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Constant Approximation Algorithms for Embedding Graph Metrics into Trees and Outerplanar Graphs

机译:图度量标准嵌入树和外平面图的常数逼近算法

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We present a simple factor 6 algorithm for approximating the optimal multiplicative distortion of embedding (unweighted) graph metrics into tree metrics (thus improving and simplifying the factor 100 and 27 algorithms of Badoiu et al. (2007) and Badoiu et al. (2008)). We also present a constant factor algorithm for approximating the optimal distortion of embedding graph metrics into outerplanar metrics. For this, we introduce a notion of metric relaxed minor and show that if G contains an α-metric relaxed H-minor, then the distortion of any embedding of G into any metric induced by a H-minor free graph is > α. Then, for H = K_(2,3), we present an algorithm which either finds an α-relaxed minor, or produces an O(α)-embedding into an outerplanar metric.
机译:我们提出了一种简单的因子6算法,用于将(未加权的)图形度量嵌入到树度量中来近似最佳乘积失真(从而改进和简化了Badoiu等人(2007)和Badoiu等人(2008)的100和27因子算法)。我们还提出了一种常数因子算法,用于近似将图形度量嵌入到外部平面度量中的最佳失真。为此,我们引入了度量宽松次要的概念,并表明如果G包含一个α度量宽松H-minor,则G嵌入到一个由H-minor自由图引起的任何度量中的失真>α。然后,对于H = K_(2,3),我们提出了一种算法,该算法可以找到松弛了α的未成年人,或者将O(α)嵌入到外平面度量中。

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