研究人员开发了一种新的学习增强算法，用于无关机 makespan 最小化问题（表示为 R||Cmax）。该方法将先前用于选择问题的框架扩展到调度领域，旨在通过整合作业分配预测来改进近似比。当预测准确时，该算法可实现 (1+ε) 近似，随着预测误差的增加，近似比会下降到 2。 AI
排序理由 这是一篇详细介绍特定计算问题新算法的研究论文。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
新算法利用学习来改进调度近似比
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Learning-Augmented Approximation for Unrelated-Machines Makespan Scheduling
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 its generalization highly compelling. We address an open qu…
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Learning-Augmented Approximation for Unrelated-Machines Makespan Scheduling
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 its generalization highly compelling. We address an open qu…