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We define new differential structures on the Wasserstein spaces $\mathcal{W}_p(M)$ for $p > 2$ and a general Riemannian manifold $(M,g)$. We consider a very general and possibly degenerate second order partial differential flow equation…

Analysis of PDEs · Mathematics 2026-03-17 Arthur Schichl

Von Renesse and the author (Ann. Prob. '09) developed a second order calculus on the Wasserstein space P([0,1]) of probability measures on the unit interval. The basic objects of interest had been Dirichlet form, semigroup and continuous…

Probability · Mathematics 2011-05-20 Karl-Theodor Sturm

We propose a primal--dual technique that applies to infinite dimensional equality constrained problems, in particular those arising from optimal control. As an application of our general framework, we solve a control-constrained double…

Optimization and Control · Mathematics 2023-11-14 Regina S. Burachik , C. Yalçın Kaya , Xuemei Liu

In this paper, we introduce a new image denoising model: the damped flow (DF), which is a second order nonlinear evolution equation associated with a class of energy functionals of image. The existence, uniqueness and regularization…

Numerical Analysis · Mathematics 2019-10-01 George Baravdish , Olof Svensson , Mårten Gulliksson , Ye Zhang

We present a novel approach to approximate Gaussian and mixture-of-Gaussians filtering. Our method relies on a variational approximation via a gradient-flow representation. The gradient flow is derived from a Kullback--Leibler discrepancy…

Computation · Statistics 2023-06-21 Adrien Corenflos , Hany Abdulsamad

In this work, we propose a numerical method to compute the Wasserstein Hamiltonian flow (WHF), which is a Hamiltonian system on the probability density manifold. Many well-known PDE systems can be reformulated as WHFs. We use parameterized…

Numerical Analysis · Mathematics 2023-07-18 Hao Wu , Shu Liu , Xiaojing Ye , Haomin Zhou

This paper develops a rigorous probabilistic framework that extends denoising diffusion models to the setting of noncommutative random variables. Building on Voiculescu's theory of free entropy and free Fisher information, we formulate…

Probability · Mathematics 2025-11-04 Swagatam Das

Score matching provides an effective approach to learning flexible unnormalized models, but its scalability is limited by the need to evaluate a second-order derivative. In this paper, we present a scalable approximation to a general family…

Machine Learning · Statistics 2020-02-19 Ziyu Wang , Shuyu Cheng , Yueru Li , Jun Zhu , Bo Zhang

We consider a controlled second order differential equation which is partially observed with an additional fractional noise. we study the asymptotic (for large observation time) design problem of the input and give an efficient estimator of…

Probability · Mathematics 2019-04-09 Chunhao Cai , Wujun LV

We consider sampling from a Gibbs distribution by evolving finitely many particles. We propose a preconditioned version of a recently proposed noise-free sampling method, governed by approximating the score function with the numerically…

Machine Learning · Statistics 2026-05-18 Hong Ye Tan , Stanley Osher , Wuchen Li

This paper is concerned with a rigorous convergence analysis of a fully discrete Lagrangian scheme for the Hele-Shaw flow, which is the fourth order thin-film equation with linear mobility in one space dimension. The discretization is based…

Numerical Analysis · Mathematics 2015-09-07 Horst Osberger , Daniel Matthes

We consider mixed model of traffic flow distribution in large networks (BMW model, 1954 & Stable Dynamic model, 1999). We build dual problem and consider primal-dual mirror descent method for the dual problem. There are two ways to recover…

Optimization and Control · Mathematics 2018-08-16 Alexander Gasnikov , Evgenia Gasnikova , Yurii Nesterov

This article develops a primal dual formulation for a primal proximal approach suitable for a large class of non-convex models in the calculus of variations. The results are established through standard tools of functional analysis, convex…

Optimization and Control · Mathematics 2021-07-27 Fabio Silva Botelho

We introduce a time discretization for Wasserstein gradient flows based on the classical Backward Differentiation Formula of order two. The main building block of the scheme is the notion of geodesic extrapolation in the Wasserstein space,…

Analysis of PDEs · Mathematics 2023-11-20 Thomas Gallouët , Andrea Natale , Gabriele Todeschi

This paper shows that the implicit bias of gradient descent on linearly separable data is exactly characterized by the optimal solution of a dual optimization problem given by a smoothed margin, even for general losses. This is in contrast…

Machine Learning · Computer Science 2020-11-13 Ziwei Ji , Matus Telgarsky

Many studies have been conducted on flows of probability measures, often in terms of gradient flows. We utilize a generalized notion of derivatives with respect to time to model the instantaneous evolution of empirically observed…

Methodology · Statistics 2021-09-16 Yaqing Chen , Hans-Georg Müller

We perform a convergence analysis of a discrete-in-time minimization scheme approximating a finite dimensional singularly perturbed gradient flow. We allow for different scalings between the viscosity parameter $\varepsilon$ and the time…

Analysis of PDEs · Mathematics 2018-11-14 Giovanni Scilla , Francesco Solombrino

In this paper, we consider the convergence rate with respect to Wasserstein distance in the invariance principle for deterministic nonuniformly hyperbolic systems, where both discrete time systems and flows are included. Our results apply…

Dynamical Systems · Mathematics 2023-01-04 Zhenxin Liu , Zhe Wang

We consider a one-dimensional kinetic model of granular media in the case where the interaction potential is quadratic. Taking advan- tage of a simple first integral, we can use a reformulation (equivalent to the initial kinetic model for…

Analysis of PDEs · Mathematics 2015-06-19 Martial Agueh , Guillaume Carlier

We focus here on a class of fourth-order parabolic equations that can be written as a system of second-order equations by introducing an auxiliary variable. We design a novel second-order fully discrete mixed finite element method to…

Numerical Analysis · Mathematics 2020-08-28 Sana Keita , Abdelaziz Beljadid , Yves Bourgault
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