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In this paper, we establish a regularity theory for the optimal transport problem when the target is composed of two disjoint convex domains. This is an important model in which singularities arise. Even though the singular set does not…

Analysis of PDEs · Mathematics 2025-07-22 Shibing Chen , Jiakun Liu

We establish that solving an optimal transportation problem in which the source and target densities are defined on manifolds with different dimensions, is equivalent to solving a new nonlocal analog of the Monge-Amp\`ere equation,…

Analysis of PDEs · Mathematics 2019-05-30 Robert J McCann , Brendan Pass

The classical problem of optimal transportation can be formulated as a linear optimization problem on a convex domain: among all joint measures with fixed marginals find the optimal one, where optimality is measured against a cost function.…

Optimization and Control · Mathematics 2012-11-29 Jonathan Korman , Robert J. McCann

In this note, we extend the regularity theory for monotone measure-preserving maps, also known as optimal transports for the quadratic cost optimal transport problem, to the case when the support of the target measure is an arbitrary convex…

Analysis of PDEs · Mathematics 2023-05-17 Alessio Figalli , Yash Jhaveri

The goal of this paper is to settle the study of non-commutative optimal transport problems with convex regularization, in their static and finite-dimensional formulations. We consider both the balanced and unbalanced problem and show in…

Mathematical Physics · Physics 2025-06-27 Emanuele Caputo , Augusto Gerolin , Nataliia Monina , Lorenzo Portinale

In this paper, we investigate optimal (partial) transport problems for which the target is a non-convex polygonal domain in \(\mathbb{R}^2\). For the complete optimal transport problem, we prove that the singular set is locally a smooth…

Analysis of PDEs · Mathematics 2026-05-19 Shibing Chen , Yuanyuan Li , Jiakun Liu

In this paper, we establish a global regularity result for the optimal transport problem with the quadratic cost, where the domains may not be convex. This result is obtained by a perturbation argument, using a recent global regularity of…

Analysis of PDEs · Mathematics 2019-01-30 Shibing Chen , Jiakun Liu , Xu-Jia Wang

This article studies problems of optimal transport, by embedding them in a general functional analytic framework of convex optimization. This provides a unified treatment of a large class of related problems in probability theory and allows…

Probability · Mathematics 2017-10-31 Teemu Pennanen , Ari-Pekka Perkkiö

This note concerns the relationship between conditions on cost functions and domains and the convexity properties of potentials in optimal transportation and the continuity of the associated optimal mappings. In particular, we prove that if…

Analysis of PDEs · Mathematics 2007-05-23 Neil S. Trudnger , Xu-Jia Wang

This paper deals with the existence of optimal transport maps for some optimal transport problems with a convex but non strictly convex cost. We give a decomposition strategy to address this issue. As part of our strategy, we have to treat…

Classical Analysis and ODEs · Mathematics 2009-09-16 Guillaume Carlier , Luigi De Pascale , Filippo Santambrogio

We consider maps $T$ solving the optimal transport problem with a cost $c(x-y)$ modeled on the $p$-cost. For H\"older continuous marginals, we prove a $C^{1,\alpha}$-partial regularity result for $T $in the set $\{|T(x)-x|>0\}$.

Analysis of PDEs · Mathematics 2024-07-15 Michael Goldman , Lukas Koch

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

Optimal maps, solutions to the optimal transportation problems, are completely determined by the corresponding c-convex potential functions. In this paper, we give simple sufficient conditions for a smooth function to be c-convex when the…

Differential Geometry · Mathematics 2010-06-22 Paul W. Y. Lee

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

We study the entropic regularization of the optimal transport problem in dimension 1 when the cost function is the distance c(x, y) = |y -- x|. The selected plan at the limit is, among those which are optimal for the non-penalized problem,…

Optimization and Control · Mathematics 2019-04-22 Simone Di Marino , Jean Louet

In this paper, we investigate the optimal transport problem when the source is a non-convex polygonal domain in $\mathbb{R}^2$. We show a global $W^{2,1+\epsilon}$ estimate for potentials of optimal transport. Our method applies to a more…

Analysis of PDEs · Mathematics 2026-01-26 Shengnan Hu , Yuanyuan Li

In order to circumvent the difficulties in solving numerically the discrete optimal transport problem, in which one minimizes the linear target function $P\mapsto\langle C,P\rangle:=\sum_{i,j}C_{ij}P_{ij}$, Cuturi introduced a variant of…

Optimization and Control · Mathematics 2020-11-30 Daiji Tsutsui

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

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

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
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