The Smart Predict+Optimize (SPO) framework tries to solve a decision-making problem expressed as mathematical optimization in which some coefficients have to be estimated by a predictive model. The challenge is that this problem is non-convex and non-differentiable, even for linear programs with linear predictive models. Despite that, we provide the first exact optimal solution to the SPO problem by formulating it as a bi-level bi-linear program and reducing it to a mixed-integer linear program (MILP) using a novel symbolic method.
