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.

翻译：许多实际决策过程可建模为优化问题，其定义参数未知且需从观测数据中推断。预测-优化框架通过机器学习模型，在求解优化问题前从特征中预测其未知参数。近期研究表明，通过在训练过程中求解并微分优化问题，可直接基于决策结果定义损失函数实现端到端训练，从而提升决策质量。然而该方法效率低下，且需针对特定问题手工设计通过优化步骤的反向传播规则。本文提出替代方法：通过预测模型直接从观测特征学习最优解。该方案具有通用性，基于学习优化范式的改进策略，可整合现有丰富技术。实验评估表明，多种学习优化方法能为一系列具有挑战性的预测-优化问题提供高效、精确且灵活的解决方案。