ACM-MILP: Adaptive Constraint Modification via Grouping and Selection for Hardness-Preserving MILP Instance Generation

This is the code of paper "ACM-MILP: Adaptive Constraint Modification via Grouping and Selection for Hardness-Preserving MILP Instance Generation". Ziao Guo, Yang Li, Chang Liu, Wenli Ouyang, Junchi Yan. ICML 2024.

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. Put the datasets under the ./data directory. Below is an illustration of the directory structure.

ACM-MILP
├── conf
├── data
│   ├── ca
│   │   ├── train/
│   │   └── test/
│   ├── mis
│   │   ├── train/
│   │   └── test/
│   └── setcover
│       ├── train/
│       └── test/
├── scripts/
├── src/
├── README.md
├── 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 ACM-MILP (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 ACM-MILP

To train ACM-MILP 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{
guo2024acmmilp,
title={ACM-MILP: Adaptive Constraint Modification via Grouping and Selection for Hardness-Preserving MILP Instance Generation},
author={Ziao Guo, Yang Li, Chang Liu, Wenli Ouyang, Junchi Yan},
booktitle={Forty-first International Conference on Machine Learning},
year={2024}
}