be difficult. Here are the reports produced by qpbenchmark:
- 📈 <a href="https://github.com/qpsolvers/maros_meszaros_qpbenchmark/blob/main/results/maros_meszaros.md"><strong>Maros-Meszaros test set results</strong></a>
- 📈 Dense subset results
- 📈 Sparse subset results
Installation
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 qpbenchmark
It is also possible to install the benchmark from PyPI.
Usage
Run the test set as follows:
qpbenchmark ./maros_meszaros.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.
Subsets
| Subset name | Description | Problems |
|---|---|---|
| - | All problems. | 138 / 138 |
| Dense | Only problems with less than $n \leq 1000$ variables and $m \leq 10000$ constraints. | 62 / 138 |
| Dense pos. def. | Only problems from the Dense subset where the cost matrix is positive-definite. | 19 / 138 |
| Sparse | Complementary to the dense subset | 76 / 138 |
Citation
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}
}
See also
Related test sets that may be relevant to your use cases:
- Free-for-all: community-built test set, new problems welcome!
- Model predictive control: model predictive control problems arising e.g. in robotics.