English
Related papers

Related papers: Isometric Rigidity of compact Wasserstein spaces

200 papers

An action of a compact quantum group on a compact metric space $(X,d)$ is (D)-isometric if the distance function is preserved by a diagonal action on $X\times X$. We show that an isometric action in this sense has the following additional…

Operator Algebras · Mathematics 2015-05-20 Alexandru Chirvasitu

Using tools from the theory of optimal transport, we establish several results concerning isometric actions of amenable topological groups with potentially unbounded orbits. Specifically, suppose $d$ is a compatible left-invariant metric on…

Functional Analysis · Mathematics 2025-09-16 Christian Rosendal

We consider rigidity properties of compact symmetric spaces $X$ with metric $g_0$ of rank one. Suppose $g$ is another Riemannian metric on $X$ with sectional curvature $\kappa$ bounded by $0 \leq \kappa \leq 1$. If $g$ equals $g_0$ outside…

Differential Geometry · Mathematics 2024-06-04 Chris Connell , Mitul Islam , Thang Nguyen , Ralf Spatzier

Borel probability measures living on metric spaces are fundamental mathematical objects. There are several meaningful distance functions that make the collection of the probability measures living on a certain space a metric space. We are…

Functional Analysis · Mathematics 2018-06-14 Dániel Virosztek

Let $M$ be a smooth, compact, connected, oriented Riemannian manifold, and let $\imath: M \to \mathbb R^d$ be an isometric embedding. We show that a Sobolev map $f: M \to M$ which has the property that the differential $df(q)$ is close to…

Analysis of PDEs · Mathematics 2024-02-12 Sergio Conti , Georg Dolzmann , Stefan Müller

For $p\in (1,\infty)$ let $\mathscr{P}_p(\mathbb{R}^3)$ denote the metric space of all $p$-integrable Borel probability measures on $\mathbb{R}^3$, equipped with the Wasserstein $p$ metric $\mathsf{W}_p$. We prove that for every…

Metric Geometry · Mathematics 2015-09-30 Alexandr Andoni , Assaf Naor , Ofer Neiman

In this essay, we discuss the notion of optimal transport on geodesic measure spaces and the associated (2-)Wasserstein distance. We then examine displacement convexity of the entropy functional on the space of probability measures. In…

Metric Geometry · Mathematics 2012-04-17 Otis Chodosh

The Wasserstein distance $\mathcal{W}_p$ is an important instance of an optimal transport cost. Its numerous mathematical properties as well as applications to various fields such as mathematical finance and statistics have been well…

Probability · Mathematics 2025-07-09 Jose Blanchet , Martin Larsson , Jonghwa Park , Johannes Wiesel

We study the reach (in the sense of Federer) of the natural isometric embedding $X\hookrightarrow W_p(X)$ of $X$ inside its $p$-Wasserstein space, where $(X,\operatorname{dist})$ is a geodesic metric space. We prove that if a point $x\in X$…

Metric Geometry · Mathematics 2023-07-04 Javier Casado , Manuel Cuerno , Jaime Santos-Rodríguez

In this paper we investigate properties of metric projections onto specific closed and geodesically convex proper subsets of Wasserstein spaces $(\mathcal{P}_p(\mathbf{R}^d),W_p).$ When $d=1$, as $(\mathcal{P}_2(\mathbf{R}),W_2)$ is…

Functional Analysis · Mathematics 2025-09-03 Anshul Adve , Alpár Mészáros

This paper connects nonpositive sectional curvature of a Riemannian manifold with the displacement convexity of the variance functional on the space $P(M)$ of probability measures over $M$. We show that $M$ has nonpositive sectional…

Differential Geometry · Mathematics 2015-03-24 Young-Heon Kim , Brendan Pass

Given a family of probability measures in P(X), the space of probability measures on a Hilbert space X, our goal in this paper is to highlight one ore more curves in P(X) that summarize efficiently that family. We propose to study this…

Machine Learning · Statistics 2015-11-24 Vivien Seguy , Marco Cuturi

A metric thickening of a given metric space $X$ is any metric space admitting an isometric embedding of $X$. Thickenings have found use in applications of topology to data analysis, where one may approximate the shape of a dataset via the…

Metric Geometry · Mathematics 2024-03-27 Henry Adams , Facundo Mémoli , Michael Moy , Qingsong Wang

We prove isometric rigidity for $p$-Wasserstein spaces over finite-dimensional tori and spheres for all $p$. We present a unified approach to proving rigidity that relies on the robust method of recovering measures from their Wasserstein…

Metric Geometry · Mathematics 2024-08-19 György Pál Gehér , Tamás Titkos , Dániel Virosztek

We study rays and co-rays in the Wasserstein space $P_p(\mathcal{X})$ ($p > 1$) whose ambient space $\mathcal{X}$ is a complete, separable, non-compact, locally compact length space. We show that rays in the Wasserstein space can be…

Dynamical Systems · Mathematics 2019-10-15 Guomin Zhu , Wen-Long Li , Xiaojun Cui

We prove general upper estimates for the distance between two Borel probability measures in Wasserstein metric in terms of the Fourier transforms of the measures. We work in compact manifolds including the torus, the Euclidean unit sphere,…

Classical Analysis and ODEs · Mathematics 2025-10-27 Bence Borda , Jean-Claude Cuenin

Motivated by persistent homology and topological data analysis, we consider formal sums on a metric space with a distinguished subset. These formal sums, which we call persistence diagrams, have a canonical 1-parameter family of metrics…

Algebraic Topology · Mathematics 2025-02-19 Peter Bubenik , Iryna Hartsock

In this paper, we prove that a metric measure space which has at least one open set isometric to an interval, and for which the (possibly non-unique) optimal transport map exists from any absolutely continuous measure to an arbitrary…

Metric Geometry · Mathematics 2020-04-27 Timo Schultz

We give necessary and sufficient conditions that show that both the group of isometries and the group of measure-preserving isometries are Lie groups for a large class of metric measure spaces. In addition we study, among other examples,…

Differential Geometry · Mathematics 2017-04-18 Gerardo Sosa

This work studies an explicit embedding of the set of probability measures into a Hilbert space, defined using optimal transport maps from a reference probability density. This embedding linearizes to some extent the 2-Wasserstein space,…

Machine Learning · Statistics 2022-05-05 Quentin Mérigot , Alex Delalande , Frédéric Chazal