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In this paper, we study the topological asymptotic expansion of a topology optimisation problem that is constrained by the Poisson equation with the design/shape variable entering through the right hand side. Using an averaged adjoint…

Numerical Analysis · Mathematics 2022-11-23 Phillip Baumann , Peter Gangl , Kevin Sturm

We consider elliptic second order partial differential operators with Lipschitz continuous leading order coefficients on finite cubes and the whole Euclidean space. We prove quantitative sampling and equidistribution theorems for…

Analysis of PDEs · Mathematics 2025-05-23 Martin Tautenhahn , Ivan Veselic

We prove some asymptotic results for the radius and the profile of large random bipartite planar maps. Using a bijection due to Bouttier, Di Francesco and Guitter between rooted bipartite planar maps and certain two-type trees with positive…

Probability · Mathematics 2007-05-23 Mathilde Weill

The effectiveness of dimensionality reduction with quadratic manifolds hinges on the choice of a reduced basis and the associated quadratic correction terms. Existing approaches typically rely on subspaces spanned by the leading principal…

Numerical Analysis · Mathematics 2026-05-27 Gavin Paxton , Seunghee Cheon , Rudy Geelen , Shane A. McQuarrie

Consider a branching random walk evolving in a macroscopic time-inhomogeneous environment, that scales with the length $n$ of the process under study. We compute the first two terms of the asymptotic of the maximal displacement at time $n$.…

Probability · Mathematics 2018-10-01 Bastien Mallein

We study the random-link matching problem on random regular graphs, alongside with two relaxed versions of the problem, namely the fractional matching and the so-called "loopy" fractional matching. We estimated the asymptotic average…

Disordered Systems and Neural Networks · Physics 2020-03-16 Giorgio Parisi , Gianmarco Perrupato , Gabriele Sicuro

The 2-Wasserstein distance (or RMS distance) is a useful measure of similarity between probability distributions that has exciting applications in machine learning. For discrete distributions, the problem of computing this distance can be…

Computational Geometry · Computer Science 2020-07-17 Nathaniel Lahn , Sharath Raghvendra

We study the properties of the set of marginal distributions of infinite translation-invariant systems in the 2D square lattice. In cases where the local variables can only take a small number $d$ of possible values, we completely solve the…

Mathematical Physics · Physics 2018-09-26 Zizhu Wang , Miguel Navascués

We present a unified duality approach to Bayesian persuasion. The optimal dual variable, interpreted as a price function on the state space, is shown to be a supergradient of the concave closure of the objective function at the prior…

Theoretical Economics · Economics 2024-06-05 Piotr Dworczak , Anton Kolotilin

Let $\mathcal{M}$ be a smooth submanifold of $\mathbb{R}^n$ equipped with the Euclidean (chordal) metric. This note considers the smallest dimension $m$ for which there exists a bi-Lipschitz function $f: \mathcal{M} \mapsto \mathbb{R}^m$…

Numerical Analysis · Mathematics 2021-05-31 Mark Iwen , Arman Tavakoli , Benjamin Schmidt

It is well known that optimal transport suffers from the curse of dimensionality: when the prescribed marginals are approximated by i.i.d. samples, the convergence of the empirical optimal transport problem to the population counterpart…

Statistics Theory · Mathematics 2025-11-14 Alberto González-Sanz , Eustasio del Barrio , Marcel Nutz

We consider a family of domains $(\Omega_N)_{N>0}$ obtained by attaching an $N\times 1$ rectangle to a fixed set $\Omega_0 = \{(x,y): 0<y<1, -\phi(y)<x<0\}$, for a Lipschitz function $\phi\geq 0$. We derive full asymptotic expansions, as…

Spectral Theory · Mathematics 2007-10-22 Daniel Grieser , David Jerison

We study optimal transport between two high-dimensional distributions $\mu,\nu$ in $R^n$ from an algorithmic perspective: given $x \sim \mu$, find a close $y \sim \nu$ in $poly(n)$ time, where $n$ is the dimension of $x,y$. Thus, running…

Data Structures and Algorithms · Computer Science 2025-09-29 Salman Beigi , Omid Etesami , Mohammad Mahmoody , Amir Najafi

We present the first nontrivial approximation algorithm for the bottleneck asymmetric traveling salesman problem. Given an asymmetric metric cost between n vertices, the problem is to find a Hamiltonian cycle that minimizes its bottleneck…

Data Structures and Algorithms · Computer Science 2020-12-29 Hyung-Chan An , Robert Kleinberg , David B. Shmoys

Random geometric graphs are random graph models defined on metric measure spaces. A random geometric graph is generated by first sampling points from a metric space and then connecting each pair of sampled points independently with a…

Probability · Mathematics 2025-11-10 Han Huang , Pakawut Jiradilok , Elchanan Mossel

We study a natural analogue of Ulam's problem for random rooted trees distributed according to a Plancherel-type measure. This probability measure is closely related to the classical Plancherel measure on integer partitions. For a…

Probability · Mathematics 2026-04-29 Shengjun Zhang

In the late eighties John Kingman studied the problem of maxima of a quadratic form, with independent, uniformly distributed, coefficients, on a simplex of growing dimension $n$. In particular, he proved that the largest support size…

Probability · Mathematics 2019-07-10 Boris Pittel

A quadratic optimal transport metric on the set of probability measure over $\R^2$ is introduced. The quadratic cost is given by the euclidean norm on $\R^2$ associated to some well chosen symmetric positive matrix, which makes the metric…

Analysis of PDEs · Mathematics 2021-02-23 Samir Salem

We consider a random walk on a homogeneous space $G/\Lambda$ where $G$ is a non-compact simple Lie group and $\Lambda$ is a lattice. The walk is driven by a probability measure $\mu$ on $G$ whose support generates a Zariski-dense subgroup.…

Dynamical Systems · Mathematics 2026-05-27 Timothée Bénard , Weikun He

We study alternating minimization for matrix completion in the simplest possible setting: completing a rank-one matrix from a revealed subset of the entries. We bound the asymptotic convergence rate by the variational characterization of…

Machine Learning · Computer Science 2020-08-13 Rui Liu , Alex Olshevsky