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Related papers: Partial Regularity for optimal transport maps

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We show that the knowledge of the Dirichlet-to-Neumann maps given on an arbitrary open non-empty portion of the boundary of a smooth domain in $\mathbb{R}^n$, $n\ge 2$, for classes of semilinear and quasilinear conductivity equations,…

Analysis of PDEs · Mathematics 2020-11-04 Yavar Kian , Katya Krupchyk , Gunther Uhlmann

We study a random matching problem on closed compact $2$-dimensional Riemannian manifolds (with respect to the squared Riemannian distance), with samples of random points whose common law is absolutely continuous with respect to the volume…

Probability · Mathematics 2026-05-01 Nicolas Clozeau , Francesco Mattesini

Let $X$ and $Y$ be domains of $\mathbb{R}^n$ equipped with respective probability measures $\mu$ and $ \nu$. We consider the problem of optimal transport from $\mu$ to $\nu$ with respect to a cost function $c: X \times Y \to \mathbb{R}$. To…

Optimization and Control · Mathematics 2020-05-27 Gabriel Khan , Jun Zhang

Recent findings for optimal transport maps between distribution functions sharing the same copula show that componentwise the solution is the optimal map between marginal distributions. This is an important discovery since in the…

Probability · Mathematics 2020-07-15 Novin Ghaffari , Stephen Walker

This paper develops a comprehensive theory of optimal transport for signed (real) measures on Rd. Extending the classical Brenier theorem, we consider Jordan decompositions of measures with possibly fractal singular parts. Under suitable…

We study the semi-discrete formulation of one-dimensional partial optimal transport with quadratic cost, where a probability density is partially transported to a finite sum of Dirac masses of smaller total mass. This problem arises…

Optimization and Control · Mathematics 2025-09-11 Adrien Cances , Hugo Leclerc

We study optimal transport plans from $m$ equally weighted points (with weights $1/m$) to $n$ equally weighted points (with weights $1/n$). The Birkhoff-von Neumann Theorem implies that if $m=n$, then the optimal transport plan can be…

Optimization and Control · Mathematics 2023-12-08 Alexander Bruce Johnson , Stefan Steinerberger

Consider two bounded domains $\Omega$ and $\Lambda$ in $\mathbb{R}^{2}$, and two sufficiently regular probability measures $\mu$ and $\nu$ supported on them. By Brenier's theorem, there exists a unique transportation map $T$ satisfying…

Analysis of PDEs · Mathematics 2015-07-29 Otis Chodosh , Vishesh Jain , Michael Lindsey , Lyuboslav Panchev , Yanir A. Rubinstein

We prove a pointwise $C^{2,\,\alpha}$ estimate for the potential of the optimal transport map in the case that the densities are only close to constant in a certain $L^p$ sense.

Analysis of PDEs · Mathematics 2025-05-02 Arghya Rakshit

We consider the multi-marginal optimal transport of aligning several compactly supported marginals on the Heisenberg group to minimize the total cost, which we take to be the sum of the squared Carnot-Carath\'eodory distances from the…

Optimization and Control · Mathematics 2020-06-22 Brendan Pass , Andrea Pinamonti , Mattia Vedovato

In this paper we extend recent developments in computational optimal transport to the setting of Riemannian manifolds. In particular, we show how to learn optimal transport maps from samples that relate probability distributions defined on…

Consider transportation of one distribution of mass onto another, chosen to optimize the total expected cost, where cost per unit mass transported from x to y is given by a smooth function c(x,y). If the source density f^+(x) is bounded…

Analysis of PDEs · Mathematics 2011-07-07 Alessio Figalli , Young-Heon Kim , Robert J. McCann

Two recent works, Avella-Medina and Gonz\'alez-Sanz (2026) and Passeggeri and Paindaveine (2026), studied the robustness of the optimal transport map through its breakdown point, i.e., the smallest fraction of contamination that can make…

Statistics Theory · Mathematics 2026-03-18 Alberto Gonzalez-Sanz , Marco Avella Medina

We introduce a constrained optimal transport problem where origins $x$ can only be transported to destinations $y\geq x$. Our statistical motivation is to describe the sharp upper bound for the variance of the treatment effect $Y-X$ given…

Optimization and Control · Mathematics 2021-06-22 Marcel Nutz , Ruodu Wang

In this note we prove that in a metric measure space $(X, d, m)$ verifying the measure contraction property with parameters $K \in \mathbb{R}$ and $1< N< \infty$, any optimal transference plan between two marginal measures is induced by an…

Metric Geometry · Mathematics 2020-04-22 Fabio Cavalletti , Andrea Mondino

Let $M,N$ be two smooth compact hypersurfaces of $\mathbb{R}^n$ which bound strictly convex domains equipped with two absolutely continuous measures $\mu$ and $\nu$ (with respect to the volume measures of $M$ and $N$). We consider the…

Differential Geometry · Mathematics 2015-07-10 Emmanuel Humbert , Luc Molinet

We introduce and investigate properties of a variant of the semi-discrete optimal transport problem. In this problem, one is given an absolutely continuous source measure and cost function, along with a finite set which will be the support…

Analysis of PDEs · Mathematics 2019-09-13 Mohit Bansil , Jun Kitagawa

We consider harmonic maps into pseudo-Riemannian manifolds. We show the removability of isolated singularities for continuous maps, i.e. that any continuous map from an open subset of R^m into a pseudo-Riemannian manifold which is two times…

Analysis of PDEs · Mathematics 2007-05-23 Frederic Helein

We study the regularity of optimal transport maps between convex domains with quadratic cost. For nondegenerate $C^{\alpha}$-densities, we prove $C^{1, 1-\varepsilon}$-regularity of the potentials up to the boundary. If in addition the…

Analysis of PDEs · Mathematics 2025-07-09 Tristan C. Collins , Freid Tong

The optimal transport (OT) map is a geometry-driven transformation between high-dimensional probability distributions which underpins a wide range of tasks in statistics, applied probability, and machine learning. However, existing…

Machine Learning · Statistics 2025-12-11 Sloan Nietert , Ziv Goldfeld
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