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In this paper we provide improved running times and oracle complexities for approximately minimizing a submodular function. Our main result is a randomized algorithm, which given any submodular function defined on $n$-elements with range…

Data Structures and Algorithms · Computer Science 2019-09-11 Brian Axelrod , Yang P. Liu , Aaron Sidford

The problem of maximizing nonnegative monotone submodular functions under a certain constraint has been intensively studied in the last decade, and a wide range of efficient approximation algorithms have been developed for this problem.…

Data Structures and Algorithms · Computer Science 2020-06-30 Akbar Rafiey , Yuichi Yoshida

Generic approximation of entire functions by their Pad\'{e} approximants has been achieved in the past (\cite{3}). In the present article we obtain generic approximation of holomorphic functions on arbitrary open sets by sequences of their…

Complex Variables · Mathematics 2011-06-02 G. Fournodavlos , V. Nestoridis

We consider the Gaussian approximation for functionals of a Poisson process that are expressible as sums of region-stabilizing (determined by the points of the process within some specified regions) score functions and provide a bound on…

Probability · Mathematics 2022-09-20 Chinmoy Bhattacharjee , Ilya Molchanov

We prove that if every bounded linear operator (or $N$-homogeneous polynomials) with the compact approximation property attains its numerical radius, then $X$ is a finite dimensional space. Moreover, we present an improvement of the…

Functional Analysis · Mathematics 2022-10-05 Mingu Jung

We characterize the extreme points of multidimensional monotone functions from $[0,1]^n$ to $[0,1]$, as well as the extreme points of the set of one-dimensional marginals of these functions. These characterizations lead to new results for…

Theoretical Economics · Economics 2025-08-29 Frank Yang , Kai Hao Yang

We show that there is a system of 14 non-trivial finitary functions on the random graph with the following properties: Any non-trivial function on the random graph generates one of the functions of this system by means of composition with…

Logic · Mathematics 2012-12-05 Manuel Bodirsky , Michael Pinsker

Our object of study is extremal functions which are defined by distance functions of convex bodies. These functions take values in the moduli spaces of algebraic and geometric objects associated with these ${\mathbb Z}$-modules (geometric…

Number Theory · Mathematics 2024-12-24 Nikolaj Glazunov

We provide relaxation for not lower semicontinuous supremal functionals of the type $W^{1,\infty}(\Omega;\mathbb R^d) \ni u \mapsto\supess_{ x \in \Omega}f(\nabla u(x))$ in the vectorial case, where $\Omega\subset \mathbb R^N$ is a…

Optimization and Control · Mathematics 2019-09-26 Francesca Prinari , Elvira Zappale

In this paper, moderate deviations for normal approximation of functionals over infinitely many Rademacher random variables are derived. They are based on a bound for the Kolmogorov distance between a general Rademacher functional and a…

Probability · Mathematics 2024-06-12 Marius Butzek , Peter Eichelsbacher , Benedikt Rednoß

We obtain approximation results for general positive linear operators satisfying mild conditions, when acting on discontinuous functions and absolutely continuous functions having discontinuous derivatives. The upper bounds, given in terms…

Probability · Mathematics 2024-10-29 José A. Adell , P. Garrancho , F. J. Martínez-Sánchez

Bernstein's theorem (also called Hausdorff--Bernstein--Widder theorem) enables the integral representation of a completely monotonic function. We introduce a finite completely monotonic function, which is a completely monotonic function…

Numerical Analysis · Mathematics 2023-07-25 Yohei M. Koyama

This article establishes a complete approximate axiomatization for the real-closed field $\mathbb{R}$ expanded with all differentially-defined functions, including special functions such as $\sin(x), \cos(x), e^x, \dots$. Every true…

Logic in Computer Science · Computer Science 2025-06-11 André Platzer , Long Qian

We prove some results on when functions on compact sets $K \subset \mathbb C$ can be approximated by polynomials avoiding values in given sets. We also prove some higher dimensional analogues. In particular we prove that a continuous…

Classical Analysis and ODEs · Mathematics 2021-08-17 Johan Andersson

We extend the work of Narasimhan and Bilmes [30] for minimizing set functions representable as a difference between submodular functions. Similar to [30], our new algorithms are guaranteed to monotonically reduce the objective function at…

Data Structures and Algorithms · Computer Science 2013-08-27 Rishabh Iyer , Jeff Bilmes

This paper is concerned with minimizing a sum of rational functions over a compact set of high-dimension. Our approach relies on the second Lasserre's hierarchy (also known as the upper bounds hierarchy) formulated on the pushforward…

Optimization and Control · Mathematics 2020-12-11 Jean Bernard Lasserre , Victor Magron , Swann Marx , Olivier Zahm

In this paper we present results on asymptotic characteristics of multivariate function classes in the uniform norm. Our main interest is the approximation of functions with mixed smoothness parameter not larger than $1/2$. Our focus will…

Functional Analysis · Mathematics 2021-11-01 Vladimir Temlyakov , Tino Ullrich

This paper presents the Gaussian subordination framework to generate optimal one-sided approximations to multidimensional real-valued functions by functions of prescribed exponential type. Such extremal problems date back to the works of…

Classical Analysis and ODEs · Mathematics 2021-09-30 Emanuel Carneiro , Friedrich Littmann

It is known that a subharmonic function of finite order $\rho$ can be approximated by the logarithm of the modulus of an entire function at the point $z$ outside an exceptional set up to $C\log|z|$. In this article we prove that if such an…

Complex Variables · Mathematics 2007-10-03 Markiyan Hirnyk

The study of universal approximation of arbitrary functions $f: \mathcal{X} \to \mathcal{Y}$ by neural networks has a rich and thorough history dating back to Kolmogorov (1957). In the case of learning finite dimensional maps, many authors…

Machine Learning · Computer Science 2019-10-04 William H. Guss , Ruslan Salakhutdinov