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For each given $p\in[1,\infty]$ we investigate certain sub-family $\mathcal{M}_p$ of the collection of all compact metric spaces $\mathcal{M}$ which are characterized by the satisfaction of a strengthened form of the triangle inequality…

Metric Geometry · Mathematics 2021-11-24 Facundo Mémoli , Zhengchao Wan

It is shown that for any two compact metric spaces there exists an "optimal" correspondence which the Gromov-Hausdorff distance is attained at. Each such correspondence generates isometric embeddings of these spaces into a compact metric…

Metric Geometry · Mathematics 2016-03-30 Alexander Ivanov , Stavros Iliadis , Alexey Tuzhilin

The Gromov--Hausdorff distance (hereinafter referred to as the GH-distance) is a measure of non-isometricity of metric spaces. In this paper, we study a modification of this distance that also takes topological differences into account. The…

Metric Geometry · Mathematics 2025-12-03 Semeon A. Bogaty , Alexey A. Tuzhilin

The Gromov-Wasserstein distances were proposed a few years ago to compare distributions which do not lie in the same space. In particular, they offer an interesting alternative to the Wasserstein distances for comparing probability measures…

Probability · Mathematics 2021-04-19 Antoine Salmona , Julie Delon , Agnès Desolneux

Let $G$ be a finite, connected metric graph and let $X\subseteq G$ be a subset. If $X$ is sufficiently dense in $G$, we show that the Gromov--Hausdorff distance matches the Hausdorff distance, namely $d_\gh(G,X)=d_\h(G,X)$. When the metric…

Metric Geometry · Mathematics 2025-12-24 Henry Adams , Sushovan Majhi , Fedor Manin , Žiga Virk , Nicolò Zava

This paper studies $l^p$-products of metric spaces and provides estimates for the Gromov-Hausdorff distances between them. The case of linear products is considered separately, and sufficient conditions for attainability of the estimates…

Metric Geometry · Mathematics 2026-03-03 Emin Abdullaev

We define a distance analogous to the Gromov-Hausdorff distance that enables the comparison of arbitrary quasi-isometric spaces. We also investigate properties preserved under limits with respect to this distance, as well as properties of…

Metric Geometry · Mathematics 2026-05-28 Alexei Naianzin

In this paper, we investigate compact ultrametric measure spaces which form a subset $\mathcal{U}^w$ of the collection of all metric measure spaces $\mathcal{M}^w$. Similar as for the ultrametric Gromov-Hausdorff distance on the collection…

Metric Geometry · Mathematics 2021-07-05 Facundo Mémoli , Axel Munk , Zhengchao Wan , Christoph Weitkamp

We introduce a hypertopology, induced by an inframetric up to full quantum isometry, on the class of pointed proper quantum metric spaces, which are separable, possibly non-unital, C*-algebras endowed with an analogue of the Lipschitz…

Operator Algebras · Mathematics 2025-12-04 Frederic Latremoliere

We provide general upper and lower bounds for the Gromov-Hausdorff distance $d_{\mathrm{GH}}(\mathbb{S}^m,\mathbb{S}^n)$ between spheres $\mathbb{S}^m$ and $\mathbb{S}^n$ (endowed with the round metric) for $0\leq m< n\leq \infty$. Some of…

Metric Geometry · Mathematics 2023-12-13 Sunhyuk Lim , Facundo Mémoli , Zane Smith

Gromov-Wasserstein (GW) distances are combinations of Gromov-Hausdorff and Wasserstein distances that allow the comparison of two different metric measure spaces (mm-spaces). Due to their invariance under measure- and distance-preserving…

Optimization and Control · Mathematics 2023-07-14 Florian Beier , Robert Beinert , Gabriele Steidl

A fundamental challenge in data science is to match disparate point sets with each other. While optimal transport efficiently minimizes point displacements under a bijectivity constraint, it is inherently sensitive to rotations. Conversely,…

Computational Geometry · Computer Science 2026-04-17 Guillaume Houry , Jean Feydy , François-Xavier Vialard

For each arbitrary finite group $G$, we consider a suitable notion of Gromov Hausdorff distance between compact $G$ metric spaces and derive lower bounds based on equivariant topology methods. As applications, we prove equivariant rigidity…

Metric Geometry · Mathematics 2026-01-29 Sunhyuk Lim , Facundo Memoli

The Gromov-Wasserstein (GW) distance is frequently used in machine learning to compare distributions across distinct metric spaces. Despite its utility, it remains computationally intensive, especially for large-scale problems. Recently, a…

Machine Learning · Statistics 2024-10-01 Antoine Salmona , Julie Delon , Agnès Desolneux

In this paper, we study metric trees, without any finiteness restrictions. For subsets of such trees, a condition that guarantees that the Hausdorff and Gromov--Hausdorff distances from the subset to the entire metric tree are the same is…

Metric Geometry · Mathematics 2024-12-30 A. O. Ivanov , I. N. Mikhailov , A. A. Tuzhilin

The Gromov-Wasserstein (GW) distance quantifies discrepancy between metric measure spaces and provides a natural framework for aligning heterogeneous datasets. Alas, as exact computation of GW alignment is NP hard, entropic regularization…

Optimization and Control · Mathematics 2024-01-11 Gabriel Rioux , Ziv Goldfeld , Kengo Kato

It is shown that any bounded metric space can be isometrically embedded into the Gromov--Hausdorff metric class GH. This result is a consequence of local geometry description of the class GH in a sufficiently small neighborhood of a generic…

Metric Geometry · Mathematics 2022-03-08 Alexander O. Ivanov , Alexey A. Tuzhilin

We study equivariant Gromov-Hausdorff distances for general continuous actions which are not necessarily isometric as Fukaya introduced. We prove that if an action is expansive and has pseudo-orbit tracing property then it is stable under…

Dynamical Systems · Mathematics 2020-05-12 Nhan-Phu Chung

Gromov--Wasserstein (GW) distances compare graphs, shapes, and point clouds through internal distances, without requiring a common coordinate system. This invariance is powerful, but discrete GW is a nonconvex quadratic optimal transport…

Machine Learning · Computer Science 2026-05-15 Ao Xu , Tieru Wu

The Gromov-Wasserstein (GW) distance has gained increasing interest in the machine learning community in recent years, as it allows for the comparison of measures in different metric spaces. To overcome the limitations imposed by the equal…

Machine Learning · Computer Science 2025-03-28 Yikun Bai , Rocio Diaz Martin , Abihith Kothapalli , Hengrong Du , Xinran Liu , Soheil Kolouri