Decision-focused learning (DFL) is an emerging paradigm that integrates machine learning (ML) and constrained optimization to enhance decision quality by training ML models in an end-to-end system. This approach shows significant potential to revolutionize combinatorial decision-making in real-world applications that operate under uncertainty, where estimating unknown parameters within decision models is a major challenge. This paper presents a comprehensive review of DFL, providing an in-depth analysis of both gradient-based and gradient-free techniques used to combine ML and constrained optimization. It evaluates the strengths and limitations of these techniques and includes an extensive empirical evaluation of eleven methods across seven problems. The survey also offers insights into recent advancements and future research directions in DFL. Code and benchmark: https://github.com/PredOpt/predopt-benchmarks

翻译：决策导向学习（DFL）是一种新兴范式，它将机器学习（ML）与约束优化相结合，通过在端到端系统中训练ML模型来提升决策质量。该方法在现实世界不确定性环境下的组合决策应用中展现出巨大潜力，其中决策模型内未知参数的估计是一个主要挑战。本文对DFL进行了全面综述，深入分析了用于融合ML与约束优化的基于梯度与无梯度技术，评估了这些技术的优势与局限，并对七类问题中的十一种方法进行了广泛的实证评估。本综述还探讨了DFL领域的最新进展与未来研究方向。代码与基准：https://github.com/PredOpt/predopt-benchmarks