用对偶预测提升在线优化算法性能，稳定性更强。
Learning-Augmented Online Minimization with Dual Predictions
- 利用对偶线性规划的预测改进在线算法
- 在k服务器和停车许可问题上表现更优
- 适合处理相似实例的在线优化任务
我们提出了两类通用在线最小化问题——度量任务系统和层状集合覆盖的学习增强算法。这两类算法利用机器学习预测的对偶线性规划最优解，获得了更优的理论保证。与极易受微小扰动影响的原始解不同，对偶解具有更高的稳定性，使得在相似实例族中存在良好且可学习的预测。尽管此前已有研究在离线场景及在线最大化问题中使用对偶预测，但据我们所知，这是首次证明对偶预测在在线最小化问题中的有效性。理论结果通过在k-服务器问题和停车许可问题上的实验得到验证。
原文摘要 · Abstract (English)
We present learning-augmented algorithms for two general classes of online minimization problems: metrical task systems and laminar set cover. Both algorithms achieve improved theoretical guarantees using machine-learned predictions of an optimal solution to the dual linear program. Unlike optimal primal solutions, which can change drastically under tiny instance perturbations, these dual solutions are much more stable, which ensures the existence of good (and learnable) predictions for families of similar instances. While previous work has used dual predictions in offline settings and for online maximization problems, our algorithms are, to the best of our knowledge, the first demonstration that such dual predictions can be effective for online minimization. Our theoretical results are complemented by experiments on the $k$-server problem and the parking permit problem.
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