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arXiv 0707.0134math.CO
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边删除问题的加法逼近

Additive approximation for edge-deletion problems

Noga Alon, Asaf Shapira, Benny Sudakov

摘要:

一个图的性质是单调的,如果它是关闭的下删除的顶点和边。本文考虑如下边删除问题:给定单调性P和图G,计算使G成为满足P的图所需的最小边删除数,记为E_P(G)。我们的第一个结果表明,对于任何单调图性质P,任何ε>0和n-顶点输入图G,我们可以逼近E_P(G),直到εn^2的加性误差。我们的第二个主要结果表明,这种逼近本质上是最好的可能性,并且对于大多数性质,对任何固定的正δ,逼近E_P(G)直到n^{2-δ}的加性误差是NP-困难的。证明需要几个新的组合思想,并涉及工具,从极值图论与频谱技术。有趣的是,在这项工作之前,甚至不知道精确计算E_P(G)的稠密单调性质是NP-困难的。因此,我们(以强有力的形式)回答了1981年提出的Yannakakis问题。

英文摘要:

A graph property is monotone if it is closed under removal of vertices and edges. In this paper we consider the following edge-deletion problem; given a monotone property P and a graph G, compute the smallest number of edge deletions that are needed in order to turn G into a graph satisfying P. We denote this quantity by E_P(G). Our first result states that for any monotone graph property P, any ε>0 and n-vertex input graph G one can approximate E_P(G) up to an additive error of εn^2 Our second main result shows that such approximation is essentially best possible and for most properties, it is NP-hard to approximate E_P(G) up to an additive error of n^{2-δ}, for any fixed positive δ. The proof requires several new combinatorial ideas and involves tools from Extremal Graph Theory together with spectral techniques. Interestingly, prior to this work it was not even known that computing E_P(G) precisely for dense monotone properties is NP-hard. We thus answer (in a strong form) a question of Yannakakis raised in 1981.

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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