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This work studies how the introduction of the entropic regularization term in unbalanced Optimal Transport (OT) models may alter their homogeneity with respect to the input measures. We observe that in common settings (including balanced OT…

Optimization and Control · Mathematics 2022-01-07 Théo Lacombe

We study MinMax solution methods for a general class of optimization problems related to (and including) optimal transport. Theoretically, the focus is on fitting a large class of problems into a single MinMax framework and generalizing…

Optimization and Control · Mathematics 2020-10-23 Luca De Gennaro Aquino , Stephan Eckstein

Models for branched networks are often expressed as the minimization of an energy $M^\alpha$ over vector measures concentrated on $1$-dimensional rectifiable sets with a divergence constraint. We study a Modica-Mortola type approximation…

Analysis of PDEs · Mathematics 2015-09-29 Antonin Monteil

This paper focuses on martingale optimal transport problems when the martingales are assumed to have bounded quadratic variation. First, we give a result that characterizes the existence of a probability measure satisfying some convex…

Probability · Mathematics 2020-03-18 Erhan Bayraktar , Xin Zhang , Zhou Zhou

Optimal transport (OT) has become a natural framework for guiding the probability flows. Yet, the majority of recent generative models assume trivial geometry (e.g., Euclidean) and rely on strong density-estimation assumptions, yielding…

Machine Learning · Computer Science 2025-09-23 Nazar Buzun , Daniil Shlenskii , Maxim Bobrin , Dmitry V. Dylov

We study the Schr\"odinger-Bass problem, a one-parameter family of semimartingale optimal transport problems indexed by $\beta>0$, whose limiting regimes interpolate between the classical Schr\"odinger bridge, the Brenier-Strassen problem,…

Probability · Mathematics 2026-04-03 Manuel Hasenbichler , Gudmund Pammer , Stefan Thonhauser

This paper connects discrete optimal transport to a certain class of multi-objective optimization problems. In both settings, the decision variables can be organized into a matrix. In the multi-objective problem, the notion of Pareto…

Optimization and Control · Mathematics 2017-12-04 Johannes M. Schumacher

The aim of this article is to show that the Monge-Kantorovich problem is the limit of a sequence of entropy minimization problems when a fluctuation parameter tends down to zero. We prove the convergence of the entropic values to the…

Optimization and Control · Mathematics 2013-08-02 Christian Léonard

In this paper we consider a shape optimization problem for the minimization of the erosion, that is caused by the impact of inert particles onto the walls of a bended pipe. Using the continuous adjoint approach, we formally compute the…

Optimization and Control · Mathematics 2019-08-14 Raphael Hohmann , Christian Leithäuser

Consider the Monge-Kantorovich problem of transporting densities $\rho_0$ to $\rho_1$ on $\mathbb{R}^d$ with a strictly convex cost function. A popular relaxation of the problem is the one-parameter family called the entropic cost problem.…

Probability · Mathematics 2019-10-14 Soumik Pal

We prove quantitative bounds on the stability of optimal transport maps and Kantorovich potentials from a fixed source measure $\rho$ under variations of the target measure $\mu$, when the cost function is the squared Riemannian distance on…

Metric Geometry · Mathematics 2025-05-06 Jun Kitagawa , Cyril Letrouit , Quentin Mérigot

We prove existence and regularity of optimal shapes for the problem$$\min\Big\{P(\Omega)+\mathcal{G}(\Omega):\ \Omega\subset D,\ |\Omega|=m\Big\},$$where $P$ denotes the perimeter, $|\cdot|$ is the volume, and the functional $\mathcal{G}$…

Optimization and Control · Mathematics 2016-09-20 Guido De Philippis , Jimmy Lamboley , Michel Pierre , Bozhidar Velichkov

Optimal transport (OT) and unbalanced optimal transport (UOT) are central in many machine learning, statistics and engineering applications. 1D OT is easily solved, with complexity O(n log n), but no efficient algorithm was known for 1D…

Performance · Computer Science 2024-02-15 Gabriel Gouvine

Optimal transport (OT) theory has been been used in machine learning to study and characterize maps that can push-forward efficiently a probability measure onto another. Recent works have drawn inspiration from Brenier's theorem, which…

Machine Learning · Computer Science 2023-02-13 Théo Uscidda , Marco Cuturi

One of the simplest models used in studying the dynamics of large-scale structure in cosmology, known as the Zeldovich approximation, is equivalent to the three-dimensional inviscid Burgers equation for potential flow. For smooth initial…

Analysis of PDEs · Mathematics 2015-06-03 Uriel Frisch , Olga Podvigina , Barbara Villone , Vladislav Zheligovsky

We show in full generality the stability of optimal traffic paths in branched transport: namely we prove that any limit of optimal traffic paths is optimal as well. This solves an open problem in the field (cf. Open problem 1 in the book…

Analysis of PDEs · Mathematics 2019-11-25 Maria Colombo , Antonio De Rosa , Andrea Marchese

We consider the standard first passage percolation model on Z^d with a distribution G on R+ that admits an exponential moment. We study the maximal flow between a compact convex subset A of R^d and infinity. The study of maximal flow is…

Probability · Mathematics 2018-11-27 Barbara Dembin

We study first order equations of continuity and transport type on metric spaces of martingale dimension one, including finite metric graphs, p.c.f. self-similar sets and classical Sierpi\'nski carpets. On such spaces solutions of the…

Analysis of PDEs · Mathematics 2024-12-12 Michael Hinz , Waldemar Schefer

We study multi-marginal optimal transport (MOT) problems where the underlying cost has a graphical structure. These graphical multi-marginal optimal transport problems have found applications in several domains including traffic flow…

Optimization and Control · Mathematics 2025-12-02 Jiaojiao Fan , Isabel Haasler , Qinsheng Zhang , Johan Karlsson , Yongxin Chen

Shape optimization with constraints given by partial differential equations (PDE) is a highly developed field of optimization theory. The elegant adjoint formalism allows to compute shape gradients at the computational cost of a further PDE…

Optimization and Control · Mathematics 2023-03-03 Matthias Bolten , Onur Tanil Doganay , Hanno Gottschalk , Kathrin Klamroth
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