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新研究定义了持续子模最大化的最优阈值

一篇题为“具有完全先知的持续子模最大化”的新研究论文探讨了具有有限追索权的在线算法的挑战。该研究侧重于单调子模最大化,其中算法必须在有限的更改次数内维护最多 k 个元素的解决方案。研究人员为确定性算法建立了 3/4 的最优近似阈值,为随机算法建立了约 0.7942 的最优近似阈值,并证明了覆盖函数的匹配不可能结果。 AI

排序理由 该集群包含一篇发表在 arXiv 上的学术论文,详细介绍了理论计算机科学研究。[lever_c_demoted from research: ic=1 ai=0.4]

在 arXiv cs.LG 阅读 →

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新研究定义了持续子模最大化的最优阈值

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该集群包含一篇发表在 arXiv 上的学术论文,详细介绍了理论计算机科学研究。[lever_c_demoted from research: ic=1 ai=0.4]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Eric Balkanski, Paul D\"utting, Federico Fusco, Silvio Lattanzi, Ashkan Norouzi-Fard, Ola Svensson, Morteza Zadimoghaddam ·

    具有完全先知的持续次模最大化

    arXiv:2611.06223v1 Announce Type: cross Abstract: Online algorithms with bounded recourse face three intertwined obstacles: uncertainty about the future, computational limits, and a limited budget for changes. We ask a fundamental question that separates consistency from the othe…