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Consider the setting of constrained optimization, with some parametersunknown at solving time and requiring prediction from relevant features.Predict+Optimize is a recent framework for end-to-end training supervisedlearning models for such predictions, incorporating information about theoptimization problem in the training process in order to yield betterpredictions in terms of the quality of the predicted solution under the trueparameters. Almost all prior works have focused on the special case where theunknowns appear only in the optimization objective and not the constraints. Huet al.~proposed the first adaptation of Predict+Optimize to handle unknownsappearing in constraints, but the framework has somewhat ad-hoc elements, andthey provided a training algorithm only for covering and packing linearprograms. In this work, we give a new \emph{simpler} and \emph{more powerful}framework called \emph{Two-Stage Predict+Optimize}, which we believe should bethe canonical framework for the Predict+Optimize setting. We also give atraining algorithm usable for all mixed integer linear programs, vastlygeneralizing the applicability of the framework. Experimental resultsdemonstrate the superior prediction performance of our training framework overall classical and state-of-the-art methods.

Two-Stage Predict+Optimize for Mixed Integer Linear Programs with Unknown Parameters in Constraints