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Exact Combinatorial Optimization with Graph Convolutional Neural Networks (NeurIPS 2019)
| Date | Stars |
|---|---|
| 2026-07-31 | 412 |
| 2026-08-03 | 413 |
| 2026-08-04 | 415 |
| 2026-08-05 | 415 |
| 2026-08-06 | 415 |
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__Update__: check out library [Ecole](https://doc.ecole.ai), which reimplements everything you'll need for learning to branch, in a nice and clean Python package ([paper here](https://arxiv.org/abs/2011.06069)).
# 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](https://arxiv.org/abs/1906.01629).
## Installation
See installation instructions [here](INSTALL.md).
## 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.
Excerpt of 3,531 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:a9142e21cccc2997, llm:Repository title and description: 'Exact Combinatorial Optimization with Graph Convolutional Neural Networks (NeurIPS 2019)'; topic: neurips-2019. Likely implements ML methods (graph convolutional networks) for combinatorial optimization / branching in solvers.
matched fp:a9142e21cccc2997, llm:Repository title and description: 'Exact Combinatorial Optimization with Graph Convolutional Neural Networks (NeurIPS 2019)'; topic: neurips-2019. Likely implements ML methods (graph convolutional networks) for combinatorial optimization / branching in solvers.
matched fp:a9142e21cccc2997, llm:Repository title and description: 'Exact Combinatorial Optimization with Graph Convolutional Neural Networks (NeurIPS 2019)'; topic: neurips-2019. Likely implements ML methods (graph convolutional networks) for combinatorial optimization / branching in solvers.