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We show that the greedy algorithm for adaptive-submodular cover has approximation ratio at least 1.3*(1+ln Q). Moreover, the instance demonstrating this gap has Q=1. So, it invalidates a prior result in the paper ``Adaptive Submodularity: A…

Data Structures and Algorithms · Computer Science 2024-05-27 Blake Harris , Viswanath Nagarajan

The problem of minimization of a quadratic functional depending on great number of binary variables is examined. 3 variants of minimization procedure are studied with the aid of computer simulation for spin-glass matrices. It is shown that…

Disordered Systems and Neural Networks · Physics 2009-07-16 Leonid B. Litinskii

We construct for each $0<p\le 1$ an infinite collection of subspaces of $\ell_p$ that extend the example from [J. Lindenstrauss, On a certain subspace of $\ell_{1}$, Bull. Acad. Polon. Sci. S\'er. Sci. Math. Astronom. Phys. 12 (1964),…

Functional Analysis · Mathematics 2019-12-19 Fernando Albiac , José L. Ansorena , Przemysław Wojtaszczyk

We investigate the performance of a deterministic GREEDY algorithm for the problem of maximizing functions under a partition matroid constraint. We consider non-monotone submodular functions and monotone subadditive functions. Even though…

Discrete Mathematics · Computer Science 2019-02-22 Tobias Friedrich , Andreas Göbel , Frank Neumann , Francesco Quinzan , Ralf Rothenberger

In this work, we consider the maximization of submodular functions constrained by independence systems. Because of the wide applicability of submodular functions, this problem has been extensively studied in the literature, on specialized…

Data Structures and Algorithms · Computer Science 2019-06-11 Alan Kuhnle

We show that any subset of the natural numbers with positive logarithmic Banach density contains a set that is within a factor of two of a geometric progression, improving the bound on a previous result of the authors. Density conditions on…

Combinatorics · Mathematics 2016-10-24 Mauro Di Nasso , Isaac Goldbring , Renling Jin , Steven Leth , Martino Lupini , Karl Mahlburg

Set Cover is a classic NP-hard problem; as shown by Slav\'{i}k (1997) the greedy algorithm gives an approximation ratio of $\ln n - \ln \ln n + \Theta(1)$. A series of works by Lund \& Yannakakis (1994), Feige (1998), Moshkovitz (2015) have…

Data Structures and Algorithms · Computer Science 2017-02-17 David G. Harris

We show the existence of a deep neural network capable of approximating a wide class of high-dimensional approximations. The construction of the proposed neural network is based on a quasi-optimal polynomial approximation. We show that this…

Numerical Analysis · Mathematics 2019-12-09 Joseph Daws , Clayton Webster

We present a general approximation framework for weighted integer covering problems. In a weighted integer covering problem, the goal is to determine a non-negative integer solution $x$ to system $\{ Ax \geq r \}$ minimizing a non-negative…

Discrete Mathematics · Computer Science 2017-04-28 Britta Peis , José Verschae , Andreas Wierz

This paper studies generic and perturbation properties inside the linear space of $m\times (m+n)$ polynomial matrices whose rows have degrees bounded by a given list $d_1, \ldots, d_m$ of natural numbers, which in the particular case $d_1 =…

Numerical Analysis · Mathematics 2017-12-12 Froilán M. Dopico , Paul Van Dooren

In this paper, we establish new advances in the theory started by T. Oikhberg in [15] where the author joins greedy approximation theory with the use of sequences with gaps. Concretely, we address and partially answer three open questions…

Functional Analysis · Mathematics 2022-10-04 Miguel Berasategui , Pablo M. Berná

We study greedy approximation in uniformly smooth Banach spaces. The Weak Chebyshev Greedy Algorithm (WCGA) is defined for any Banach space $X$ and a dictionary $\mathcal{D}$, and provides nonlinear $n$-term approximation with respect to…

Numerical Analysis · Mathematics 2021-06-07 A. V. Dereventsov

$\newcommand{\eps}{\varepsilon}$ In this paper, we consider two important problems defined on finite metric spaces, and provide efficient new algorithms and approximation schemes for these problems on inputs given as graph shortest path…

Computational Geometry · Computer Science 2021-02-23 David Eppstein , Sariel Har-Peled , Anastasios Sidiropoulos

In this note we show that a recent existence result on quasiequilibrium problems, which seems to improve deeply some well-known results, is not correct. We exhibit a counterexample and we furnish a generalization of a lemma about continuous…

Optimization and Control · Mathematics 2015-04-08 Marco Castellani , Massimiliano Giuli

Given a frequency $\lambda=(\lambda_n)$, we consider the Hardy spaces $ \mathcal{H}_p^\lambda$ of $\lambda$-Dirichlet series $ D = \sum_n a_n e^{-\lambda_n s}$ and study the asymptotic behavior of the upper and lower democracy functions of…

Functional Analysis · Mathematics 2024-06-07 Daniel Carando , Silvia Lassalle , Leandro Milne

A wide range of numerical methods exists for computing polynomial approximations of solutions of ordinary differential equations based on Chebyshev series expansions or Chebyshev interpolation polynomials. We consider the application of…

Symbolic Computation · Computer Science 2014-07-11 Alexandre Benoit , Mioara Joldes , Marc Mezzarobba

We study the Banach space $D([0,1]^m)$ of functions of several variables that are (in a certain sense) right-continuous with left limits, and extend several results previously known for the standard case $m=1$. We give, for example, a…

Probability · Mathematics 2020-04-02 Svante Janson

A generalization of Problem 73 of Mazur and Orlicz in the Scottish Book was introduced from L. Harris. The exact value of the constant that appears there is known when complex normed linear spaces are considered. In this paper, we give…

Functional Analysis · Mathematics 2012-10-30 M. K. Papadiamantis

Ensembles of independently trained neural networks are a state-of-the-art approach to estimate predictive uncertainty in Deep Learning, and can be interpreted as an approximation of the posterior distribution via a mixture of delta…

Machine Learning · Computer Science 2022-07-11 Aleksei Tiulpin , Matthew B. Blaschko

Bayesian Reinforcement Learning (RL) is capable of not only incorporating domain knowledge, but also solving the exploration-exploitation dilemma in a natural way. As Bayesian RL is intractable except for special cases, previous work has…

Artificial Intelligence · Computer Science 2013-06-14 Kenji Kawaguchi , Mauricio Araya