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Tight Kernel Bounds for Problems on Graphs with Small Degeneracy (Extended Abstract)

机译:小退化图上的问题的紧核界(扩展摘要)

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Kernelization is a strong and widely-applied technique in parameterized complexity. In a nutshell, a kernelization algorithm for a parameterized problem transforms a given instance of the problem into an equivalent instance whose size depends solely on the parameter. Recent years have seen major advances in the study of both upper and lower bound techniques for kernelization, and by now this area has become one of the major research threads in parameterized complexity. We consider kernelization for problems on d-degenerate graphs, i.e. graphs such that any subgraph contains a vertex of degree at most d. This graph class generalizes many classes of graphs for which effective kernelization is known to exist, e.g. planar graphs, H-minor free graphs, H-topological minor free graphs. We show that for several natural problems on d-degenerate graphs the best known kernelization upper bounds are essentially tight. In particular, using intricate constructions of weak compositions, we prove that unless NP (∈) coNP/poly: 1.1 Dominating Set has no kernels of size O(k~((d-1)(d-3)-ε)) for any ε > 0. The current best upper bound is O(k~((d+1)~2)). 1.2 Independent Dominating Set has no kernels of size O(k~(d-4-ε)) for any ε > 0. The current best upper bound is O(k~(d+1)). 1.3 Induced Matching has no kernels of size O(k~(d-3-ε)) for any ε > 0. The current best upper bound is O(k~d). We also give simple kernels for Connected Vertex Cover and Capacitated Vertex Cover of size O(k~d) and O(k~(d+1)) respectively. Both these problems do not have kernels of size O(k~(d-1-ε)) unless coNP/poly. In this extended abstract we will focus on the lower bound for Dominating Set, which we feel is the central result of our study. The proofs of the other results can be found in the full version of the paper.
机译:内核化是参数化复杂性中的一种强大且广泛应用的技术。简而言之,用于参数化问题的内核化算法将问题的给定实例转换为等效实例,其大小仅取决于参数。近年来,在用于内核化的上限和下限技术的研究中取得了重大进展,到目前为止,该领域已成为参数化复杂性的主要研究线程之一。我们考虑针对d退化图(即任何子图最多包含d个度数的顶点)的图的问题进行核化。该图类归纳了已知存在有效核化的许多图类,例如图2。平面图,H次要自由图,H拓扑次要自由图。我们表明,对于d退化图上的几个自然问题,最著名的核化上限基本上是紧密的。特别地,使用弱组成的复杂构造,我们证明除非NP(ε)coNP / poly:1.1支配集不具有大小为O(k〜((d-1)(d-3)-ε))的核任何ε>0。当前的最佳上限为O(k〜((d + 1)〜2))。 1.2对于任何ε> 0,独立控制集都没有大小为O(k〜(d-4-ε))的内核。当前的最佳上限是O(k〜(d + 1))。 1.3对于任何ε> 0,诱导匹配都没有大小为O(k〜(d-3-ε))的内核。当前的最佳上限是O(k〜d)。我们还分别给出了大小分别为O(k〜d)和O(k〜(d + 1))的Connected Vertex Cover和Capacapated Vertex Covering的简单内核。这两个问题都没有大小为O(k〜(d-1-ε))的内核,除非有coNP / poly。在这个扩展的摘要中,我们将集中于控制集的下限,我们认为这是我们研究的主要结果。其他结果的证明可以在论文的完整版本中找到。

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