Learning-augmented algorithms have received significant attention in recent years, particularly in the context of online optimization. Motivated by the high computational cost of generating predictions, a growing line of work studies the tradeoff between performance guarantees and the number of predictions used in learning-augmented algorithms for problems such as caching and metrical task systems. In this paper, we extend this line of research to online metric matching by developing parsimonious learning-augmented algorithms and establishing lower bounds on their performance. Our approach extends the Follow-the-Prediction framework to the parsimonious setting by filling in a virtual prediction in the absence of an actual prediction, using an online metric matching algorithm that maintains good intermediate matchings throughout its execution. We complement our theoretical results with an empirical evaluation, demonstrating the practical effectiveness of our approach.

翻译：学习增强算法近年来受到了广泛关注，尤其在在线优化领域。受生成预测的高计算成本启发，越来越多的研究开始探讨性能保证与预测使用数量之间的权衡，这些研究主要针对缓存和度量任务系统等问题中的学习增强算法。在本文中，我们将这一研究方向扩展到在线度量匹配问题，通过开发简约的学习增强算法并建立其性能的下界。我们的方法将“跟随预测”框架扩展到简约场景，通过使用一种在线度量匹配算法在执行过程中维护良好的中间匹配，在缺乏实际预测时填充虚拟预测。我们通过实证评估补充了理论结果，展示了该方法在实际应用中的有效性。