PyEPO: A PyTorch-based End-to-End Predict-then-Optimize Library for Linear and Integer Programming

ArXi:2206.14234v3 Announce Type: replace-cross In deterministic optimization, it is typically assumed that all problem parameters are fixed and known. In practice, however, some parameters may be a priori unknown but can be estimated from contextual information. A typical predict-then-optimize approach separates predictions and optimization into two distinct stages. Recently, end-to-end predict-then-optimize has emerged as an attractive alternative. This work