PyEPO is presented as the first generic PyTorch library implementing surrogate losses, black-box solvers, and perturbed methods for end-to-end predict-then-optimize on linear and integer programs.
In: Advances in Neural Information Processing Systems 28 (2015), pp 2962– 2970
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PyEPO: A PyTorch-based End-to-End Predict-then-Optimize Library for Linear and Integer Programming
PyEPO is presented as the first generic PyTorch library implementing surrogate losses, black-box solvers, and perturbed methods for end-to-end predict-then-optimize on linear and integer programs.