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A Randomised Approximation Algorithm for the Partial Vertex Cover Problem in Hypergraphs

机译:超图中部分顶点覆盖问题的随机逼近算法。

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In this paper we present an approximation algorithm for the k-partial vertex cover problem in hypergraphs. Let H = (V,ε) be a hypergraph with set of vertices V, |V| = n and set of (hyper-)edges |ε|,|ε| = m. The k-partial vertex cover problem in hypergraphs is the problem of finding a minimum cardinality subset of vertices in which at least k hyperedges are incident. It is a generalisation of the fundamental (partial) vertex cover problem in graphs and the hitting set problem in hypergraphs. Let l,l ≥ 2 be the maximum size of an edge, Δ be the maximum vertex degree and D be maximum edge degree. For a constant l,l ≥ 2 a non-approximabilty result is known: an approximation ratio better than l cannot be achieved in polynomial-time under the unique games conjecture (Khot and Rageev 2003, 2008). On the other hand, with the primal-dual method (Gandhi, Khuller, Srinivasan 2001) and the local-ratio method (Bar-Yehuda 2001), the l-approximation ratio can be proved. Thus approximations below the Z-ratio for large classes of hypergraphs, for example those with constant D or Δ are interesting. In case of graphs (I = 2) such results are known. In this paper we break the l-approximation barrier for hypergraph classes with constant D resp. Δ for the partial vertex cover problem in hypergraphs. We propose a randomised algorithm of hybrid type which combines LP-based randomised rounding and greedy repairing. For hypergraphs with arbitrary l, l ≥ 3, and constant D the algorithm achieves an approximation ratio of l(1 - Ω(1/(D +1))), and this can be improved to l(1 - Ω(1/Δ)) if A is constant and k ≥ m/4. For the class of l-uniform hypergraphs with both l and Δ being constants and l ≤ 4Δ, we get a further improvement to a ratio of l (1 - l-1/4Δ). The analysis relies on concentration inequalities and combinatorial arguments.
机译:在本文中,我们为超图中的k部分顶点覆盖问题提供了一种近似算法。令H =(V,ε)为顶点为V,| V |的超图。 = n和(超)边的集合|ε|,|ε| =米超图中的k部分顶点覆盖问题是找到至少有k个超边沿入射的顶点的最小基数子集的问题。它是图形中基本(部分)顶点覆盖问题和超图形中命中集问题的概括。令l,l≥2是边缘的最大大小,Δ是最大顶点度,D是最大边缘度。对于恒定的l,l≥2,已知一个不近似的结果:在唯一的游戏猜想下,多项式时间内无法获得比l更好的近似比率(Khot和Rageev 2003,2008)。另一方面,使用原始对偶方法(Gandhi,Khuller,Srinivasan 2001)和局部比率方法(Bar-Yehuda 2001),可以证明l逼近比。因此,对于大类超图(例如具有恒定D或Δ的超图),Z值以下的近似值很有趣。在图形(I = 2)的情况下,这样的结果是已知的。在本文中,我们打破了具有恒定D表示的超图类的l逼近壁垒。超图中的部分顶点覆盖问题的Δ。我们提出了一种混合类型的随机算法,该算法结合了基于LP的随机舍入和贪婪修复。对于具有任意l,l≥3和常数D的超图,该算法获得的逼近比为l(1-Ω(1 /(D +1))),可以将其提高为l(1-Ω(1 / Δ))如果A为常数且k≥m / 4。对于l和Δ均为常数且l≤4Δ的l一致超图的类,我们进一步提高了比率l(​​1-1-1/4)。分析依赖于浓度不等式和组合论证。

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