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Related papers: Limit Theorems for Optimal Mass Transportation

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Several extensions of the classical optimal transport distances to the quantum setting have been proposed. In this paper, we investigate the pseudometrics introduced by Golse, Mouhot and Paul in [Commun Math Phys 343:165-205, 2016] and by…

Analysis of PDEs · Mathematics 2023-12-25 Laurent Lafleche

In this series of lectures we introduce the Monge-Kantorovich problem of optimally transporting one distribution of mass onto another, where optimality is measured against a cost function c(x,y). Connections to geometry, inequalities, and…

Analysis of PDEs · Mathematics 2010-11-15 Nestor Guillen , Robert McCann

The Wasserstein metric or earth mover's distance (EMD) is a useful tool in statistics, machine learning and computer science with many applications to biological or medical imaging, among others. Especially in the light of increasingly…

Optimization and Control · Mathematics 2018-01-26 Jörn Schrieber , Dominic Schuhmacher , Carsten Gottschlich

We examine the optimal mass transport problem in $\mathbb{R}^{n}$ between densities having independent compact support by considering the geometry of a continuous interpolating support boundary in space-time within which the mass density…

Optimization and Control · Mathematics 2021-06-22 Anthony Yezzi

In this paper we propose a gauge-theoretic approach to the problems of optimal mass transport for vector and matrix densities. This resolves both the issues of positivity and action transitivity constraints. Bures-type metrics on the…

Differential Geometry · Mathematics 2025-10-03 Boris Khesin , Klas Modin

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

During the last decade Optimal Transport had a relevant role in the study of geometry of singular spaces that culminated with the Lott-Sturm-Villani theory. The latter is built on the characterisation of Ricci curvature lower bounds in…

Metric Geometry · Mathematics 2020-05-04 Fabio Cavalletti , Nicola Gigli , Flavia Santarcangelo

We address the Monge problem in metric spaces with a geodesic distance: (X, d) is a Polish space and dN is a geodesic Borel distance which makes (X,dN) a possibly branching geodesic space. We show that under some assumptions on the…

Probability · Mathematics 2012-10-01 Fabio Cavalletti

We present a minimization problem with a horizontal divergence-type constraint in the Heisenberg group. Our study explores its dual formulation and examines its relationship with the congested optimal transport problem, for $1 < p <…

Analysis of PDEs · Mathematics 2025-10-29 Michele Circelli , Albert Clop

The optimal transport problem is studied in the context of Lorentz-Finsler geometry. For globally hyperbolic Lorentz-Finsler spacetimes the first Kantorovich problem and the Monge problem are solved. Further the intermediate regularity of…

Differential Geometry · Mathematics 2018-04-20 Stefan Suhr

We consider an extension of the Monge-Kantorovitch optimal transportation problem. The mass is transported along a continuous semimartingale, and the cost of transportation depends on the drift and the diffusion coefficients of the…

Probability · Mathematics 2013-10-04 Xiaolu Tan , Nizar Touzi

This paper is concerned with the study of the Monge optimal transport problem in sub-Riemannian manifolds where the cost is given by the square of the sub-Riemannian distance. Our aim is to extend previous results on existence and…

Differential Geometry · Mathematics 2017-06-23 Zeinab Badreddine

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 show that existence and uniqueness of solutions to transported Monge-Ampere problem on complex compact toric manifold follows easily from the real theory of optimal transportation.

Analysis of PDEs · Mathematics 2021-02-18 Szymon Myga

Caffarelli's contraction theorem bounds the derivative of the optimal transport map between a log-convex measure and a strongly log-concave measure. We show that an analogous phenomenon holds on the level of the trace: The trace of the…

Analysis of PDEs · Mathematics 2025-11-26 Guido De Philippis , Yair Shenfeld

Caffarelli's contraction theorem states that the Brenier optimal transport map from the standard Gaussian measure to a more log-concave probability measure is 1-Lipschitz. Owing to its many applications in analysis, probability, and…

Differential Geometry · Mathematics 2026-05-26 Shrey Aryan

Motivated by conjectures in Mirror Symmetry, we continue the study of the real Monge--Amp\`ere operator on the boundary of a simplex. This can be formulated in terms of optimal transport, and we consider, more generally, the problem of…

Analysis of PDEs · Mathematics 2025-01-14 Rolf Andreasson , Jakob Hultgren , Mattias Jonsson , Enrica Mazzon , Nicholas McCleerey

Let M and \bar M be n-dimensional manifolds equipped with suitable Borel probability measures \rho and \bar\rho. Ma, Trudinger & Wang gave sufficient conditions on a transportation cost c \in C^4(M \times \bar M) to guarantee smoothness of…

Differential Geometry · Mathematics 2007-12-20 Young-Heon Kim , Robert J. McCann

The goal of the present work is three-fold. The first goal is to set foundational results on optimal transport in Lorentzian (pre-)length spaces, including cyclical monotonicity, stability of optimal couplings and Kantorovich duality…

Metric Geometry · Mathematics 2025-03-14 Fabio Cavalletti , Andrea Mondino

Many problems in machine learning involve calculating correspondences between sets of objects, such as point clouds or images. Discrete optimal transport provides a natural and successful approach to such tasks whenever the two sets of…

Machine Learning · Statistics 2019-02-28 David Alvarez-Melis , Stefanie Jegelka , Tommi S. Jaakkola