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This article gives estimates on covering numbers and diameters of random proportional sections and projections of symmetric quasi-convex bodies in $\mathbb R$. These results were known for the convex case and played an essential role in…

Metric Geometry · Mathematics 2008-02-03 A. E. Litvak , V. D. Milman , A. Pajor

The present work surveys problems in $n$-dimensional space with $n$ large. Early development in the study of packing and covering in high dimensions was motivated by the geometry of numbers. Subsequent results, such as the discovery of the…

Metric Geometry · Mathematics 2022-02-24 Gábor Fejes Tóth

Consider a sum of convex functions, where the only information known about each individual summand is the location of a minimizer. In this work, we give an exact characterization of the set of possible minimizers of the sum. Our results…

Optimization and Control · Mathematics 2024-03-11 Moslem Zamani , François Glineur , Julien M. Hendrickx

We study some properties convex functions fulfill. Among the conclusions we obtain from such result, we are able to prove some nontrivial inequalities among real numbers, and we give an improvement of the reverse triangle inequality in the…

We establish the existence of a regular functional $M$-position, in the sense of Pisier, for geometric log-concave functions. This provides a functional analogue of Pisier's regular $M$-positions for convex bodies and yields uniform control…

Metric Geometry · Mathematics 2026-03-03 Apostolos Giannopoulos , Natalia Tziotziou

This paper presents a lower bound for optimizing a finite sum of $n$ functions, where each function is $L$-smooth and the sum is $\mu$-strongly convex. We show that no algorithm can reach an error $\epsilon$ in minimizing all functions from…

Machine Learning · Statistics 2015-10-06 Alekh Agarwal , Leon Bottou

We study the $L^p$ mapping properties of the strong spherical maximal function, which is a multiparameter generalisation of Stein's spherical maximal function. We show that this operator is bounded on $L^p$ for $p > 2$ in all dimensions $n…

Classical Analysis and ODEs · Mathematics 2025-02-06 Jonathan Hickman , Joshua Zahl

This paper determines the optimal upper bound for the simultaneous packing and covering constants of the two-dimensional centrally symmetric convex domains. It solved a problem opening for more than thirty years.

Metric Geometry · Mathematics 2007-06-14 Chuanming Zong

We discuss the optimal matching solution for both the assignment problem and the matching problem in one dimension for a large class of convex cost functions. We consider the problem in a compact set with the topology both of the interval…

Disordered Systems and Neural Networks · Physics 2017-10-11 Sergio Caracciolo , Matteo D'Achille , Gabriele Sicuro

In the maximum coverage problem, we are given subsets $T_1, \ldots, T_m$ of a universe $[n]$ along with an integer $k$ and the objective is to find a subset $S \subseteq [m]$ of size $k$ that maximizes $C(S) := \Big|\bigcup_{i \in S}…

Data Structures and Algorithms · Computer Science 2021-05-04 Siddharth Barman , Omar Fawzi , Paul Fermé

We establish upper and lower bounds for the metric entropy and bracketing entropy of the class of $d$-dimensional bounded monotonic functions under $L^p$ norms. It is interesting to see that both the metric entropy and bracketing entropy…

Statistics Theory · Mathematics 2007-06-13 Fuchang Gao , Jon A. Wellner

Let $H$ be a Hilbert space. For a closed convex body $A$ denote by $r(A)$ the supremum of radiuses of balls, contained in $A$. We prove, that $\sum_{n=1}^\infty r(A_n) \ge r(A)$ for every covering of a convex closed body $A \subset H$ by a…

Functional Analysis · Mathematics 2007-05-23 Vladimir Kadets

Let $\pi$ be a cuspidal automorphic representation of a general linear group over the rational numbers. We establish a subconvex bound for the standard $L$-function of $\pi$ in the $t$-aspect. More generally, we address the spectral aspect…

Number Theory · Mathematics 2023-01-25 Paul D. Nelson

This paper presents a study of generalized polyhedral convexity under basic operations on multifunctions. We address the preservation of generalized polyhedral convexity under sums and compositions of multifunctions, the domains and ranges…

Optimization and Control · Mathematics 2023-10-19 Nguyen Ngoc Luan , Nguyen Mau Nam , Nguyen Dong Yen

Given a convex set $\Omega$ of $\mathbb{R}^n$, we consider the shape optimization problem of finding a convex subset $\omega\subset \Omega$, of a given measure, minimizing the $p$-distance functional $$\mathcal{J}_p(\omega) :=…

Optimization and Control · Mathematics 2025-01-03 Zakaria Fattah , Ilias Ftouhi , Enrique Zuazua

In this paper, we prove necessary and sufficient conditions for a sense-preserving harmonic function to be absolutely convex in the open unit disk. We also estimate the coefficient bound and obtain growth, covering and area theorems for…

Complex Variables · Mathematics 2016-05-10 Saminathan Ponnusamy , Anbareeswaran Sairam Kaliraj , Victor V. Starkov

We investigate two new optimization problems -- minimizing a submodular function subject to a submodular lower bound constraint (submodular cover) and maximizing a submodular function subject to a submodular upper bound constraint…

Data Structures and Algorithms · Computer Science 2013-11-12 Rishabh Iyer , Jeff Bilmes

Given a function $f$ defined on a nonempty and convex subset of the $d$-dimensional Euclidean space, we prove that if $f$ is bounded from below and it satisfies a convexity-type functional inequality with infinite convex combinations, then…

Classical Analysis and ODEs · Mathematics 2025-09-16 Matyas Barczy , Zsolt Páles

For any $p\in[1,\infty]$, we prove that the set of simple functions taking at most $k$ different values is proximinal in $L^p$ for all $k\geq 1$. We introduce the class of uniformly approximable subsets of $L^p$, which is larger than the…

Classical Analysis and ODEs · Mathematics 2022-09-07 Guillaume Grelier , Jaime San Martín

Submodular function minimization is well studied, and existing algorithms solve it exactly or up to arbitrary accuracy. However, in many applications, such as structured sparse learning or batch Bayesian optimization, the objective function…

Machine Learning · Computer Science 2022-03-10 Marwa El Halabi , Stefanie Jegelka
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