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Configurations with few crossings in topological graphs

机译:拓扑图中几乎没有交叉的配置

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摘要

In this paper we study the problem of computing subgraphs of a certain configuration in a given topological graph G such that the number of crossings in the subgraph is minimum. The configurations that we consider are spanning trees, s–t paths, cycles, matchings, and κ-factors for κ∈{1,2}. We show that it is NP-hard to approximate the minimum number of crossings for these configurations within a factor of k1-ε for any ε>0, where k is the number of crossings in G. We then give a simple fixed-parameter algorithm that tests in O(2k) time whether G has a crossing-free configuration for any of the above, where the O-notation neglects polynomial terms. For some configurations we have faster algorithms. The respective running times are O(1.9999992k) for spanning trees and O((3)k) for s-t paths and cycles. For spanning trees we also have an O(1.968k)-time Monte-Carlo algorithm. Each O(βk)-time decision algorithm can be turned into an O((β+1)k)-time optimization algorithm that computes a configuration with the minimum number of crossings.
机译:在本文中,我们研究了在给定的拓扑图G中计算特定配置的子图的问题,使得子图中的相交数最小。我们考虑的配置是κ∈{1,2}的生成树,s–t路径,循环,匹配和κ因子。我们证明,对于任何ε> 0,在k1-ε的因子内近似于这些配置的最小交叉数是NP-困难的,其中k是G中的交叉数。然后,我们给出了一个简单的固定参数算法该函数以O(2k)时间测试G是否具有上述任意一项的无交叉配置,其中O表示忽略多项式项。对于某些配置,我们有更快的算法。生成树的运行时间分别为O(1.9999992k),s-t路径和循环的运行时间分别为O((3)k)。对于生成树,我们还具有O(1.968k)时间的蒙特卡洛算法。每个O(βk)时间决策算法都可以转换为O((β+ 1)k)时间优化算法,该算法计算具有最小交叉次数的配置。

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