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A Turing Kernelization Dichotomy for Structural Parameterizations of F-Minor-Free Deletion

机译:F次要缺失的结构化参数的图灵核化二分法

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For a fixed finite family of graphs F the F-Minor-Free Deletion problem takes as input a graph G and an integer I and asks whether there exists a set X ⊆ V(G) of size at most I such that G — X is F-minor-free. For F = {K_2} and F = {K_3} this encodes Vertex Cover and Feedback Vertex Set respectively. When parameterized by the feedback vertex number of G these two problems are known to admit a polynomial kernelization. Such a polynomial kernelization also exists for any F containing a planar graph but no forests. In this paper we show that F-Minor-Free Deletion parameterized by the feedback vertex number is MK[2]-hard for F = {P_3}. This rules out the existence of a polynomial kernel assuming NP ⊄ coNP/poly, and also gives evidence that the problem does not admit a polynomial Turing kernel. Our hardness result generalizes to any F not containing a P_3-subgraph-free graph, using as parameter the vertex-deletion distance to treewidth mintw(F), where min tw(F) denotes the minimum treewidth of the graphs in F For the other case, where F contains a P_3-subgraph-free graph, we present a polynomial Turing kernelization. Our results extend to F-Subgraph-Free Deletion.
机译:对于固定的有限图族F,无F次要删除问题采用图G和一个整数I作为输入,并询问是否存在最大为I的集合X⊆V(G),使得G_X为无F小调。对于F = {K_2}和F = {K_3},这分别对“顶点覆盖”和“反馈顶点集”进行编码。当由G的反馈顶点数进行参数化时,这两个问题已知可以进行多项式核化。对于任何包含平面图但不包含森林的F,也都存在这样的多项式核化。在本文中,我们表明,对于F = {P_3},由反馈顶点号参数化的F次要删除是MK [2] -hard。这排除了假设NP⊄coNP / poly的多项式内核的存在,并且还提供了证明该问题不容许多项式Turing内核的证据。我们的硬度结果推广到不包含无P_3-subgraph-free图的任何F,使用到树宽mintw(F)的顶点删除距离作为参数,其中min tw(F)表示F中图的最小树宽。在F包含无P_3子图的情况下,我们提出了多项式Turing核化。我们的结果扩展到F-Subgraph-Free删除。

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