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General and Fractional Hypertree Decompositions: Hard and Easy Cases (Invited Talk)

机译:一般和分数阶超树分解:难易案例(特邀演讲)

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Hypertree decompositions [5, 4], the more powerful generalized hypertree decompositions (GHDs) [5, 4], and the yet more general fractional hypertree decompositions (FHDs) [8, 9] are hypergraph decomposition methods successfully used for answering conjunctive queries and for solving constraint satisfaction problems. For a survey on these decompositions, see [3], for exact hypertree decomposition algorithms [7], and for heuristic decomposition methods, see [1]. Each hypergraph H has a width relative to each of these methods: its hypertree width hw(H), its generalized hypertree width ghw{H), and its fractional hypertree width fhw{H), respectively. While hw{H) ≤ k can be checked in polynomial time, the complexity of checking whether fhw(H) ≤ k holds for a fixed constant k was unknown. We settle this problem by proving that checking whether fhw(H) ≤ k is NP-complete, even for k = 2 and by same construction also the problem deciding whether ghw(H) ≤ k is NP-complete for k ≥ 2. Hardness was previously known for k ≥ 3 [6], whilst the case k = 2 has remained open since 2001. Given these hardness results, we investigate meaningful restrictions, for which checking for bounded ghw is easy. We study classes of hypergraphs that enjoy the bounded edge-intersection property (BIP) and the more general bounded multi-edge intersection property (BMIP). For such classes, for each constant k, checking whether ghw{H) ≤ k, and if so, computing a GHD of width k of H is tractable and actually FPT. Finally we derive some approx-imability results for fhw. We consider classes of hypergraphs whose fhw is bounded by a constant k and which also enjoy the BIP or M1P, or bounded VC-dimension. For each hypergraph in such a class, we are able to compute an FHD of width 0(k log k) efficiently. A different restriction on classes of hypergraphs gives a linear approximation in PTIME. Hypergraphs of bounded rank are a simple example of such a class. A full paper [2] with these and further results is available online via the link https://arxiv.org/abs/1611.01090.
机译:超树分解[5,4],功能更强大的广义超树分解(GHD)[5,4]和更通用的分数超树分解(FHD)[8,9]是成功用于回答联合查询和查询的超图分解方法。解决约束满足问题。有关这些分解的调查,请参见[3],有关确切的超树分解算法[7],以及启发式分解方法,请参见[1]。每个超图H的宽度均相对于以下每种方法:其超树宽度hw(H),其广义超树宽度ghw {H)和其分数超树宽度fhw {H)。虽然可以在多项式时间内检查hw {H)≤k,但对于固定常数k而言,检查fhw(H)≤k是否成立的复杂性是未知的。我们通过证明fhw(H)≤k是否为NP完全来解决这个问题,即使对于k = 2,并且通过相同的构造,也要确定kw≥2的ghw(H)≤k是否是NP完全的问题。以前已知k≥3 [6],而k = 2的情况自2001年以来一直处于打开状态。鉴于这些硬度结果,我们研究了有意义的限制,对于这些限制,容易检查出边界ghw。我们研究享有有界边相交属性(BIP)和更一般的有界多边相交属性(BMIP)的超图类。对于此类,对于每个常数k,检查ghw(H)≤k,如果是,则计算H的宽度k的GHD很容易,而且实际上是FPT。最后,我们得出fhw的近似近似结果。我们考虑超图的类,它们的fhw以常数k为界,并且还具有BIP或M1P或有界VC维。对于此类中的每个超图,我们能够有效地计算出宽度为0(k log k)的FHD。对超图的类别的不同限制给出了PTIME中的线性近似。有界秩的超图就是此类的一个简单示例。可通过链接https://arxiv.org/abs/1611.01090在线获得包含这些结果和更多结果的全文[2]。

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