Predict+Optimize for Packing and Covering LPs with Unknown Parameters in Constraints

• URL: http://arxiv.org/abs/2209.03668v1
• Date: Thu, 8 Sep 2022 09:28:24 GMT
• Title: Predict+Optimize for Packing and Covering LPs with Unknown Parameters in Constraints
• Authors: Xinyi Hu, Jasper C.H. Lee, Jimmy H.M. Lee
• Abstract summary: We propose a novel and practically relevant framework for the Predict+ setting, but with unknown parameters in both the objective and the constraints. We introduce the notion of a correction function, and an additional penalty term in the loss function, modelling practical scenarios where an estimated optimal solution can be modified into a feasible solution after the true parameters are revealed. Our approach is inspired by the prior work of Mandi and Guns, though with crucial modifications and re-derivations for our very different setting.
• Score: 5.762370982168012
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Predict+Optimize is a recently proposed framework which combines machine learning and constrained optimization, tackling optimization problems that contain parameters that are unknown at solving time. The goal is to predict the unknown parameters and use the estimates to solve for an estimated optimal solution to the optimization problem. However, all prior works have focused on the case where unknown parameters appear only in the optimization objective and not the constraints, for the simple reason that if the constraints were not known exactly, the estimated optimal solution might not even be feasible under the true parameters. The contributions of this paper are two-fold. First, we propose a novel and practically relevant framework for the Predict+Optimize setting, but with unknown parameters in both the objective and the constraints. We introduce the notion of a correction function, and an additional penalty term in the loss function, modelling practical scenarios where an estimated optimal solution can be modified into a feasible solution after the true parameters are revealed, but at an additional cost. Second, we propose a corresponding algorithmic approach for our framework, which handles all packing and covering linear programs. Our approach is inspired by the prior work of Mandi and Guns, though with crucial modifications and re-derivations for our very different setting. Experimentation demonstrates the superior empirical performance of our method over classical approaches.

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