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We present a novel method for efficiently computing optimal transport maps and Wasserstein barycenters in high-dimensional spaces. Our approach uses conditional normalizing flows to approximate the input distributions as invertible…

Machine Learning · Statistics 2025-05-29 Gabriele Visentin , Patrick Cheridito

In this paper, we derive a novel optimal image transport algorithm over sparse dictionaries by taking advantage of Sparse Representation (SR) and Optimal Transport (OT). Concisely, we design a unified optimization framework in which the…

Computer Vision and Pattern Recognition · Computer Science 2023-11-06 Junqing Huang , Haihui Wang , Andreas Weiermann , Michael Ruzhansky

We propose a new approach to graph compression by appeal to optimal transport. The transport problem is seeded with prior information about node importance, attributes, and edges in the graph. The transport formulation can be setup for…

Machine Learning · Computer Science 2019-05-30 Vikas K. Garg , Tommi Jaakkola

In the geometric transportation problem, we are given a collection of points $P$ in $d$-dimensional Euclidean space, and each point is given a supply of $\mu(p)$ units of mass, where $\mu(p)$ could be a positive or a negative integer, and…

Data Structures and Algorithms · Computer Science 2019-02-25 Andrey Boris Khesin , Aleksandar Nikolov , Dmitry Paramonov

A three-dimensional (3D) Poisson solver with longitudinal periodic and transverse open boundary conditions can have important applications in beam physics of particle accelerators. In this paper, we present a fast efficient method to solve…

Accelerator Physics · Physics 2017-09-13 Ji Qiang

This paper proposes a novel Machine Learning-based approach to solve a Poisson problem with mixed boundary conditions. Leveraging Graph Neural Networks, we develop a model able to process unstructured grids with the advantage of enforcing…

We study the geometry of conditional optimal transport (COT) and prove a dynamical formulation which generalizes the Benamou-Brenier Theorem. Equipped with these tools, we propose a simulation-free flow-based method for conditional…

Machine Learning · Computer Science 2024-06-03 Gavin Kerrigan , Giosue Migliorini , Padhraic Smyth

Optimal transport methods have recently established state of the art accuracy and efficiency for holographic laser beam shaping. However, use of such methods is hindered by severe $\mathcal{O}(N^2)$ memory and $\mathcal{O}(N^2)$ time…

Optics · Physics 2026-02-23 Andrii Torchylo , Hunter Swan , Lucas Tellez , Jason M. Hogan

We investigate the optimal transport problem between probability measures when the underlying cost function is understood to satisfy a least action principle, also known as a Lagrangian cost. These generalizations are useful when connecting…

Machine Learning · Computer Science 2024-06-04 Aram-Alexandre Pooladian , Carles Domingo-Enrich , Ricky T. Q. Chen , Brandon Amos

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…

In many applications such as color image processing, data has more than one piece of information associated with each spatial coordinate, and in such cases the classical optimal mass transport (OMT) must be generalized to handle…

Optimization and Control · Mathematics 2018-06-19 Ernest K. Ryu , Yongxin Chen , Wuchen Li , Stanley Osher

A primal-dual accelerated stochastic gradient descent with variance reduction algorithm (PDASGD) is proposed to solve linear-constrained optimization problems. PDASGD could be applied to solve the discrete optimal transport (OT) problem and…

Machine Learning · Statistics 2023-05-31 Yiling Xie , Yiling Luo , Xiaoming Huo

We propose a volumetric formulation for computing the Optimal Transport problem defined on surfaces in $\mathbb{R}^3$, found in disciplines like optics, computer graphics, and computational methodologies. Instead of directly tackling the…

Numerical Analysis · Mathematics 2024-05-16 Richard Tsai , Axel G. R. Turnquist

Discrete variational methods show excellent performance in numerical simulations of different mechanical systems. In this paper, we introduce an iterative procedure for the solution of discrete variational equations for boundary value…

Optimization and Control · Mathematics 2022-06-22 Sebastián J. Ferraro , David Martín de Diego , Rodrigo Takuro Sato Martín de Almagro

Optimal transport has been an essential tool for reconstructing dynamics from complex data. With the increasingly available multifaceted data, a system can often be characterized across multiple spaces. Therefore, it is crucial to maintain…

Optimization and Control · Mathematics 2024-06-06 Zixuan Cang , Yanxiang Zhao

The aim of this paper is to obtain quantitative bounds for solutions to the optimal matching problem in dimension two. These bounds show that up to a logarithmically divergent shift, the optimal transport maps are close to be the identity…

Analysis of PDEs · Mathematics 2018-08-29 Michael Goldman , Martin Huesmann , Felix Otto

Optimal transport has gained much attention in image processing field, such as computer vision, image interpolation and medical image registration. Recently, Bredies et al. (ESAIM:M2AN 54:2351-2382, 2020) and Schmitzer et al. (IEEE T MED…

Numerical Analysis · Mathematics 2023-08-21 Yiming Gao

The paper considers existence results of solution for a linear coupled system of Boltzmann transport equations and related inverse problem. The system models the evolution of three species of particles, photons, electrons and positrons.…

Analysis of PDEs · Mathematics 2019-12-02 Jouko Tervo

In this paper, we study dynamical optimal transport on a connected graph from the perspective of the Benamou-Brenier formulation, where densities are assigned to vertices and velocities to edges. However, directly using Newton's method on…

Numerical Analysis · Mathematics 2026-05-11 Qujiangxue Chen , Jianbo Cui , Luca Dieci , Haomin Zhou

Optimal transport has been used extensively in resource matching to promote the efficiency of resources usages by matching sources to targets. However, it requires a significant amount of computations and storage spaces for large-scale…

Optimization and Control · Mathematics 2019-04-10 Rui Zhang , Quanyan Zhu