SymILO: A Symmetry-Aware Learning Framework for Integer Linear Optimization

This repository contains the code for the NeurIPS 2024 paper: SymILO: A Symmetry-Aware Learning Framework for Integer Linear Optimization.

Environment Setup

To run this code, you need the following dependencies:

• Python 3.9
• CPLEX 22.2.0
• PyTorch 2.0.1

Training the Model

To train the model, you can use the following bash commands:

dataset=IP # indicate the dataset
python train.py --expName $dataset --dataset $dataset --opt mean --epoch 50

Evaluation

To evaluate the model, you can use the following commands:

dataset=IP
python eval.py --expName $dataset --dataset $dataset --method fixTop
python eval.py --expName $dataset --dataset $dataset --method PS
python eval.py --expName $dataset --dataset $dataset --method node_selection

Citation

If you find this code helpful in your research, please consider citing our paper:

@article{chen2024symilo,
  title={SymILO: A Symmetry-Aware Learning Framework for Integer Linear Optimization},
  author={Chen, Qian and Zhang, Tianjian and Yang, Linxin and Han, Qingyu and Wang, Akang and Sun, Ruoyu and Luo, Xiaodong and Chang, Tsung-Hui},
  journal={NeurIPS},
  year={2024}
}