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Estimating Wasserstein distances between two high-dimensional densities suffers from the curse of dimensionality: one needs an exponential (wrt dimension) number of samples to ensure that the distance between two empirical measures is…

Machine Learning · Statistics 2020-07-13 François-Pierre Paty , Alexandre d'Aspremont , Marco Cuturi

If $u : \Omega\subset \mathbb{R}^d \to {\rm X}$ is a harmonic map valued in a metric space ${\rm X}$ and ${\sf E} : {\rm X} \to \mathbb{R}$ is a convex function, in the sense that it generates an ${\rm EVI}_0$-gradient flow, we prove that…

Metric Geometry · Mathematics 2021-07-21 Hugo Lavenant , Léonard Monsaingeon , Luca Tamanini , Dmitry Vorotnikov

We introduce a new non-linear optimal transport formulation for a pair of probability measures on $\mathbb{R}^d$ sharing a common barycentre, in which admissible transference plans satisfy two martingale-type constraints. This bi-martingale…

Probability · Mathematics 2025-11-03 Karol Bołbotowski

The fundamental theorem of classical optimal transport establishes strong duality and characterizes optimizers through a complementary slackness condition. Milestones such as Brenier's theorem and the Kantorovich-Rubinstein formula are…

Probability · Mathematics 2025-01-28 Mathias Beiglböck , Gudmund Pammer , Lorenz Riess , Stefan Schrott

We prove that if $\Omega\subset \mathbb{R}^{n+1}$ is a (not necessarily strictly) convex, $C^1$ domain, and $\mu$ and $\bar{\mu}$ are probability measures absolutely continuous with respect to surface measure on $\partial \Omega$, with…

Analysis of PDEs · Mathematics 2025-03-11 Seonghyeon Jeong , Jun Kitagawa

Optimal transport maps define a one-to-one correspondence between probability distributions, and as such have grown popular for machine learning applications. However, these maps are generally defined on empirical observations and cannot be…

Statistics Theory · Mathematics 2021-02-18 Lucas de Lara , Alberto González-Sanz , Jean-Michel Loubes

We study metric projections onto cones in the Wasserstein space of probability measures, defined by stochastic orders. Dualities for backward and forward projections are established under general conditions. Dual optimal solutions and their…

Probability · Mathematics 2021-10-12 Young-Heon Kim , Yuan Long Ruan

We generalize a well-known result of L. Caffarelli on Lipschitz estimates for optimal transportation $T$ between uniformly log-concave probability measures. Let $T : \R^d \to \R^d$ be an optimal transportation pushing forward $\mu =…

Functional Analysis · Mathematics 2010-01-12 Alexander V. Kolesnikov

We consider the optimal transport problem over convex costs arising from optimal control of linear time-invariant(LTI) systems when the initial and target measures are assumed to be supported on the set of equilibrium points of the LTI…

Optimization and Control · Mathematics 2023-12-19 Karthik Elamvazhuthi , Matt Jacobs

We develop an $\e$-regularity theory at the boundary for a general class of Monge-Amp\`ere type equations arising in optimal transportation. As a corollary we deduce that optimal transport maps between H\"older densities supported on $C^2$…

Analysis of PDEs · Mathematics 2014-12-19 Shibing Chen , Alessio Figalli

The stability of solutions to optimal transport problems under variation of the measures is fundamental from a mathematical viewpoint: it is closely related to the convergence of numerical approaches to solve optimal transport problems and…

Numerical Analysis · Mathematics 2022-07-25 Anatole Gallouët , Quentin Mérigot , Boris Thibert

Optimal transport provides a metric which quantifies the dissimilarity between probability measures. For measures supported in discrete metric spaces, finding the optimal transport distance has cubic time complexity in the size of the…

Machine Learning · Computer Science 2024-01-30 Samantha Chen , Puoya Tabaghi , Yusu Wang

We give a new probabilistic construction of solutions to real Monge-Amp\`ere equations in R^n satisfying the second boundary value problem with respect to a given target convex body P) which fits naturally into the theory of optimal…

Analysis of PDEs · Mathematics 2013-02-19 Robert J. Berman

We prove a nonsmooth implicit function theorem applicable to the zero set of the difference of convex functions. This theorem is explicit and global: it gives a formula representing this zero set as a difference of convex functions which…

Analysis of PDEs · Mathematics 2021-02-25 Jun Kitagawa , Robert McCann

Many numerical and learning algorithms rely on the solution of the Monge-Kantorovich problem and Wasserstein distances, which provide appropriate distributional metrics. While the natural approach is to treat the problem as an…

Optimization and Control · Mathematics 2025-12-11 Mohsen Sadr , Peyman Mohajerin Esfahani , Hossein Gorji

In the classical Monge-Kantorovich problem, the transportation cost only depends on the amount of mass sent from sources to destinations and not on the paths followed by this mass. Thus, it does not allow for congestion effects. Using the…

Optimization and Control · Mathematics 2007-05-23 G. Carlier , C. Jimenez , F. Santambrogio

We consider random i.i.d. samples of absolutely continuous measures on bounded connected domains. We prove an upper bound on the $\infty$-transportation distance between the measure and the empirical measure of the sample. The bound is…

Probability · Mathematics 2014-07-07 Nicolás García Trillos , Dejan Slepčev

Let (X,L) be a (semi-) polarized complex projective variety and T a real torus acting holomorphically on X with moment polytope P. Given a probability density g on P we introduce a new type of Monge-Ampere measure on X, defined for singular…

Differential Geometry · Mathematics 2014-02-03 Robert J. Berman , David Witt Nystrom

We introduce and analyze a statistical estimator for Monge transport maps: solutions to the quadratic optimal transport problem in Euclidean space. For absolutely continuous source measures, this map is uniquely defined as the gradient of a…

Optimization and Control · Mathematics 2026-04-27 Elsa Cazelles , Edouard Pauwels , Léo Portales

We consider dynamical transport metrics for probability measures on discretisations of a bounded convex domain in $\mathbb{R}^d$. These metrics are natural discrete counterparts to the Kantorovich metric $\mathbb{W}_2$, defined using a…

Analysis of PDEs · Mathematics 2020-01-30 Peter Gladbach , Eva Kopfer , Jan Maas
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