Issue |
ESAIM: PS
Volume 24, 2020
|
|
---|---|---|
Page(s) | 703 - 717 | |
DOI | https://doi.org/10.1051/ps/2020013 | |
Published online | 16 November 2020 |
Squared quadratic Wasserstein distance: optimal couplings and Lions differentiability*
1
CERMICS, Ecole des Ponts,
Marne-la-Vallée, France.
2
MathRisk,
Inria,
Paris, France.
** Corresponding author: aurelien.alfonsi@enpc.fr
Received:
23
December
2019
Accepted:
4
March
2020
In this paper, we remark that any optimal coupling for the quadratic Wasserstein distance W22(μ,ν) between two probability measures μ and ν with finite second order moments on ℝd is the composition of a martingale coupling with an optimal transport map 𝛵. We check the existence of an optimal coupling in which this map gives the unique optimal coupling between μ and 𝛵#μ. Next, we give a direct proof that σ ↦ W22(σ,ν) is differentiable at μ in the Lions (Cours au Collège de France. 2008) sense iff there is a unique optimal coupling between μ and ν and this coupling is given by a map. It was known combining results by Ambrosio, Gigli and Savaré (Lectures in Mathematics ETH Zürich. Birkhäuser Verlag, Basel, 2005) and Ambrosio and Gangbo (Comm. Pure Appl. Math., 61:18–53, 2008) that, under the latter condition, geometric differentiability holds. Moreover, the two notions of differentiability are equivalent according to the recent paper of Gangbo and Tudorascu (J. Math. Pures Appl. 125:119–174, 2019). Besides, we give a self-contained probabilistic proof that mere Fréchet differentiability of a law invariant function F on L2(Ω, ℙ; ℝd) is enough for the Fréchet differential at X to be a measurable function of X.
Mathematics Subject Classification: 90C08 / 60G42 / 60E15 / 58B10 / 49J50
Key words: Optimal transport / Wasserstein distance / differentiability / couplings of probability measures / convex order
© The authors. Published by EDP Sciences, SMAI 2020
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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