English(EN) Learning-Augmented Approximation for Unrelated-Machines Makespan Scheduling

新算法使用预测来改进机器调度

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-05 05:00

研究人员为无关联机器上的 makespan 最小化问题（一个复杂的调度挑战）开发了一种新的带学习增强的算法。该方法利用作业分配的预测来实现多项式时间近似，在预测准确时提供 $(1+\varepsilon)$-近似，并在预测误差增加时退化到 2-近似。这项工作扩展了先前应用于选择问题的框架，并包括了对该方法性能的实证分析。 AI

影响引入了一种新颖的算法方法，可以提高复杂调度任务的效率。

排序理由关于一种新颖调度问题的算法的学术论文。[lever_c_demoted from research: ic=1 ai=0.4]

在 arXiv cs.LG 阅读 →

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Signal score: 10 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Kaito Baba, Evripidis Bampis, Giorgos Mitropoulos · 2026-11-05 05:00

为无关联机器 makespan 调度引入学习增强近似算法

arXiv:2606.13133v2 Announce Type: replace-cross Abstract: Recently, Antoniadis et al. (ICLR 2025) proposed a framework for incorporating predictions to approximate NP-hard selection problems. Despite its simplicity, this approach tightly matches theoretical lower bounds, making i…

本文如何被排名

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为无关联机器 makespan 调度引入学习增强近似算法

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