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TIGHT BOUNDS FOR PLANAR STRONGLY CONNECTED STEINER SUBGRAPH WITH FIXED NUMBER OF TERMINALS (AND EXTENSIONS)

机译:平面强度界限强大连接的施坦拌工子图,具有固定数量的终端(和延伸)

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Given a vertex-weighted directed graph G = (V, E) and a set T = {t(1), t(2), ..., t(k)} of k terminals, the objective of the STRONGLY CONNECTED STEINER SUBGRAPH (SCSS) problem is to find a vertex set H subset of V of minimum weight such that G[H] contains a t(i) -> t(j) path for each i not equal j. The prob- lem is NP-hard, but Feldman and Ruhl [SIAM J. Comput., 36 (2006), pp. 543-561] gave a novel n(O(k)) algorithm for the SCSS problem, where n is the number of vertices in the graph and k is the number of terminals. We explore how much easier the problem becomes on planar directed graphs. Our main algorithmic result is a 2(O(k)).n(O(root k)) algorithm for planar SCSS, which is an improvement of a factor of O(root k) in the exponent over the algorithm of Feldman and Ruhl. Our main hardness result is a matching lower bound for our algorithm: we show that planar SCSS does not have an f(k).n(o(root k)) algorithm for any computable function f, unless the exponential time hypothesis (ETH) fails. To obtain our algorithm, we first show combinatorially that there is a minimal solution whose treewidth is O(root k), and then use the dynamic-programming based algorithm for finding bounded-treewidth solutions due to Feldmann and Marx [The Complexity Landscape of Fixed-Parameter Directed Steiner Network Problems, preprint, https://arxiv.org/abs/1707.06808] . To obtain the lower bound matching the algorithm, we need a delicate construction of gadgets arranged in a gridlike fashion to tightly control the number of terminals in the created instance. The following additional results put our upper and lower bounds in context: our 2(O(k)).n(O(root k)) algorithm for planar directed graphs can be generalized to graphs excluding a fixed minor. Additionally, we can obtain this running time for the problem of finding an optimal planar solution even if the input graph is not planar. In general graphs, we cannot hope for such a dramatic improvement over the n(O(k)) algorithm of Feldman and Ruhl: assuming ETH, SCSS in general graphs does not have an f(k).n(o(k/log k)) algorithm for any computable function f. Feldman and Ruhl generalized their n(O(k)) algorithm to the more general DIRECTED STEINER NETWORK (DSN) problem; here the task is to find a subgraph of minimum weight such that for every source s(i) there is a path to the corresponding terminal t(i). We show that, assuming ETH, there is no f(k).n(o(k)) time algorithm for DSN on acyclic planar graphs. All our lower bounds hold for the integer weighted edge version, while the algorithm works for the more general unweighted vertex version.
机译:给定顶点加权的指示图G =(v,e)和k端子的设置t = {t(1),t(2),...,t(k)}强直连接的Steiner子图(SCSS)问题是找到最小重量的V顶点集H子集,使得G [H]在每个I I不等于J的(i) - > t(j)路径上。 prob-lem是np-hard,但Feldman和Ruhl [Siam J. Comput。,36(2006),PP。543-561]给出了SCSS问题的新型N(o(k))算法,其中n是图中的顶点和k的顶点是终端的数量。我们探讨了Paralar定向图中的更容易。我们的主要算法结果是2(o(k))。N( 。我们的主要硬度结果是我们算法的匹配下限:除非指数时间假设(ETH)失败。为了获得我们的算法,我们首先在组合地上显示一个最小的解决方案,它的树布线是O(根k),然后使用基于动态编程的算法来查找Feldmann和Marx [Marx的界限 - 树Width解决方案[固定的复杂性景观-Parameter指示施泰纳网络问题,预印,https://arxiv.org/abs/1707.06808]。为了获得算法的下限匹配,我们需要精致地构造以网格状方式排列的小工具,以便在所创建的实例中紧密地控制终端的数量。以下额外的结果将我们的上限和下限放入上下文:我们的2(o(k))。n(o(根k))平面定向图的算法可以广泛地概括为不包括固定的未成计的图表。此外,即使输入图不平面,我们也可以获得该运行时间以解决最佳平面解决方案的问题。在一般图中,我们无法希望Feldman和Ruhl的n(o(k))算法的这种戏剧性改进:假设eth,一般图中的SCS没有f(k).n(o(k / log k))任何可计算函数f的算法。 Feldman和Ruhl将它们的n(o(k))算法概括为更一般的指示静脉网络(DSN)问题;这里,任务是找到最小重量的子图,使得对于每个源S s(i),存在对应终端T(i)的路径。我们表明,假设eth,没有f(k).n(o(k))在非循环平面图上的DSN时间算法。我们所有的下限都适用于整数加权边缘版本,而算法适用于更普通的未加权顶点版本。

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