# Approximating Gromov-Hausdorff Distance

### Description

The Gromov-Hausdorff distance has been shown in memoli_theoretical_2005,memoli_use_nodate to provide a reasonable framework for comparing shapes. This metric was first introduced by M. Gromov in the context of Riemannian manifolds; see Gromov’s book gromov_metric_2007. However, it can also be defined between two abstract metric spaces $(X,d_X)$ and $(Y,d_Y)$ to measure how much they are metrically-close. Motivated by its potential use in topological data analysis, we address the problem of computing the Gromov-Hausdorff distance between two (finite) metric spaces.

Given two (compact) metric spaces $(X,d_X)$ and $(Y,d_Y)$, their Gromov-Hausdorff distance is formally defined (gromov_metric_2007) by

where the Hausdorff distance $d^Z_H$ of $Z$ is minimized over all isometries $f,g$ and metric spaces $(Z,d_Z)$.

Sometimes, an alternative definition, which is computationally more viable, is considered using the concept of correspondences. Given two point sets $X$ and $Y$, a correspondence $C$ between them is a subset of $X\times Y$ such that $\forall x\in X\exists y\in Y\ni(x,y)\in C$ and $\forall y\in Y\exists x\in X\ni(x,y)\in C$. Roughly speaking, a correspondence is a relation that is bi-surjective’’. Any surjective function $f:X\to Y$ naturally induces a correspondence

For a given correspondence $C$, its distortion is defined to be

Now, the Gromov-Hausdorff distance is characterized by

see burago_course_2001 for a proof. We consider the problem of efficiently computing the Gromov-Hausdorff distance between finite subsets $X$ and $Y$ of $\mathbb{R}^d$ equipped with the standard Euclidean metric.

We note from the latter definition of the Gromov-Hausdorff distance that it takes at least $2^k$ operations to naively compute it if $X,Y$ have at most $k$ points. In such a case in general, we seek an approximation algorithm that requires only polynomial many operations, but at the expense of computing an approximate answer that is within a certain approximation factor.

The authors of agarwal_computing_2015 show the NP-hardness of approximation of $d_{GH}$ when $X,Y$ are metric trees. However, computing the Gromov-Hausdorff distance between Euclidean subsets is still elusive.

In hutchison_approximating_2005, the authors consider (finite) subsets $X,Y$ of $\mathbb{R}^d$ of same cardinality and the class of only bijective correspondences between them. For $d=1$, they present an approximation algorithm to find the least distortion bijection with an approximation factor of $2$. They then pose two open problems: Can an approximation factor better than $2$ be achieved and is it NP-hard to compute the least distortion bijective correspondence between Euclidean subsets?

In our effort to approximate $d_{GH}$, we look into its relationship with $d_{H,iso}$, the minimum Hausdorff distance under the class of Euclidean isometries. For $d=1$ and compact subsets $X,Y$, we show in majhi2019approximating that

We also show that the upper bound is tight. This gives rise to an $O(k\log{k})$-time algorithm for approximating $d_{GH}(X,Y)$ with an approximation factor of $\left(1+\frac{1}{4}\right)$; see majhi2019approximating. Our method can also be adapted to have the same approximation factor for finding the lowest distortion bijective correspondences, hence solving the open problem addressed in hutchison_approximating_2005.

We now investigate the following two directions:

1) For $d=1$, we conjecture that it is NP-hard to approximate the Gromov-Hausdorff distance with a factor better than $\left(1+\frac{1}{4}\right)$.

2) Unlike $d=1$, the $d_{H,iso}$ fails to provide an approximation algorithm with a constant approximation factor when $d>1$. In higher dimension, are there any other ways to approximate the Gromov-Hausdorff distance?

### People

• Carola Wenk, Tulane University
• Jeffrey Vitter, University of Mississippi

• ### Publications

1. majhi2019approximating

### References

2020 Sushovan Majhi