This is the code of paper Apollo-MILP: An Alternating Prediction-Correction Neural Solving Framework for Mixed-Integer Linear Programming. Haoyang Liu, Jie Wang, Zijie Geng, Xijun Li, Yuxuan Zong, Fangzhou Zhu, Jianye HAO, Feng Wu. ICLR 2025.
The code is build on top of https://github.com/sribdcn/Predict-and-Search_MILP_method
🚀🚀 Apollo-MILP has now been integrated into MILP-X (https://github.com/happypu326/MILP-X). MILP-X is a unified machine learning framework for MILP that supports data generation, training, evaluation, and benchmarking across 16+ problem types and multiple learning methods. This integration makes it easier for researchers to reproduce Apollo-MILP, compare it with other approaches under a common framework, and conduct more systematic experiments on ML for MILP.
- Python 3.9
- PyTorch 1.12.1
- torch-geometric 2.5.3
- pyscipopt 4.2.0
- gurobipy 9.5.2
- ecole 0.8.1
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torch-cluster 1.6.0+pt112cu113 pypi_0 pypi
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xz 5.4.6 h5eee18b_1
yarl 1.9.4 pypi_0 pypi
zipp 3.19.2 pypi_0 pypi
zlib 1.2.13 h4ab18f5_6 conda-forge
zstd 1.5.6 ha6fb4c9_0 conda-forge
python Apollo.py -p [problem_name]
If you find this code useful, please consider citing the following papers.
@inproceedings{
liu2025apollomilp,
title={Apollo-{MILP}: An Alternating Prediction-Correction Neural Solving Framework for Mixed-Integer Linear Programming},
author={Haoyang Liu and Jie Wang and Zijie Geng and Xijun Li and Yuxuan Zong and Fangzhou Zhu and Jianye HAO and Feng Wu},
booktitle={The Thirteenth International Conference on Learning Representations},
year={2025},
url={https://openreview.net/forum?id=mFY0tPDWK8}
}