Predict-Then-Optimize by Proxy: Learning Joint Models of Prediction and Optimization
Predict-Then-Optimize by Proxy: Learning Joint Models of Prediction and Optimization
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DOI：
10.48550/arxiv.2311.13087
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发表时间：
2023-11
期刊：
影响因子：
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通讯作者：
James Kotary;Vincenzo Di Vito;Jacob Christopher;P. V. Hentenryck;Ferdinando Fioretto
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文献类型：
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作者：
James Kotary;Vincenzo Di Vito;Jacob Christopher;P. V. Hentenryck;Ferdinando Fioretto
许多现实世界的决策过程是由优化问题建模的，其定义参数是未知的，必须从可观察的数据中推断出来。Predict-Then-Optimize框架使用机器学习模型在求解之前从特征预测优化问题的未知参数。最近的工作表明，在这种情况下，可以通过解决和区分训练循环中的优化问题来提高决策质量，从而实现端到端训练，损失函数直接定义在最终决策上。然而，这种方法可能是低效的，并且需要手工制作的、特定于问题的规则来通过优化步骤进行反向传播。本文提出了一种替代方法，在该方法中，通过预测模型直接从可观察特征中学习最优解。该方法是通用的，并基于适应的学习优化范式，从丰富的各种现有的技术可以采用。实验评估表明，几个学习优化方法提供高效，准确，灵活的解决方案，一系列具有挑战性的预测，然后优化问题的能力。
Many real-world decision processes are modeled by optimization problems whose defining parameters are unknown and must be inferred from observable data. The Predict-Then-Optimize framework uses machine learning models to predict unknown parameters of an optimization problem from features before solving. Recent works show that decision quality can be improved in this setting by solving and differentiating the optimization problem in the training loop, enabling end-to-end training with loss functions defined directly on the resulting decisions. However, this approach can be inefficient and requires handcrafted, problem-specific rules for backpropagation through the optimization step. This paper proposes an alternative method, in which optimal solutions are learned directly from the observable features by predictive models. The approach is generic, and based on an adaptation of the Learning-to-Optimize paradigm, from which a rich variety of existing techniques can be employed. Experimental evaluations show the ability of several Learning-to-Optimize methods to provide efficient, accurate, and flexible solutions to an array of challenging Predict-Then-Optimize problems.