We provide the code to learn to solve four standard combinatorial optimization problems:
- the (Euclidian and Asymetric) Traveling Salesman Problems (TSPs)
- the Capacitated Vehicle Routing Problem (CVRP)
- the Orienteering Problem (OP)
- the Knapsack Problem (KP)
For BQ-Perceiver code, please check bq-perceiver branch.
See BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial Optimization for the paper associated with this codebase. If you find this code useful, please cite our paper as:
@inproceedings{
drakulic2023bqnco,
title={BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial Optimization},
author={Darko Drakulic and Sofia Michel and Florian Mai and Arnaud Sors and Jean-Marc Andreoli},
booktitle={Advances in Neural Information Processing Systems},
year={2023},
url={https://arxiv.org/abs/2301.03313},
}
For data preparation, check data directory.
Using (near-) optimal trajectories
python train_[tsp,cvrp,op,kp].py
--train_dataset TRAIN_DATASET
--val_datasets VAL_DATASET
--test_datasets TEST_DATASET
--output_dir OUTPUT_DIR
To test our pretrained models:
python test_[tsp,cvrp,op,kp].py
--path_to_model_to_test ./pretrained_models/[tsp,cvrp,op,kp].best
--test_datasets TEST_DATASET
--output_dir OUTPUT_DIR
--test_only
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent