The subject of graph embeddings deals with embedding a finite point set in a given metric space by points in another target metric space in such a way that distances in the new space are at least as much as, but not too much more than, distances in the old space. The largest new distance to old distance ratio over all pairs of points is called the distortion of the embedding. In this paper, we will study the distortion ${\rm dist}(G,H)$ while embedding metrics supported on a given graph $G$ into metrics supported on a graph $H$ of lower characteristic, where the characteristic $\chi(H)$ of a graph $H$ is the quantity $E-V+1$ ($E$ is the number of edges and $V$ is the number of vertices in $H$). We will prove the following lower bounds for s...
Abstract. This paper addresses the basic question of how well a tree can approximate distances of a ...
We consider the problem of embedding finite metrics with slack: we seek to produce embeddings with s...
In the last decade, the notion of metric embeddings with small distortion received wide attention in...
The subject of graph embeddings deals with embedding a finite point set in a given metric space by p...
3 Further, we will also give an alternative proof for lower bounding the distortion when probabilist...
The METRIC EMBEDDING problem takes as input two metric spaces (X,DX) and (Y,DY), and a positive inte...
The METRIC EMBEDDING problem takes as input two metric spaces (X,DX) and (Y,DY), and a positive inte...
The METRIC EMBEDDING problem takes as input two metric spaces (X,DX) and (Y,DY), and a positive inte...
\u3cp\u3eThe METRIC EMBEDDING problem takes as input two metric spaces (X,DX) and (Y,DY), and a posi...
Motivated by many recent algorithmic applications, this paper aims to promote a systematic study of ...
AbstractEmbeddings of finite metric spaces into Euclidean space have been studied in several context...
We design an algorithm to embed graph metrics into `p with dimension and distortion both dependent o...
Motivated by many recent algorithmic applications, this paper aims to promote a systematic study of ...
We initiate the study of metric embedding problems from an approximation point of view. Metric embed...
We consider the problem of embedding finite metrics with "slack": we seek to produce embeddings wit...
Abstract. This paper addresses the basic question of how well a tree can approximate distances of a ...
We consider the problem of embedding finite metrics with slack: we seek to produce embeddings with s...
In the last decade, the notion of metric embeddings with small distortion received wide attention in...
The subject of graph embeddings deals with embedding a finite point set in a given metric space by p...
3 Further, we will also give an alternative proof for lower bounding the distortion when probabilist...
The METRIC EMBEDDING problem takes as input two metric spaces (X,DX) and (Y,DY), and a positive inte...
The METRIC EMBEDDING problem takes as input two metric spaces (X,DX) and (Y,DY), and a positive inte...
The METRIC EMBEDDING problem takes as input two metric spaces (X,DX) and (Y,DY), and a positive inte...
\u3cp\u3eThe METRIC EMBEDDING problem takes as input two metric spaces (X,DX) and (Y,DY), and a posi...
Motivated by many recent algorithmic applications, this paper aims to promote a systematic study of ...
AbstractEmbeddings of finite metric spaces into Euclidean space have been studied in several context...
We design an algorithm to embed graph metrics into `p with dimension and distortion both dependent o...
Motivated by many recent algorithmic applications, this paper aims to promote a systematic study of ...
We initiate the study of metric embedding problems from an approximation point of view. Metric embed...
We consider the problem of embedding finite metrics with "slack": we seek to produce embeddings wit...
Abstract. This paper addresses the basic question of how well a tree can approximate distances of a ...
We consider the problem of embedding finite metrics with slack: we seek to produce embeddings with s...
In the last decade, the notion of metric embeddings with small distortion received wide attention in...