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In connection to two recent publications of ours in Arch. Math. Basel (2021) and Acta Math. Hung. (2022), respectively, and in regard to the results obtained in Arch. Math. Basel (2012), we have the motivation to study the near property of…

Group Theory · Mathematics 2023-09-25 Peter Danchev , Brendan Goldsmith

This paper extends the Lebesgue property and (weak) $G$-completeness to generalized quasi-uniform spaces. It investigates the connections between completeness, (weak) $G$-completeness, and the Lebesgue property of the product of generalized…

General Topology · Mathematics 2023-11-07 Sugata Adhya , A. Deb Ray

We propose a greedy algorithm to select $N$ important features among $P$ input features for a non-linear prediction problem. The features are selected one by one sequentially, in an iterative loss minimization procedure. We use neural…

Machine Learning · Computer Science 2023-09-14 Sandipan Das , Alireza M. Javid , Prakash Borpatra Gohain , Yonina C. Eldar , Saikat Chatterjee

The total effective resistance, also called the Kirchhoff index, provides a robustness measure for a graph $G$. We consider two optimization problems of adding $k$ new edges to $G$ such that the resulting graph has minimal total effective…

Social and Information Networks · Computer Science 2023-09-18 Maria Predari , Lukas Berner , Robert Kooij , Henning Meyerhenke

The greedy defining sets of graphs were appeared first time in [M. Zaker, Greedy defining sets of graphs, Australas. J. Combin, 2001]. We show that to determine the greedy defining number of bipartite graphs is an NP-complete problem. This…

Combinatorics · Mathematics 2008-11-05 Manouchehr Zaker

We analyze greedy algorithms for the Hierarchical Aggregation (HAG) problem, a strategy introduced in [Jia et al., KDD 2020] for speeding up learning on Graph Neural Networks (GNNs). The idea of HAG is to identify and remove redundancies in…

Data Structures and Algorithms · Computer Science 2021-02-09 Alexandra Porter , Mary Wootters

Given a finite set of bases $b_1$, $b_2$, \dots, $b_r$ (integers greater than $1$), a multi-base representation of an integer~$n$ is a sum with summands $db_1^{\alpha_1}b_2^{\alpha_2} \cdots b_r^{\alpha_r}$, where the $\alpha_j$ are…

Number Theory · Mathematics 2019-07-15 Daniel Krenn , Vorapong Suppakitpaisarn , Stephan Wagner

It is a survey on recent results in constructive sparse approximation. Three directions are discussed here: (1) Lebesgue-type inequalities for greedy algorithms with respect to a special class of dictionaries, (2) constructive sparse…

Numerical Analysis · Mathematics 2015-11-06 Vladimir Temlyakov

This paper establishes alternative characterizations of very expressive classes of existential rule sets with decidable query entailment. We consider the notable class of greedy bounded-treewidth sets (gbts) and a new, generalized variant,…

Logic in Computer Science · Computer Science 2023-07-19 Tim S. Lyon , Sebastian Rudolph

This paper establishes a connection between a problem in Potential Theory and Mathematical Physics, arranging points so as to minimize an energy functional, and a problem in Combinatorics and Number Theory, constructing 'well-distributed'…

Mathematical Physics · Physics 2020-01-27 Florian Pausinger

An $\alpha$-greedy balanced pair in an ordered set $P=(V,\leq)$ is a pair $(x,y)$ of elements of $V$ such that the proportion of greedy linear extensions of $P$ that put $x$ before $y$ among all greedy linear extensions is in the real…

Combinatorics · Mathematics 2020-10-13 Imed Zaguia

We present a simple greedy procedure to compute an $(\alpha,\beta)$-spanner for a graph $G$. We then show that this procedure is useful for building fault-tolerant spanners, as well as spanners for weighted graphs. Our first main result is…

Data Structures and Algorithms · Computer Science 2026-03-19 Elizaveta Popova , Elad Tzalik

We investigate the genus $g(n,m)$ of the Erd\H{o}s-R\'enyi random graph $G(n,m)$, providing a thorough description of how this relates to the function $m=m(n)$, and finding that there is different behaviour depending on which `region' $m$…

Combinatorics · Mathematics 2019-03-28 Chris Dowden , Mihyun Kang , Michael Krivelevich

We prove thatthe Banach space $(\oplus_{n=1}^\infty \ell_p^n)_{\ell_q}$, which is isomorphic to certain Besov spaces, has a greedy basis whenever $1\leq p \leq\infty$ and $1<q<\infty$. Furthermore, the Banach spaces $(\oplus_{n=1}^\infty…

Functional Analysis · Mathematics 2009-10-21 S. J. Dilworth , D. Freeman , E. Odell , Th. Schlumprecht

Given an initial point $x_0 \in \mathbb{R}^d$ and a sequence of vectors $v_1, v_2, \dots$ in $\mathbb{R}^d$, we define a greedy sequence by setting $x_{n} = x_{n-1} \pm v_n$ where the sign is chosen so as to minimize $\|x_n\|$. We prove…

Probability · Mathematics 2024-12-06 Alex Albors , François Clément , Shosuke Kiami , Braeden Sodt , Ding Yifan , Tony Zeng

The aim of this paper is to develop greedy algorithms which generate uniformly distributed sequences in the $d$-dimensional unit cube $[0,1]^d$. The figures of merit are three different variants of $L_2$ discrepancy. Theoretical results…

Number Theory · Mathematics 2022-10-19 Ralph Kritzinger

Recent work on exploration in reinforcement learning (RL) has led to a series of increasingly complex solutions to the problem. This increase in complexity often comes at the expense of generality. Recent empirical studies suggest that,…

Machine Learning · Computer Science 2020-06-03 Will Dabney , Georg Ostrovski , André Barreto

We give a complete characterization of $2\pi$-periodic weights $w$ for which the usual trigonometric system forms a quasi-greedy basis for $L^p(\bT;w)$, i.e., bases for which simple thresholding approximants converge in norm. The…

Functional Analysis · Mathematics 2007-05-23 Morten Nielsen

In many prediction problems, it is not uncommon that the number of variables used to construct a forecast is of the same order of magnitude as the sample size, if not larger. We then face the problem of constructing a prediction in the…

Statistics Theory · Mathematics 2016-02-08 Alessio Sancetta

Submodular functions -- functions exhibiting diminishing returns -- are central to machine learning. When the objective is monotone and non-negative, the greedy algorithm achieves a tight $63\%$ approximation. But many practical objectives…

Machine Learning · Computer Science 2026-05-11 Yixin Chen , Alan Kuhnle