abfariah / learn2branch

Exact Combinatorial Optimization with Graph Convolutional Neural Networks (NeurIPS 2019)

Update: check out library Ecole, which reimplements everything you'll need for learning to branch, in a nice and clean Python package (paper here).

Exact Combinatorial Optimization with Graph Convolutional Neural Networks

Maxime Gasse, Didier Chételat, Nicola Ferroni, Laurent Charlin, Andrea Lodi

This is the official implementation of our NeurIPS 2019 paper.

Installation

See installation instructions here.

Running the experiments

Set Covering

# Generate MILP instances
python 01_generate_instances.py setcover
# Generate supervised learning datasets
python 02_generate_samples.py setcover -j 4  # number of available CPUs
# Training
for i in {0..4}
do
    python 03_train_gcnn.py setcover -m baseline -s $i
    python 03_train_gcnn.py setcover -m mean_convolution -s $i
    python 03_train_gcnn.py setcover -m no_prenorm -s $i
    python 03_train_competitor.py setcover -m extratrees -s $i
    python 03_train_competitor.py setcover -m svmrank -s $i
    python 03_train_competitor.py setcover -m lambdamart -s $i
done
# Test
python 04_test.py setcover
# Evaluation
python 05_evaluate.py setcover

Combinatorial Auction

# Generate MILP instances
python 01_generate_instances.py cauctions
# Generate supervised learning datasets
python 02_generate_samples.py cauctions -j 4  # number of available CPUs
# Training
for i in {0..4}
do
    python 03_train_gcnn.py cauctions -m baseline -s $i
    python 03_train_competitor.py cauctions -m extratrees -s $i
    python 03_train_competitor.py cauctions -m svmrank -s $i
    python 03_train_competitor.py cauctions -m lambdamart -s $i
done
# Test
python 04_test.py cauctions
# Evaluation
python 05_evaluate.py cauctions

Capacitated Facility Location

# Generate MILP instances
python 01_generate_instances.py facilities
# Generate supervised learning datasets
python 02_generate_samples.py facilities -j 4  # number of available CPUs
# Training
for i in {0..4}
do
    python 03_train_gcnn.py facilities -m baseline -s $i
    python 03_train_competitor.py facilities -m extratrees -s $i
    python 03_train_competitor.py facilities -m svmrank -s $i
    python 03_train_competitor.py facilities -m lambdamart -s $i
done
# Test
python 04_test.py facilities
# Evaluation
python 05_evaluate.py facilities

Maximum Independent Set

# Generate MILP instances
python 01_generate_instances.py indset
# Generate supervised learning datasets
python 02_generate_samples.py indset -j 4  # number of available CPUs
# Training
for i in {0..4}
do
    python 03_train_gcnn.py indset -m baseline -s $i
    python 03_train_competitor.py indset -m extratrees -s $i
    python 03_train_competitor.py indset -m svmrank -s $i
    python 03_train_competitor.py indset -m lambdamart -s $i
done
# Test
python 04_test.py indset
# Evaluation
python 05_evaluate.py indset

Citation

Please cite our paper if you use this code in your work.

@inproceedings{conf/nips/GasseCFCL19,
  title={Exact Combinatorial Optimization with Graph Convolutional Neural Networks},
  author={Gasse, Maxime and Chételat, Didier and Ferroni, Nicola and Charlin, Laurent and Lodi, Andrea},
  booktitle={Advances in Neural Information Processing Systems 32},
  year={2019}
}

Questions / Bugs

Please feel free to submit a Github issue if you have any questions or find any bugs. We do not guarantee any support, but will do our best if we can help.