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Related papers: On optimal transport of matrix-valued measures

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We discuss a new notion of distance on the space of finite and nonnegative measures which can be seen as a generalization of the well-known Kantorovich-Wasserstein distance. The new distance is based on a dynamical formulation given by an…

Metric Geometry · Mathematics 2018-01-17 Matthias Liero , Alexander Mielke , Giuseppe Savaré

We consider the modified Monge-Kantorovich problem with additional restriction: admissible transport plans must vanish on some fixed functional subspace. Different choice of the subspace leads to different additional properties optimal…

Functional Analysis · Mathematics 2014-04-22 Danila Zaev

We consider a Kantorovich potential associated to an optimal transportation problem between measures that are not necessarily absolutely continuous with respect to the Lebesgue measure, but are comparable to the Lebesgue measure when…

Analysis of PDEs · Mathematics 2023-08-22 Pierre-Emmanuel Jabin , Antoine Mellet

We analyze optimal transport problems with additional entropic cost evaluated along curves in the Wasserstein space which join two probability measures $m_0,m_1$. The effect of the additional entropy functional results into an elliptic…

Analysis of PDEs · Mathematics 2022-11-18 Alessio Porretta

Inspired by the matching of supply to demand in logistical problems, the optimal transport (or Monge--Kantorovich) problem involves the matching of probability distributions defined over a geometric domain such as a surface or manifold. In…

Optimization and Control · Mathematics 2018-05-02 Justin Solomon

We consider the optimal mass transportation problem in $\RR^d$ with measurably parameterized marginals, for general cost functions and under conditions ensuring the existence of a unique optimal transport map. We prove a joint measurability…

Probability · Mathematics 2008-09-09 Joaquin Fontbona , Helene Guerin , Sylvie Meleard

The Kantorovich metric provides a way of measuring the distance between two Borel probability measures on a metric space. This metric has a broad range of applications from bioinformatics to image processing, and is commonly linked to the…

Functional Analysis · Mathematics 2018-10-30 Trubee Davison

This work investigates several aspects related to quantitative stability in optimal transport, as well as uniqueness of the dual transport problem. Our main contributions are as follows. Chapter 1: Observations regarding the quantitative…

Functional Analysis · Mathematics 2025-10-22 William Ford

We study optimal transportation with the quadratic cost function in geodesic metric spaces satisfying suitable non-branching assumptions. We introduce and study the notions of slope along curves and along geodesics and we apply the latter…

Metric Geometry · Mathematics 2011-11-23 Luigi Ambrosio , Tapio Rajala

We develop a theory of optimal transport relative to a distinguished subset, which acts as a reservoir of mass, allowing us to compare measures of different total variation. This relative transportation problem has an optimal solution and…

Metric Geometry · Mathematics 2026-04-08 Peter Bubenik , Alex Elchesen

We study optimal transport between probability measures supported on the same finite metric space, where the ground cost is a distance induced by a weighted connected graph. Building on recent work showing that the resulting Kantorovich…

Optimization and Control · Mathematics 2026-01-14 Jérémie Bigot , Luis Fredes

The presentation covers prerequisite results from Topology and Measure Theory. This is then followed by an introduction into couplings and basic definitions for optimal transport. The Kantrorovich problem is then introduced and an existence…

Probability · Mathematics 2020-10-12 Austin Vandegriffe

We study the problem of estimating, in the sense of optimal transport metrics, a measure which is assumed supported on a manifold embedded in a Hilbert space. By establishing a precise connection between optimal transport metrics, optimal…

Machine Learning · Computer Science 2012-09-06 Guillermo D. Canas , Lorenzo Rosasco

We consider Kantorovich optimal transportation problem in the case where the cost function and marginal distributions continuously depend on a parameter with values in a metric space. We prove the existence of approximate optimal Monge…

Functional Analysis · Mathematics 2023-02-27 Svetlana Popova

Motivated by applications in classification of vector valued measures and multispecies PDE, we develop a theory that unifies existing notions of vector valued optimal transport, from dynamic formulations (\`a la Benamou-Brenier) to static…

Analysis of PDEs · Mathematics 2025-05-07 Katy Craig , Nicolás García Trillos , Đorđe Nikolić

Optimal transport is widely used in pure and applied mathematics to find probabilistic solutions to hard combinatorial matching problems. We extend the Wasserstein metric and other elements of optimal transport from the matching of sets to…

Optimization and Control · Mathematics 2019-07-16 Evan Patterson

In this paper, we present a novel and principled approach to learn the optimal transport between two distributions, from samples. Guided by the optimal transport theory, we learn the optimal Kantorovich potential which induces the optimal…

Machine Learning · Computer Science 2020-06-19 Ashok Vardhan Makkuva , Amirhossein Taghvaei , Sewoong Oh , Jason D. Lee

We consider the optimal transportation problem on a globally hyperbolic spacetime for some cost function $c_2$, which corresponds to the optimal transportation problem on a complete Riemannian manifold where the cost function is the…

Optimization and Control · Mathematics 2025-06-10 Alec Metsch

The optimal (Monge-Kantorovich) transportation problem is discussed from several points of view. The Lagrangian formulation extends the action of the {\em Lagrangian} $L(v,x,t)$ from the set of orbits in $\R^n$ to a set of measure-valued…

Mathematical Physics · Physics 2007-05-23 Gershon Wolansky

Classical optimal transport problem seeks a transportation map that preserves the total mass betwenn two probability distributions, requiring their mass to be the same. This may be too restrictive in certain applications such as color or…

Machine Learning · Statistics 2020-06-15 Laetitia Chapel , Mokhtar Z. Alaya , Gilles Gasso