English(EN) When Weak Submodularity Meets Matroids: How Does Standard Greedy Behave?

为弱次模最大化中的贪心算法建立了新的界限

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-21 04:00

研究人员 Qixin Zhang 等人分析了标准贪心算法在具有拟阵约束的弱次模最大化问题上的性能。他们的工作为算法的近似保证（表示为 $R_{\mathrm{gr}}(\gamma,r)$）建立了精炼的下界和上界。这些发现改进了先前的保证，并有效地缩紧了某些参数的上界，从而更精确地理解了该算法在这些复杂优化场景中的最坏情况性能。 AI

影响为与机器学习和数据挖掘相关的优化算法提供了理论进展。

排序理由学术论文发表在 arXiv 上，详细介绍了理论计算机科学研究。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Qixin Zhang, Shi Fu, Shengminjie Chen, Chao Qian, Dacheng Tao · 2026-10-21 04:00

当弱次模性遇上拟阵：标准贪心算法表现如何？

arXiv:2610.26967v1 Announce Type: cross Abstract: Weakly submodular maximization finds numerous applications in machine learning, data mining and statistics. In particular, under a cardinality constraint, the standard greedy algorithm is known to attain the tight $(1-e^{-\gamma})…

本文如何被排名

报道来源 [1]

当弱次模性遇上拟阵：标准贪心算法表现如何？

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