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arXiv 2302.02567cs.DS
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一般图的随机最小点覆盖:一个$3/2$ -逼近

Stochastic Minimum Vertex Cover in General Graphs: a $3/2$-Approximation

Mahsa Derakhshan, Naveen Durvasula, Nika Haghtalab

摘要:

我们的主要结果是设计一个算法,返回一个顶点覆盖的$\mathcal{G}^\star$的大小最多$(3/2+ε)$倍的预期大小的最小顶点覆盖,只使用$O(n/εp)$非自适应查询。这改进了Behnezhad,Blum和Derakhshan [SODA'22]的最著名的2-近似算法,他们还表明$Ω(n/p)$查询是实现任何常数近似所必需的。我们的保证还扩展到边缘实现不完全独立的情况。我们补充这个上限与紧$3/2$ -近似随机图的边缘实现表现出温和的相关性的下限。

英文摘要:

Our main result is designing an algorithm that returns a vertex cover of $\mathcal{G}^\star$ with size at most $(3/2+ε)$ times the expected size of the minimum vertex cover, using only $O(n/εp)$ non-adaptive queries. This improves over the best-known 2-approximation algorithm by Behnezhad, Blum, and Derakhshan [SODA'22], who also show that $Ω(n/p)$ queries are necessary to achieve any constant approximation. Our guarantees also extend to instances where edge realizations are not fully independent. We complement this upper bound with a tight $3/2$-approximation lower bound for stochastic graphs whose edges realizations demonstrate mild correlations.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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