Issue |
ESAIM: COCV
Volume 30, 2024
|
|
---|---|---|
Article Number | 78 | |
Number of page(s) | 21 | |
DOI | https://doi.org/10.1051/cocv/2024063 | |
Published online | 07 October 2024 |
Comparison results for Gromov–Wasserstein and Gromov–Monge distances
1
Department of Mathematics, The Ohio State University, Columbus, OH, USA
2
Department of Mathematics, Florida State University, Tallahassee, FL, USA
* Corresponding author: tneedham@fsu.edu
Received:
11
July
2023
Accepted:
12
August
2024
Inspired by the Kantorovich formulation of optimal transport distance between probability measures on a metric space, Gromov–Wasserstein (GW) distances comprise a family of metrics on the space of isomorphism classes of metric measure spaces. In previous work, the authors introduced a variant of this construction which was inspired by the original Monge formulation of optimal transport; elements of the resulting family are referred to Gromov–Monge (GM) distances. These GM distances, and related ideas, have since become a subject of interest from both theoretical and applications-oriented perspectives. In this note, we establish several theoretical properties of GM distances, focusing on comparisons between GM and GW distances. In particular, we show that GM and GW distances are equal for non-atomic metric measure spaces. We also consider variants of GM distance, such as a Monge version of Sturm’s Lp-transportion distance, and give precise comparisons to GW distance. Finally, we establish bi-Hölder equivalence between GM distance and an isometry-invariant Monge optimal transport distance between Euclidean metric measure spaces that has been utilized in shape and image analysis applications.
Mathematics Subject Classification: 51F99 / 49Q22
Key words: Gromov-Wasserstein distance / metric measure spaces / Monge maps
© The authors. Published by EDP Sciences, SMAI 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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