Year: 2024
Type: article
Abstract: 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.
Authors: Facundo Mémoli, Tom Needham
Institutions: The Ohio State University, Florida State University
Language: English
Subfield: Applied Mathematics
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Funder: National Science Foundation
Awards: Collaborative Research: Applications of Symplectic Geometry to Frame Theory and Signal Processing, RI: Medium:Collaborative Research: Through synapses to spatial learning: a topological approach, AF: Small: Analyzing Complex Data with a Topological Lens, Collaborative Research: The Topology of Functional Data on Random Metric Spaces, Graphs, and Graphons, eMB: New Approaches for Interpreting Neural Responses to Behaviorally-Relevant Sensory Stimuli, TRIPODS: Topology, Geometry, and Data Analysis (TGDA@OSU):Discovering Structure, Shape, and Dynamics in Data
OpenAlex ID: W6903079587
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