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Let $D$ be a convex subset of a real vector space. It is shown that a radially lower semicontinuous function $f: D\to \mathbf{R}\cup \{+\infty\}$ is convex if and only if for all $x,y \in D$ there exists $\alpha=\alpha(x,y) \in (0,1)$ such…

Classical Analysis and ODEs · Mathematics 2017-09-26 Paolo Leonetti

Purely multiplicative comparisons of quantum relative entropy are desirable but challenging to prove. We show such comparisons for relative entropies between comparable densities, including the relative entropy of a density with respect to…

Quantum Physics · Physics 2022-12-16 Nicholas LaRacuente

In this work, we propose a new existence result for quasi-equilibrium problems using generalized monotonicity in an infinite dimensional space. Also, we show that the notions of generalized monotonicity can be characterized in terms of…

Optimization and Control · Mathematics 2019-02-28 John Cotrina

Constrained maximization of submodular functions poses a central problem in combinatorial optimization. In many realistic scenarios, a number of agents need to maximize multiple submodular objectives over the same ground set. We study such…

Data Structures and Algorithms · Computer Science 2024-07-22 Georgios Amanatidis , Georgios Birmpas , Philip Lazos , Stefano Leonardi , Rebecca Reiffenhäuser

We address the problem of approximating an unknown function from its discrete samples given at arbitrarily scattered sites. This problem is essential in numerical sciences, where modern applications also highlight the need for a solution to…

Numerical Analysis · Mathematics 2023-05-16 Nir Sharon , Rafael Sherbu Cohen , Holger Wendland

We generalize quasi-arithmetic means beyond scalars by considering the gradient map of a Legendre type real-valued function. The gradient map of a Legendre type function is proven strictly comonotone with a global inverse. It thus yields a…

Information Theory · Computer Science 2025-06-02 Frank Nielsen

We study the problem of minimizing a nonnegative separable concave function over a compact feasible set. We approximate this problem to within a factor of 1+epsilon by a piecewise-linear minimization problem over the same feasible set. Our…

Optimization and Control · Mathematics 2012-01-17 Thomas L. Magnanti , Dan Stratila

Usual termination proofs for a functional program require to check all the possible reduction paths. Due to an exponential gap between the height and size of such the reduction tree, no naive formalization of termination proofs yields a…

Logic in Computer Science · Computer Science 2015-09-11 Naohi Eguchi

The problem of maximizing non-negative monotone submodular functions under a certain constraint has been intensively studied in the last decade. In this paper, we address the problem for functions defined over the integer lattice. Suppose…

Data Structures and Algorithms · Computer Science 2016-05-11 Tasuku Soma , Yuichi Yoshida

Given a subset $A\times B$ of a locally convex space $X\times Y$ (with $A$ compact) and a function $f:A\times B\rightarrow\overline{\mathbb{R}}$ such that $f(\cdot,y),$ $y\in B,$ are concave and upper semicontinuous, the minimax inequality…

Optimization and Control · Mathematics 2023-08-21 M. I. A. Ghitri , A. Hantoute

A subequation on an open subset $X\subset \mathbb R^n$ is a subset $F$ of the space of $2$-jets on $X$ with certain properties. A smooth function is said to be $F$-subharmonic if all of its $2$-jets lie in $F$, and using the viscosity…

Analysis of PDEs · Mathematics 2021-05-13 Julius Ross , David Witt Nyström

We study a stochastic variant of monotone submodular maximization problem as follows. We are given a monotone submodular function as an objective function and a feasible domain defined on a finite set, and our goal is to find a feasible…

Data Structures and Algorithms · Computer Science 2020-06-29 Takanori Maehara , Yutaro Yamaguchi

We consider several classes of highly important semidefinite optimization problems that involve both a convex objective function (smooth or nonsmooth) and additional linear or nonlinear smooth and convex constraints, which are ubiquitous in…

Optimization and Control · Mathematics 2025-04-08 Dan Garber , Atara Kaplan

A new and simple method for quasi-convex optimization is introduced from which its various applications can be derived. Especially, a global optimum under constrains can be approximated for all continuous functions.

Optimization and Control · Mathematics 2020-12-07 Sompong Dhompongsa , Poom Kumam

We introduce and study the notion of weak semi-greedy systems -which is inspired in the concepts of semi-greedy and Branch semi-greedy systems and weak thresholding sets-, and prove that in the context Markushevich bases in infinite…

Functional Analysis · Mathematics 2021-10-15 Miguel Berasategui , Silvia Lassalle

The article is devoted to holomorphic and meromorphic functions of quaternion and octonion variables. New classes of quasi-conformal and quasi-meromorphic mappings are defined and investigated. Properties of such functions such as their…

Complex Variables · Mathematics 2018-12-18 S. V. Ludkovsky

Quasi-lattices are introduced in terms of 'join' and 'meet' operations. It is observed that quasi-lattices become lattices when these operations are associative and when these operations satisfy 'modularity' conditions. A fundamental…

Combinatorics · Mathematics 2019-05-14 C. Ganesa Moorthy , SG. Karpagavalli

We present a new type of monotone submodular functions: \emph{multi-peak submodular functions}. Roughly speaking, given a family of sets $\cF$, we construct a monotone submodular function $f$ with a high value $f(S)$ for every set $S \in…

Data Structures and Algorithms · Computer Science 2015-03-20 Shahar Dobzinski , Jan Vondrak

We generalize Newton-type methods for minimizing smooth functions to handle a sum of two convex functions: a smooth function and a nonsmooth function with a simple proximal mapping. We show that the resulting proximal Newton-type methods…

Machine Learning · Statistics 2014-03-19 Jason D. Lee , Yuekai Sun , Michael A. Saunders

Submodular function maximization has found a wealth of new applications in machine learning models during the past years. The related supermodular maximization models (submodular minimization) also offer an abundance of applications, but…

Data Structures and Algorithms · Computer Science 2020-06-25 Mehrdad Ghadiri , Richard Santiago , Bruce Shepherd