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Related papers: Mass transportation with LQ cost functions

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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 quadratically regularized optimal transport problem has recently been considered in various applications where the coupling needs to be \emph{sparse}, i.e., the density of the coupling needs to be zero for a large subset of the product…

Analysis of PDEs · Mathematics 2024-08-01 Alejandro Garriz-Molina , Alberto González-Sanz , Gilles Mordant

Optimal mass transport is described by an approximation of transport cost via semi-discrete costs. The notions of optimal partition and optimal strong partition are given as well. We also suggest an algorithm for computation of Optimal…

Numerical Analysis · Mathematics 2015-02-17 Gershon Wolansky

We present a numerical method to solve the optimal transport problem with a quadratic cost when the source and target measures are periodic probability densities. This method is based on a numerical resolution of the corresponding…

Numerical Analysis · Mathematics 2011-03-02 Louis-Philippe Saumier , Martial Agueh , Boualem Khouider

Given a transportation cost $c: M \times\bar M \to\mathbf{R}$, optimal maps minimize the total cost of moving masses from $M$ to $\bar M$. We find a pseudo-metric and a calibration form on $M\times\bar M$ such that the graph of an optimal…

Differential Geometry · Mathematics 2010-04-13 Young-Heon Kim , Robert J. McCann , Micah Warren

We study the vanishing-regularization limit of entropically regularized optimal transport (EOT) for the Euclidean distance cost $c(x,y)=\|x-y\|$ in dimension $d>1$. We develop a comprehensive variational convergence framework that entails…

Optimization and Control · Mathematics 2026-04-29 Marcel Nutz , Chenyang Zhong

Optimal Transport (OT) has fueled machine learning (ML) across many domains. When paired data measurements $(\boldsymbol{\mu}, \boldsymbol{\nu})$ are coupled to covariates, a challenging conditional distribution learning setting arises.…

We define and discuss the properties of a class of cost functions on the sphere which we term defective cost functions. We then discuss how to extend these definitions and some properties to cost functions defined on Euclidean space and on…

Analysis of PDEs · Mathematics 2024-06-05 Axel G. R. Turnquist

We study the entropic regularizations of optimal transport problems under suitable summability assumptions on the point-wise transport cost. These summability assumptions already appear in the literature. However, we show that the weakest…

Optimization and Control · Mathematics 2025-12-30 Camilla Brizzi , Luigi De Pascale , Anna Kausamo

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

We propose a model to describe the optimal distributions of residents and services in a prescribed urban area. The cost functional takes into account the transportation costs (according to a Monge--Kantorovich-type criterion) and two…

Optimization and Control · Mathematics 2013-12-24 Giuseppe Buttazzo , Filippo Santambrogio

We establish numerical methods for solving the martingale optimal transport problem (MOT) - a version of the classical optimal transport with an additional martingale constraint on transport's dynamics. We prove that the MOT value can be…

Probability · Mathematics 2019-04-08 Gaoyue Guo , Jan Obloj

We study multi-marginal optimal transport problems from a probabilistic graphical model perspective. We point out an elegant connection between the two when the underlying cost for optimal transport allows a graph structure. In particular,…

Optimization and Control · Mathematics 2020-06-26 Isabel Haasler , Rahul Singh , Qinsheng Zhang , Johan Karlsson , Yongxin Chen

We consider the multidimensional Monge-Kantrovich transport problem in an abstract setting. Our main results state that if a cost function and marginal measures are invariant by a family of transformations, then a solution of the Kantrovich…

Analysis of PDEs · Mathematics 2015-04-22 Abbas Moameni

We study a multi-marginal optimal transportation problem with a cost function of the form $c(x_{1}, \ldots,x_{m})=\sum_{k=1}^{m-1}|x_{k}-x_{k+1}|^{2} + |x_{m}- F(x_{1})|^{2}$, where $F: \mathbb{R}^n \rightarrow \mathbb{R}^n$. When $m=4$,…

Optimization and Control · Mathematics 2020-01-13 Brendan Pass , Adolfo Vargas-Jiménez

The function that maps a family of probability measures to the solution of the dual entropic optimal transport problem is known as the Schr\"odinger map. We prove that when the cost function is $\mathcal{C}^{k+1}$ with $k\in \mathbb{N}^*$…

Optimization and Control · Mathematics 2024-03-04 Guillaume Carlier , Lénaïc Chizat , Maxime Laborde

We study the consequences of the equivalence between the least gradient problem and a boundary-to-boundary optimal transport problem in two dimensions. We extend the relationship between the two problems to their respective dual problems,…

Analysis of PDEs · Mathematics 2021-02-12 Wojciech Górny

In classical optimal transport, the contributions of Benamou$-$Brenier and McCann regarding the time-dependent version of the problem are cornerstones of the field and form the basis for a variety of applications in other mathematical…

This paper considers the evolution dynamics of the free boundaries in terms of the change of $m$, the allowed amount of transported mass or the change of $\lambda$, the transportation cost cap, i.e. the allowed maximum cost for a unit mass…

Analysis of PDEs · Mathematics 2015-08-12 Gonzalo Davila , Young-Heon Kim

We present a method for optimal control with respect to a linear cost function for positive linear systems with coupled input constraints. We show that the optimal cost function and resulting sparse state feedback for these systems can be…

Optimization and Control · Mathematics 2023-11-07 David Ohlin , Emma Tegling , Anders Rantzer