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FPT Algorithms for Embedding into Low Complexity Graphic Metrics

机译:嵌入低复杂度图形指标的FPT算法

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The Metric Embedding problem takes as input two metric spaces (X,D_X) and (Y,D_Y), and a positive integer d. The objective is to determine whether there is an embedding F:X - Y such that the distortion d_{F} = d. Such an embedding is called a distortion d embedding. In parameterized complexity, the Metric Embedding problem is known to be W-hard and therefore, not expected to have an FPT algorithm. In this paper, we consider the Gen-Graph Metric Embedding problem, where the two metric spaces are graph metrics. We explore the extent of tractability of the problem in the parameterized complexity setting. We determine whether an unweighted graph metric (G,D_G) can be embedded, or bijectively embedded, into another unweighted graph metric (H,D_H), where the graph H has low structural complexity. For example, H is a cycle, or H has bounded treewidth or bounded connected treewidth. The parameters for the algorithms are chosen from the upper bound d on distortion, bound Delta on the maximum degree of H, treewidth alpha of H, and the connected treewidth alpha_{c} of H.Our general approach to these problems can be summarized as trying to understand the behavior of the shortest paths in G under a low distortion embedding into H, and the structural relation the mapping of these paths has to shortest paths in H.
机译:度量嵌入问题将两个度量空间(X,D_X)和(Y,D_Y)以及一个正整数d作为输入。目的是确定是否存在嵌入F:X-> Y,以使失真d_ {F} <= d。这样的嵌入称为失真d嵌入。在参数化的复杂度中,公制嵌入问题是已知的W-难问题,因此不期望具有FPT算法。在本文中,我们考虑Gen-Graph度量嵌入问题,其中两个度量空间是图度量。我们探索了在参数化复杂性设置中问题的易处理性的程度。我们确定是否可以将非加权图度量(G,D_G)嵌入或双射地嵌入到另一个非加权图度量(H,D_H)中,其中图H具有较低的结构复杂性。例如,H是一个循环,或者H具有有界树宽或有界连接树宽。这些算法的参数是从失真的上限d,最大H的边界Delta,H的树宽alpha和H的连接树宽alpha_ {c}中选择的。我们针对这些问题的一般方法可总结为:试图了解嵌入到H中的低失真下G中最短路径的行为,以及这些路径映射到H中最短路径的结构关系。

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