End-to-end learning via constraint-enforcing approximators for linear programs with applications to supply chains
Published in AAAI-23 Main Track, 2022
Recommended citation: Cristian, R., Harsha, P., Perakis, G., Quanz, B. L., & Spantidakis, I. (2022). End-to-End Learning via Constraint-Enforcing Approximators for Linear Programs with Applications to Supply Chains.
In many real-world applications, predictive methods are used to provide inputs for downstream optimization problems. It has been shown that using the downstream task-based objective to learn the intermediate predictive model is often better than using only intermediate task objectives, such as prediction error. The difficulty in end-to-end learning lies in differentiating through the optimization problem. Therefore, we propose a neural network architecture that can learn to approximately solve these optimization problems, particularly ensuring its output satisfies the feasibility constraints via alternate projections.