直接学习预测与优化联合模型，提升决策效率
Learning Joint Models of Prediction and Optimization
- 用联合模型直接从特征预测最优解，跳过传统优化步骤
- 在多个复杂问题上实现高精度与高效求解
- 无需手工设计反向传播规则，通用性强适合广泛场景
预测-然后优化框架通过机器学习模型从外部特征预测优化问题的未知参数，再进行求解。该设置常见于诸多现实决策过程，近期研究表明，在端到端训练中求解并微分优化问题可显著提升决策质量。然而，该方法需额外计算开销，且依赖针对具体问题的手动设计反向传播规则，限制了其在广泛优化问题中的应用。本文提出一种替代方法：通过联合预测模型直接从可观测特征学习最优解。该方法具有通用性，基于对学习-优化范式的改进，可采用大量现有技术。实验表明，多种学习-优化方法能有效解决一系列挑战性的预测-然后优化问题。
原文摘要 · Abstract (English)
The Predict-Then-Optimize framework uses machine learning models to predict unknown parameters of an optimization problem from exogenous features before solving. This setting is common to many real-world decision processes, and recently it has been shown that decision quality can be substantially improved by solving and differentiating the optimization problem within an end-to-end training loop. However, this approach requires significant computational effort in addition to handcrafted, problem-specific rules for backpropagation through the optimization step, challenging its applicability to a broad class of optimization problems. This paper proposes an alternative method, in which optimal solutions are learned directly from the observable features by joint 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 and accurate solutions to an array of challenging Predict-Then-Optimize problems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。