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We consider mesh functions which are discrete convex in the sense that their central second order directional derivatives are positive. Analogous to the case of a uniformly bounded sequence of convex functions, we prove that the uniform…

Numerical Analysis · Mathematics 2019-11-01 Gerard Awanou

The k-core of a graph is its maximal subgraph with minimum degree at least k, and the core value of a vertex u is the largest k for which u is contained in the k-core of the graph. Among cohesive subgraphs, k-core and its variants have…

Data Structures and Algorithms · Computer Science 2025-10-14 Yan S. Couto , Cristina G. Fernandes

A proportionally dense subgraph (PDS) of a graph is an induced subgraph of size at least two such that every vertex in the subgraph has proportionally as many neighbors inside as outside of the subgraph. Then, maxPDS is the problem of…

Computational Complexity · Computer Science 2024-06-03 Narmina Baghirova , Antoine Castillon

To characterize the location (mean, median) of a set of graphs, one needs a notion of centrality that is adapted to metric spaces, since graph sets are not Euclidean spaces. A standard approach is to consider the Frechet mean. In this work,…

Machine Learning · Statistics 2022-01-19 Daniel Ferguson , Francois G. Meyer

We propose a variable metric framework for minimizing the sum of a self-concordant function and a possibly non-smooth convex function, endowed with an easily computable proximal operator. We theoretically establish the convergence of our…

Machine Learning · Statistics 2014-04-15 Quoc Tran-Dinh , Anastasios Kyrillidis , Volkan Cevher

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

Let $G=(V,E,w)$ be a finite, connected graph with weighted edges. We are interested in the problem of finding a subset $W \subset V$ of vertices and weights $a_w$ such that $$ \frac{1}{|V|}\sum_{v \in V}^{}{f(v)} \sim \sum_{w \in W}{a_w…

Statistics Theory · Mathematics 2018-03-20 George C. Linderman , Stefan Steinerberger

In this paper we characterize a mathematical model called Maximum Common Subelement (MCS) Model and prove the existence of four different metrics on such model. We generalize metrics on graphs previously proposed in the literature and…

Discrete Mathematics · Computer Science 2015-01-28 Lauro Lins , Nivan Ferreira , Juliana Freire , Claudio Silva

By considering a certain univalent function in the open unit disk U, that maps U onto a strip domain, we introduce a new class of analytic and close-to-convex functions by means of a certain non-homogeneous Cauchy-Euler-type differential…

Complex Variables · Mathematics 2019-02-19 Serap Bulut

A vertex $w$ resolves two vertices $u$ and $v$ in a directed graph $G$ if the distance from $w$ to $u$ is different to the distance from $w$ to $v$. A set of vertices $R$ is a resolving set for a directed graph $G$ if for every pair of…

Computational Complexity · Computer Science 2023-06-16 Yannick Schmitz , Egon Wanke

Let $d \geq 2$. The Cheeger constant of a graph is the minimum surface-to-volume ratio of all subsets of the vertex set with relative volume at most 1/2. There are several ways to define surface and volume here: the simplest method is to…

Probability · Mathematics 2018-05-23 Tobias Müller , Mathew D. Penrose

Quasi-isometries are mappings on graphs, with distance-distortions parameterized by a multiplicative factor and an additive constant. The distance-distortions of quasi-isometries are in a general form that captures a wide range of…

Data Structures and Algorithms · Computer Science 2022-08-22 Khí-Uí Soo , Bakhadyr Khoussainov , Simone Linz

In this paper, approximate optimality conditions and sensitivity analysis in nearly convex optimization are discussed. More precisely, as in the spirit of convex analysis, we introduce the concept of $\varepsilon$-subdifferential for nearly…

Optimization and Control · Mathematics 2024-10-08 Nguyen Van Tuyen , Liguo Jiao , Vu Hong Quan , Duong Thi Viet An

We define quasiconvex programming, a form of generalized linear programming in which one seeks the point minimizing the pointwise maximum of a collection of quasiconvex functions. We survey algorithms for solving quasiconvex programs either…

Computational Geometry · Computer Science 2007-05-23 David Eppstein

In this paper, approximate convexity and approximate midconvexity properties, called $\varphi$-convexity and $\varphi$-midconvexity, of real valued function are investigated. Various characterizations of $\varphi$-convex and…

Classical Analysis and ODEs · Mathematics 2012-11-21 Judit Makó , Zsolt Páles

We determine the optimum topology of quasi-one dimensional nonlinear optical structures using generalized quantum graph models. Quantum graphs are relational graphs endowed with a metric and a multiparticle Hamiltonian acting on the edges,…

Quantum Physics · Physics 2015-06-19 Rick Lytel , Shoresh Shafei , Mark G. Kuzyk

Let $X$ be a normed space of a finite dimension at least two, and $C\subsetneq X$ a closed convex set with nonempty interior. We are interested in extending Lipschitz quasiconvex functions on $C$ to quasiconvex functions on $X$. We show…

Functional Analysis · Mathematics 2026-03-06 Carlo Alberto De Bernardi , Libor Veselý

We study the relations between some geometric properties of maximal monotone operators and generic geometric and analytical properties of the functions on the associate Fitzpatrick family of convex representations. We also investigate under…

Functional Analysis · Mathematics 2012-07-13 B. F. Svaiter

This paper investigates fractional torsional rigidity on compact, connected metric graphs, a novel extension of the classical concept to nonlocal operators. The fractional torsional rigidity is defined as the $L^1$-norm of the fractional…

Analysis of PDEs · Mathematics 2025-11-04 Sedef Özcan

The mixed metric dimension ${\rm mdim}(G)$ of a graph $G$ is the cardinality of a smallest set of vertices that (metrically) resolves each pair of elements from $V(G)\cup E(G)$. We say that $G$ is a max-mdim graph if ${\rm mdim}(G) = n(G)$.…

Combinatorics · Mathematics 2023-06-01 Ali Ghalavand , Sandi Klavžar , Mostafa Tavakoli
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