G2MILP: A Deep Instance Generative Framework for MILP Solvers Under Limited Data Availability

This is the code of paper "A Deep Instance Generative Framework for MILP Solvers Under Limited Data Availability". Zijie Geng, Xijun Li, Jie Wang, Xiao Li, Yongdong Zhang, Feng Wu. NeurIPS 2023 (Spotlight).

Page: https://miralab-ustc.github.io/L2O-G2MILP/

Environment

• Python environment

  • python 3.7
  • pytorch 1.13
  • torch-geometric 2.3
  • ecole 0.7.3
  • pyscipopt 3.5.0
  • community 0.16
  • networkx
  • pandas
  • tensorboardX
  • gurobipy

• MILP Solver

  • Gurobi 10.0.1. Academic License.

• Hydra

  • Hydra for managing hyperparameters and experiments.

In order to build the environment, you can follow commands in scripts/environment.sh.

Or alternatively, to build the environment from a file,

conda env create -f scripts/environment.yml

Usage

Go to the root directory L2O-G2MILP. Put the datasets under the ./data directory. Below is an illustration of the directory structure.

L2O-G2MILP
├── conf
├── data
│   ├── mik
│   │   ├── train/
│   │   └── test/
│   ├── mis
│   │   ├── train/
│   │   └── test/
│   └── setcover
│       ├── train/
│       └── test/
├── scripts/
├── src/
├── README.md
├── benchmark.py
├── generate.py
├── preprocess.py
└── train.py

The hyperparameter configurations are in ./conf/. The commands to run for all datasets are in ./scripts/. The main part of the code is in ./src/. The workflow of G2MILP (using MIS as an example) is as following.

1. Preprocessing

To preprocess a dataset,

python preprocess.py dataset=mis num_workers=10

This will produce graph data for instances and the statistics of the dataset to be used for training. The preprocessed results are saved under ./preprocess/mis/.

2. Training G2MILP

To train G2MILP with default parameters,

python train.py dataset=mis cuda=0 num_workers=10 job_name=mis-default

The training log is saved under TRAIN DIR=./outputs/train/${DATE}/${TIME}-${JOB NAME}/. The model ckpts are saved under ${TRAIN DIR}/model/. The generated instances and benchmarking results are saved under ${TRAIN DIR}/eta-${eta}/.

3. Generating new instances

To generate new instances with a trained model,

python generate.py dataset=mis \
    generator.mask_ratio=0.01 \
    cuda=0 num_workers=10 \
    dir=${TRAIN DIR}

The generated instances and benchmarking results are saved under ${TRAIN DIR}/generate/${DATE}/${TIME}.

Citation

If you find this code useful, please consider citing the following paper.

@inproceedings{
anonymous2023a,
title={A Deep Instance Generative Framework For {MILP} Solvers Under Limited Data Availability},
author={Anonymous},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=AiEipk1X0c}
}