Finite Volume Spaces and Sparsification

TL;DR

This paper introduces finite d-volumes as a high-dimensional generalization of metric spaces, explores their properties, and investigates approximation and dimension reduction techniques, revealing both possibilities and limitations in high-dimensional sparsification.

Contribution

It develops the theory of finite d-volumes, demonstrates their approximation by -volumes, and extends graph sparsification techniques to high-dimensional settings.

Findings

Finite d-volumes generalize metric spaces to high dimensions.

-volumes can approximate any d-volume with O(n^d) distortion.

There exist 2-volumes that cannot be well-approximated by -volumes, showing limits of approximation.

Abstract

We introduce and study finite $d$-volumes - the high dimensional generalization of finite metric spaces. Having developed a suitable combinatorial machinery, we define $\ell_1$-volumes and show that they contain Euclidean volumes and hypertree volumes. We show that they can approximate any $d$-volume with $O(n^d)$ multiplicative distortion. On the other hand, contrary to Bourgain's theorem for $d=1$, there exists a $2$-volume that on $n$ vertices that cannot be approximated by any $\ell_1$-volume with distortion smaller than $\tilde{\Omega}(n^{1/5})$. We further address the problem of $\ell_1$-dimension reduction in the context of $\ell_1$ volumes, and show that this phenomenon does occur, although not to the same striking degree as it does for Euclidean metrics and volumes. In particular, we show that any $\ell_1$ metric on $n$ points can be $(1+ \epsilon)$-approximated by a sum of $O(n/\epsilon^2)$ cut metrics, improving over the best previously known bound of $O(n \log n)$ due to Schechtman. In order to deal with dimension reduction, we extend the techniques and ideas introduced by Karger and Bencz{\'u}r, and Spielman et al.~in the context of graph Sparsification, and develop general methods with a wide range of applications.