Citation and References¶

If you use PyEPO in your research, please cite:

@article{tang2024,
  title={PyEPO: a PyTorch-based end-to-end predict-then-optimize library for linear and integer programming},
  author={Tang, Bo and Khalil, Elias B},
  journal={Mathematical Programming Computation},
  issn={1867-2957},
  doi={10.1007/s12532-024-00255-x},
  year={2024},
  month={July},
  publisher={Springer}
}

If you use the CaVE loss, please also cite:

@inproceedings{tang2024cave,
  title={CaVE: A Cone-Aligned Approach for Fast Predict-then-Optimize with Binary Linear Programs},
  author={Tang, Bo and Khalil, Elias B},
  booktitle={Integration of Constraint Programming, Artificial Intelligence, and Operations Research},
  pages={193--210},
  year={2024},
  publisher={Springer}
}

References¶

[1] Elmachtoub, A. N., & Grigas, P. (2021). Smart “predict, then optimize”. Management Science.
[2] Mandi, J., Stuckey, P. J., & Guns, T. (2020). Smart predict-and-optimize for hard combinatorial optimization problems. Proceedings of the AAAI Conference on Artificial Intelligence.
[3] Vlastelica, M., Paulus, A., Musil, V., Martius, G., & Rolinek, M. (2019). Differentiation of blackbox combinatorial solvers. arXiv preprint arXiv:1912.02175.
[4] Sahoo, S. S., Paulus, A., Vlastelica, M., Musil, V., Kuleshov, V., & Martius, G. (2022). Backpropagation through combinatorial algorithms: Identity with projection works. arXiv preprint arXiv:2205.15213.
[5] Berthet, Q., Blondel, M., Teboul, O., Cuturi, M., Vert, J. P., & Bach, F. (2020). Learning with differentiable perturbed optimizers. Advances in Neural Information Processing Systems, 33, 9508-9519.
[6] Dalle, G., Baty, L., Bouvier, L., & Parmentier, A. (2022). Learning with Combinatorial Optimization Layers: a Probabilistic Approach. arXiv preprint arXiv:2207.13513.
[7] Mulamba, M., Mandi, J., Diligenti, M., Lombardi, M., Bucarey, V., & Guns, T. (2021). Contrastive losses and solution caching for predict-and-optimize. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence.
[8] Mandi, J., Bucarey, V., Mulamba, M., & Guns, T. (2022). Decision-focused learning: through the lens of learning to rank. Proceedings of the 39th International Conference on Machine Learning.
[9] Niepert, M., Minervini, P., & Franceschi, L. (2021). Implicit MLE: backpropagating through discrete exponential family distributions. Advances in Neural Information Processing Systems, 34, 14567-14579.
[10] Minervini, P., Franceschi, L., & Niepert, M. (2023). Adaptive perturbation-based gradient estimation for discrete latent variable models. Proceedings of the AAAI Conference on Artificial Intelligence.
[11] Gupta, V., & Huang, M. (2024). Decision-Focused Learning with Directional Gradients. arXiv preprint arXiv:2402.03256.
[12] Schutte, N., Postek, K., & Yorke-Smith, N. (2023). Robust Losses for Decision-Focused Learning. arXiv preprint arXiv:2310.04328.
[13] Tang, B., & Khalil, E. B. (2024). CaVE: A Cone-Aligned Approach for Fast Predict-then-Optimize with Binary Linear Programs. In Integration of Constraint Programming, Artificial Intelligence, and Operations Research (pp. 193-210).