English
Related papers

Related papers: Shadow couplings

200 papers

The purpose of this paper is to introduce a new numerical method to solve multi-marginal optimal transport problems with pairwise interaction costs. The complexity of multi-marginal optimal transport generally scales exponentially in the…

Optimization and Control · Mathematics 2023-08-08 Luca Nenna , Brendan Pass

We propose a new weak convergence theorem for martingales, under gentler conditions than the usual convergence in probability of the sequence of associated quadratic variations. Its proof requires the combined use of Skorohod's…

Probability · Mathematics 2025-06-30 Bruno Rémillard , Jean Vaillancourt

Many numerical and learning algorithms rely on the solution of the Monge-Kantorovich problem and Wasserstein distances, which provide appropriate distributional metrics. While the natural approach is to treat the problem as an…

Optimization and Control · Mathematics 2025-12-11 Mohsen Sadr , Peyman Mohajerin Esfahani , Hossein Gorji

Inspired by the matching of supply to demand in logistical problems, the optimal transport (or Monge--Kantorovich) problem involves the matching of probability distributions defined over a geometric domain such as a surface or manifold. In…

Optimization and Control · Mathematics 2018-05-02 Justin Solomon

We give a new proof of the Caffarelli contraction theorem, which states that the Brenier optimal transport map sending the standard Gaussian measure onto a uniformly log-concave probability measure is Lipschitz. The proof combines a recent…

Probability · Mathematics 2019-04-15 Max Fathi , Nathael Gozlan , Maxime Prodhomme

We explore the asymptotic convergence and nonasymptotic maximal inequalities of supermartingales and backward submartingales in the space of positive semidefinite matrices. These are natural matrix analogs of scalar nonnegative…

Probability · Mathematics 2025-10-21 Hongjian Wang , Aaditya Ramdas

We give a short new proof of a version of the Kruskal-Katona theorem due to Lov\'asz. Our method can be extended to a stability result, describing the approximate structure of configurations that are close to being extremal, which answers a…

Combinatorics · Mathematics 2008-06-13 Peter Keevash

In this paper we study theoretical properties of the entropy-transport functional with repulsive cost functions. We provide sufficient conditions for the existence of a minimizer in a class of metric spaces and prove the…

Analysis of PDEs · Mathematics 2019-07-19 Augusto Gerolin , Anna Kausamo , Tapio Rajala

Let $\mu$ = ($\mu$t)t$\in$R be any 1-parameter family of probability measures on R. Its quantile process (Gt)t$\in$R : ]0, 1[ $\rightarrow$ RR, given by Gt($\alpha$) = inf{x $\in$ R : $\mu$t(]--$\infty$, x]) > $\alpha$}, is not Markov in…

Probability · Mathematics 2018-04-30 Charles Boubel , Nicolas Juillet

We show non-asymptotic exponential convergence of Sinkhorn iterates to the Schr\"odinger potentials, solutions of the quadratic Entropic Optimal Transport problem on $\mathbb{R}^ d$. Our results hold under mild assumptions on the marginal…

Probability · Mathematics 2026-05-01 Giovanni Conforti , Alain Durmus , Giacomo Greco

We consider SDEs with (distributional) drift in negative Besov spaces and random initial condition and investigate them from two different viewpoints. In the first part we set up a martingale problem and show its well-posedness.We then…

Probability · Mathematics 2024-03-08 Elena Issoglio , Francesco Russo

The classical fuel-optimal two-impulse rendezvous problem between Keplerian orbits is revisited from a family-based perspective. Conventional approaches often yield isolated optimal solutions whose mutual relationships remain unclear; yet,…

Optimization and Control · Mathematics 2026-04-07 Beom Park , Kathleen C. Howell , Jaewoo Kim , Jaemyung Ahn

During recent decades, there has been a substantial development in optimal mass transport theory and methods. In this work, we consider multi-marginal problems wherein only partial information of each marginal is available, which is a setup…

Signal Processing · Electrical Eng. & Systems 2019-05-13 Filip Elvander , Isabel Haasler , Andreas Jakobsson , Johan Karlsson

We prove uniqueness and Monge solution results for multi-marginal optimal transportation problems with a certain class of surplus functions; this class arises naturally in multi-agent matching problems in economics. This result generalizes…

Analysis of PDEs · Mathematics 2012-10-30 Brendan Pass

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

Couplings play a central role in the analysis of Markov chain convergence and in the construction of novel Markov chain Monte Carlo estimators, diagnostics, and variance reduction techniques. The set of possible couplings is often…

Statistics Theory · Mathematics 2023-01-09 John O'Leary , Guanyang Wang

In this paper we consider convex subsets of locally-convex topological vector spaces. Given a fixed point in such a convex subset, we show that there exists a curve completely contained in the convex subset and leaving the point in a given…

Optimization and Control · Mathematics 2018-10-16 Rodolfo Rios-Zertuche

In 1934, Reinhardt conjectured that the shape of the centrally symmetric convex body in the plane whose densest lattice packing has the smallest density is a smoothed octagon. This conjecture is still open. We formulate the Reinhardt…

Optimization and Control · Mathematics 2017-03-07 Thomas Hales

The stochastic Landau-Lifshitz-Bloch equation in dimensions 1; 2; and 3 perturbed by pure jump noise is considered in the Marcus canonical form. A proof for existence of a martingale solution is given. The proof uses the Faedo-Galerkin…

Probability · Mathematics 2023-02-13 Soham Gokhale , Utpal Manna

For probability measures $\mu,\nu$ and $\rho$ define the cost functionals \begin{align*} C(\mu,\rho):=\sup_{\pi\in \Pi(\mu,\rho)} \int \langle x,y\rangle\, \pi(dx,dy),\quad C(\nu,\rho):=\sup_{\pi\in \Pi(\nu,\rho)} \int \langle x,y\rangle\,…

Probability · Mathematics 2023-03-09 Johannes Wiesel , Erica Zhang
‹ Prev 1 8 9 10 Next ›