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We develop a general condition on the cost function which is sufficient to imply Monge solution and uniqueness results in the multi-marginal optimal transport problem. This result unifies and generalizes several results in the rather…

Analysis of PDEs · Mathematics 2013-07-25 Young-Heon Kim , Brendan Pass

We show continuity of the martingale optimal transport optimisation problem as a functional of its marginals. This is achieved via an estimate on the projection in the nested/causal Wasserstein distance of an arbitrary coupling on to the…

Probability · Mathematics 2022-06-22 Johannes Wiesel

We provide a solution to the problem of optimal transport by Brownian martingales in general dimensions whenever the transport cost satisfies certain subharmonic properties in the target variable, as well as a stochastic version of the…

Analysis of PDEs · Mathematics 2020-10-07 Nassif Ghoussoub , Young-Heon Kim , Aaron Zeff Palmer

Standard representational similarity methods align each layer of a network to its best match in another independently, producing asymmetric results, lacking a global alignment score, and struggling with networks of different depths. These…

Machine Learning · Computer Science 2026-04-23 Shaan Shah , Meenakshi Khosla

We study the optimal transport between two probability measures on the real line, where the transport plans are laws of one-step martingales. A quasi-sure formulation of the dual problem is introduced and shown to yield a complete duality…

Probability · Mathematics 2016-06-14 Mathias Beiglböck , Marcel Nutz , Nizar Touzi

We consider an optimal transport problem between laws of random probability measures: given a base cost function, we build the associated OT cost between probability measures that in turn we use to define the OT cost between probability…

Optimization and Control · Mathematics 2026-05-05 Alessandro Pinzi

We introduce a new non-linear optimal transport formulation for a pair of probability measures on $\mathbb{R}^d$ sharing a common barycentre, in which admissible transference plans satisfy two martingale-type constraints. This bi-martingale…

Probability · Mathematics 2025-11-03 Karol Bołbotowski

The optimal transport problem has recently developed into a powerful framework for various applications in estimation and control. Many of the recent advances in the theory and application of optimal transport are based on regularizing the…

Optimization and Control · Mathematics 2021-03-12 Isabel Haasler , Axel Ringh , Yongxin Chen , Johan Karlsson

We consider a class of stochastic optimal transport, SOT for short, with given two endpoint marginals in the case where a cost function exhibits at most quadratic growth. We first study the upper and lower estimates, the short--time…

Probability · Mathematics 2023-09-19 Toshio Mikami

We propose a numerical algorithm for the computation of multi-marginal optimal transport (MMOT) problems involving general probability measures that are not necessarily discrete. By developing a relaxation scheme in which marginal…

Optimization and Control · Mathematics 2025-12-29 Ariel Neufeld , Qikun Xiang

We solve the martingale optimal transport problem for cost functionals represented by optimal stopping problems. The measure-valued martingale approach developed in ArXiv: 1507.02651 allows us to obtain an equivalent infinite-dimensional…

Probability · Mathematics 2017-11-27 Erhan Bayraktar , Alexander Cox , Yavor Stoev

Given a stochastic state process $(X_t)_t$ and a real-valued submartingale cost process $(S_t)_t$, we characterize optimal stopping times $\tau$ that minimize the expectation of $S_\tau$ while realizing given initial and target…

Probability · Mathematics 2020-12-24 Nassif Ghoussoub , Young-Heon Kim , Aaron Zeff Palmer

We consider optimal transport problems where the cost is optimized over controlled dynamics and the end time is free. Unlike the classical setting, the search for optimal transport plans also requires the identification of optimal "stopping…

Optimization and Control · Mathematics 2018-07-09 Nassif Ghoussoub , Young-Heon Kim , Aaron Zeff Palmer

Optimal transport (OT) based data analysis is often faced with the issue that the underlying cost function is (partially) unknown. This paper is concerned with the derivation of distributional limits for the empirical OT value when the cost…

Statistics Theory · Mathematics 2023-01-05 Shayan Hundrieser , Gilles Mordant , Christoph Alexander Weitkamp , Axel Munk

We study a single-period optimal transport problem on $\mathbb{R}^2$ with a covariance-type cost function $c(x,y) = (x_1-y_1)(x_2-y_2)$ and a backward martingale constraint. We show that a transport plan $\gamma$ is optimal if and only if…

Probability · Mathematics 2022-09-13 Dmitry Kramkov , Yan Xu

We develop a method to solve, theoretically and numerically, general optimal stopping problems. Our general setting allows for multiple exercise rights, i.e., optimal multiple stopping, for a robust evaluation that accounts for model…

We study the quadratically regularized optimal transport (QOT) problem for quadratic cost and compactly supported marginals $\mu$ and $\nu$. It has been empirically observed that the optimal coupling $\pi_\epsilon$ for the QOT problem has…

Optimization and Control · Mathematics 2024-10-07 Johannes Wiesel , Xingyu Xu

A convex duality result for martingale optimal transport problems with two marginals was established in Beiglb\"ock et al. (2013). In this paper we provide a generalization of this result to the multi-period setting.

Probability · Mathematics 2024-03-06 Julian Sester

This paper analyzes the support of the conditional distribution of optimal martingale transport plans in higher dimension. In the context of a distance coupling in dimension larger than 2, previous results established by Ghoussoub, Kim &…

Probability · Mathematics 2018-11-07 Hadrien De March

We study the complexity of approximating the multimarginal optimal transport (MOT) distance, a generalization of the classical optimal transport distance, considered here between $m$ discrete probability distributions supported each on $n$…

Machine Learning · Statistics 2022-02-23 Tianyi Lin , Nhat Ho , Marco Cuturi , Michael I. Jordan