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Multi-marginal optimal transport (MOT) is a generalization of optimal transport to multiple marginals. Optimal transport has evolved into an important tool in many machine learning applications, and its multi-marginal extension opens up for…

Machine Learning · Computer Science 2021-12-07 Jiaojiao Fan , Isabel Haasler , Johan Karlsson , Yongxin Chen

In this paper, the Lipschitz clustering property of a metric space refers to the existence of Lipschitz retractions between its finite subset spaces. Obstructions to this property can be either topological or geometric features of the…

Metric Geometry · Mathematics 2022-12-20 Leonid V. Kovalev

Optimal Transport is a theory that allows to define geometrical notions of distance between probability distributions and to find correspondences, relationships, between sets of points. Many machine learning applications are derived from…

Machine Learning · Statistics 2020-11-10 Titouan Vayer

We study the information geometry of $\bcc$-divergences from families of costs of the form $\mathsf{c}(x, \barx) =\mathsf{u}(x^{\mathfrak{t}}\barx)$ through the optimal transport point of view. Here, $\mathsf{u}$ is a scalar function with…

Analysis of PDEs · Mathematics 2025-12-02 Du Nguyen

The Kolmogorov $n$-width is an established benchmark to judge the performance of reduced basis and similar methods that produce linear reduced spaces. Although immensely successful in the elliptic regime, this width, shows unsatisfactory…

Numerical Analysis · Mathematics 2023-10-24 D. Rim , G. Welper

This paper continues the investigation of `Wasserstein-like' transportation distances for probability measures on discrete sets. We prove that the discrete transportation metrics on the d-dimensional discrete torus with mesh size 1/N…

Metric Geometry · Mathematics 2013-02-15 Nicola Gigli , Jan Maas

Many causal and structural parameters in economics can be identified and estimated by computing the value of an optimization program over all distributions consistent with the model and the data. Existing tools apply when the data is…

Econometrics · Economics 2025-07-31 Andrei Voronin

Diamond graphs and binary trees are important examples in the theory of metric embeddings and also in the theory of metric characterizations of Banach spaces. Some results for these families of graphs are parallel to each other, for example…

Metric Geometry · Mathematics 2018-11-13 Siu Lam Leung , Sarah Nelson , Sofiya Ostrovska , Mikhail Ostrovskii

This paper considers the evolution dynamics of the free boundaries in terms of the change of $m$, the allowed amount of transported mass or the change of $\lambda$, the transportation cost cap, i.e. the allowed maximum cost for a unit mass…

Analysis of PDEs · Mathematics 2015-08-12 Gonzalo Davila , Young-Heon Kim

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

In this paper, we consider the Uniform Cost-Distance Steiner Tree Problem in metric spaces, a generalization of the well-known Steiner tree problem. Cost-distance Steiner trees minimize the sum of the total length and the weighted path…

Data Structures and Algorithms · Computer Science 2022-11-10 Stephan Held , Yannik Kyle Dustin Spitzley

We study the Optimal Transport problem for laws of random measures in the Kantorovich-Wasserstein space $\mathcal{P}_2(\mathcal{P}_2(\mathrm{H}))$, associated with a Hilbert space $\mathrm{H}$ (with finite or infinite dimension) and for the…

Functional Analysis · Mathematics 2025-09-03 Alessandro Pinzi , Giuseppe Savaré

In this paper we provide several \emph{metric universality} results. We exhibit for certain classes $\cC$ of metric spaces, families of metric spaces $(M_i, d_i)_{i\in I}$ which have the property that a metric space $(X,d_X)$ in $\cC$ is…

Metric Geometry · Mathematics 2020-04-15 Florent P. Baudier , Gilles Lancien , Pavlos Motakis , Thomas Schlumprecht

An easy consequence of Kantorovich-Rubinstein duality is the following: if $f:[0,1]^d \rightarrow \infty$ is Lipschitz and $\left\{x_1, \dots, x_N \right\} \subset [0,1]^d$, then $$ \left| \int_{[0,1]^d} f(x) dx - \frac{1}{N}…

Probability · Mathematics 2020-10-27 Stefan Steinerberger

New transportation cost inequalities are derived by means of elementary large deviation reasonings. Their dual characterization is proved; this provides an extension of a well-known result of S. Bobkov and F. G\"{o}tze. Their tensorization…

Probability · Mathematics 2007-05-23 Nathael Gozlan , Christian Léonard

We address the Monge problem in metric spaces with a geodesic distance: (X, d) is a Polish space and dL is a geodesic Borel distance which makes (X,dL) a non branching geodesic space. We show that under the assumption that geodesics are…

Probability · Mathematics 2015-03-19 Stefano Bianchini , Fabio Cavalletti

We prove that the spaces $\ell_p$, $1<p<\infty, p\ne 2$, and all infinite-dimensional subspaces of their quotient spaces do not admit equivalent almost transitive renormings. This is a step towards the solution of the Banach-Mazur rotation…

Functional Analysis · Mathematics 2015-01-28 S. J. Dilworth , B. Randrianantoanina

The celebrated Lott-Sturm-Villani theory of metric measure spaces furnishes synthetic notions of a Ricci curvature lower bound $K$ joint with an upper bound $N$ on the dimension. Their condition, called the Curvature-Dimension condition and…

Differential Geometry · Mathematics 2023-09-26 Afiny Akdemir , Fabio Cavalletti , Andrew Colinet , Robert McCann , Flavia Santarcangelo

We first give a characterization of the L^1-transportation cost-information inequality on a metric space and next find some appropriate sufficient condition to transportation cost-information inequalities for dependent sequences.…

Probability · Mathematics 2007-05-23 H. Djellout , A. Guillin , L. Wu

This paper studies the uniqueness of solutions to the dual optimal transport problem, both qualitatively and quantitatively (bounds on the diameter of the set of optimisers). On the qualitative side, we prove that when one marginal…

Optimization and Control · Mathematics 2026-04-03 William Ford