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Given a strictly convex multiobjective optimization problem with objective functions $f_1,\dots,f_N$, let us denote by $x_0$ its solution, obtained as minimum point of the linear scalarized problem, where the objective function is the…

Optimization and Control · Mathematics 2023-03-06 Carlo Alberto De Bernardi , Enrico Miglierina , Elena Molho , Jacopo Somaglia

If four people with Gaussian-distributed heights stand at Gaussian positions on the plane, the probability that there are exactly two people whose height is above the average of the four is exactly the same as the probability that they…

Probability · Mathematics 2024-07-04 Florian Frick , Andrew Newman , Wesley Pegden

We study $\mathbb{R}^2\oplus\mathbb{R}$-separately convex hulls of finite sets of points in $\mathbb{R}^3$, as in KirchheimMullerSverak2003. This notion of convexity, which we call $2+1$ convexity, corresponds to rank-one convex convexity,…

Analysis of PDEs · Mathematics 2022-09-30 Pablo Angulo , Carlos García-Gutiérrez

For a given complex finite dimensional subspace $S$ of $\mathbb{C}^n$ and a fixed basis, we study the compact and convex subset of $\left(\mathbb{R}_{\geq 0}\right)^n$ that we call the moment of $S$ $m_S=$ convex hull…

Functional Analysis · Mathematics 2021-10-22 Abel Klobouk , Alejandro Varela

Consider a finite system of non-strict polynomial inequalities with solution set $S\subseteq\mathbb R^n$. Its Lasserre relaxation of degree $d$ is a certain natural linear matrix inequality in the original variables and one additional…

Algebraic Geometry · Mathematics 2018-11-30 Tom-Lukas Kriel , Markus Schweighofer

In this short note, we prove a version of the Johnson-Lindenstrauss flattening Lemma for point sets taking values in discrete subgroups. More precisely, given $d,\lambda_0,N_0\in\mathbb{N}$ and $\epsilon\in \left(0,\frac{1}{2}\right)$…

Metric Geometry · Mathematics 2025-01-22 Rodolfo Viera

The Reifenberg theorem \cite{reif_orig} tells us that if a set $S\subseteq B_2\subseteq \mathbb R^n$ is uniformly close on all points and scales to a $k$-dimensional subspace, then $S$ is H\"older homeomorphic to a $k$-dimensional Euclidean…

Analysis of PDEs · Mathematics 2024-05-07 Nicholas Edelen , Aaron Naber , Daniele Valtorta

For a probability measure on a real separable Hilbert space, we are interested in "volume-based" approximations of the d-dimensional least squares error of it, i.e., least squares error with respect to a best fit d-dimensional affine…

Functional Analysis · Mathematics 2012-10-08 Gilad Lerman , J. Tyler Whitehouse

In this paper we study the Buchstaber invariant of simplicial complexes, which comes from toric topology. With each simplicial complex $K$ on $m$ vertices we can associate a moment-angle complex $\mathcal Z_K$ with a canonical action of the…

Algebraic Topology · Mathematics 2012-12-18 Nickolai Erokhovets

Assume that we observe i.i.d.~points lying close to some unknown $d$-dimensional $\mathcal{C}^k$ submanifold $M$ in a possibly high-dimensional space. We study the problem of reconstructing the probability distribution generating the…

Statistics Theory · Mathematics 2022-02-15 Vincent Divol

For a Minkowski centered convex compact set $K$ we define $\alpha(K)$ to be the smallest possible factor to cover $K \cap (-K)$ by a rescalation of $\mathrm{conv} (K\cup (-K))$ and give a complete description of the possible values of…

Metric Geometry · Mathematics 2024-01-29 René Brandenberg , Katherina von Dichter , Bernardo González Merino

The variance conjecture in Asymptotic Convex Geometry stipulates that the Euclidean norm of a random vector uniformly distributed in a (properly normalised) high-dimensional convex body $K\subset {\mathbb R}^n$ satisfies a Poincar\'e-type…

Functional Analysis · Mathematics 2018-05-09 Beatrice-Helen Vritsiou

We prove an explicit combinatorial formula for the expected number of faces of the zero polytope of the homogeneous and isotropic Poisson hyperplane tessellation in $\mathbb R^d$. The expected $f$-vector is expressed through the…

Probability · Mathematics 2020-08-18 Zakhar Kabluchko

We develop a technique that can be applied to provide improved upper bounds for two important questions in linear integer optimization. - Proximity bounds: Given an optimal vertex solution for the linear relaxation, how far away is the…

Optimization and Control · Mathematics 2022-11-29 Marcel Celaya , Stefan Kuhlmann , Joseph Paat , Robert Weismantel

Some geometric facts concerning sets with positive reach in the Euclidean $d$-dimensional space ${\bf E}^d$ are proved. For $x_1$ and $x_2$ in ${\bf E}^d$ and $R>0$ let us denote by ${\mathfrak H}(x_1,x_2,R)$ the intersection of all closed…

Metric Geometry · Mathematics 2007-05-23 Andrea Colesanti , Paolo Manselli

Let $V$ be any vector space of multivariate degree-$d$ homogeneous polynomials with co-dimension at most $k$, and $S$ be the set of points where all polynomials in $V$ {\em nearly} vanish. We establish a qualitatively optimal upper bound on…

Machine Learning · Computer Science 2020-12-15 Ilias Diakonikolas , Daniel M. Kane

We study approximations of smooth convex bodies by random ball-polytopes. We examine the following probability model: let $K\subset{\bf R}^d$ be a convex body such that $K$ slides freely in a ball of radius $R>0$ and has $C^2$ smooth…

Metric Geometry · Mathematics 2020-08-07 Ferenc Fodor

The Weak Gravity Conjecture has recently been re-formulated in terms of a particle with non-negative self-binding energy. Because of the dual conformal field theory (CFT) formulation in the anti-de Sitter space the conformal dimension…

High Energy Physics - Theory · Physics 2022-01-19 Oleg Antipin , Jahmall Bersini , Francesco Sannino , Zhi-Wei Wang , Chen Zhang

We propose a notion of depth with respect to a finite family $\mathcal{F}$ of convex sets in $\mathbb{R}^d$ which we call $\text{dep}_\mathcal{F}$. We begin showing that $\text{dep}_\mathcal{F}$ satisfies some expected properties for a…

Combinatorics · Mathematics 2016-12-13 Leonardo Martínez-Sandoval , Roee Tamam

In the subspace approximation problem, we seek a k-dimensional subspace F of R^d that minimizes the sum of p-th powers of Euclidean distances to a given set of n points a_1, ..., a_n in R^d, for p >= 1. More generally than minimizing sum_i…

Data Structures and Algorithms · Computer Science 2015-10-22 Kenneth L. Clarkson , David P. Woodruff