2022 - T3 - WS3 - Measure-theoretic Approaches and Optimal Transportation in Statistics

Collection 2022 - T3 - WS3 - Measure-theoretic Approaches and Optimal Transportation in Statistics

Organizer(s) Aamari, Eddie ; Aaron, Catherine ; Chazal, Frédéric ; Fisher, Aurélie ; Hoffmann, Marc ; Le Brigant, Alice ; Levrard, Clément ; Michel, Bertrand

Date(s) 21/11/2022 - 25/11/2022

linked URL https://indico.math.cnrs.fr/event/7547/

4 14

On the existence of Monge maps for Gromov-Wasserstein problems

By Théo Lacombe

The Gromov-Wasserstein (GW) problem provides a way to compare probability measures supported on (possibly) different spaces. It relies on a quadratic minimization problem over the transportation polytope using a cost function over each space. As for the optimal transportation (OT) problem, it is natural to study the structure of minimizers of GW, in particular to wonder whenever they are deterministic (i.e. induced by a map between the spaces). In this talk, we will characterize GW minimizers for two cost functions introduced in the literature, and prove that some of them are actual maps following some specific structure. In addition, we will provide numerical evidence for situations where the map structure does not hold, suggesting the sharpness of our assumptions.

Information about the video

Date of recording 22/11/2022
Date of publication 19/04/2024
Institution IHP
Language English
Audience Researchers, Graduate Students
Director(s) Alexandre Duplessis, Marco Perez
Format MP4
Venue Amphitheater Hermite, IHP

Citation data

DOI 10.57987/IHP.2022.T3.WS3.004
Cite this video Lacombe, Théo (22/11/2022). On the existence of Monge maps for Gromov-Wasserstein problems. IHP. Audiovisual resource. DOI: 10.57987/IHP.2022.T3.WS3.004
URL https://dx.doi.org/10.57987/IHP.2022.T3.WS3.004

Domain(s)

Optimization and Control

Bibliography

T. Dumont, T. Lacombe, F-X. Vialard / On The Existence Of Monge Maps For The Gromov-wasserstein Distance. arXiv preprint arXiv:2210.11945

MSC codes

90C26 Nonconvex programming, global optimization

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