Improved algorithms for optimal embeddings

In the last decade, the notion of metric embeddings with small distortion has received wide attention in the literature, with applications in combinatorial optimization, discrete mathematics, and bio-informatics. The notion of embedding is, given two metric spaces on the same number of points, to find a bijection that minimizes maximum Lipschitz and bi-Lipschitz constants. One reason for the popularity of the notion is that algorithms designed for one metric space can be applied to a different one, given an embedding with small distortion. The better distortion, the better the effectiveness of the original algorithm applied to a new metric space. The goal recently studied by Kenyon et al. [2004] is to consider all possible embeddings between two finite metric spaces and to find the best possible one; that is, consider a single objective function over the space of all possible embeddings that minimizes the distortion. In this article we continue this important direction. In particular, using a theorem of Albert and Atkinson [2005], we are able to provide an algorithm to find the optimal bijection between two line metrics, provided that the optimal distortion is smaller than 13.602. This improves the previous bound of 3 + 2&sqrt;2, solving an open question posed by Kenyon et al. [2004]. Further, we show an inherent limitation of algorithms using the “forbidden pattern” based dynamic programming approach, in that they cannot find optimal mapping if the optimal distortion is more than 7 + 4&sqrt;3 (≃ 13.928). Thus, our results are almost optimal for this method. We also show that previous techniques for general embeddings apply to a (slightly) more general class of metrics.