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Submodular maximization has been widely studied over the past decades, mostly because of its numerous applications in real-world problems. It is well known that the standard greedy algorithm guarantees a worst-case approximation factor of…

Data Structures and Algorithms · Computer Science 2020-02-12 Alfredo Torrico , Mohit Singh , Sebastian Pokutta

Greedy algorithms which use only function evaluations are applied to convex optimization in a general Banach space $X$. Along with algorithms that use exact evaluations, algorithms with approximate evaluations are treated. A priori upper…

Machine Learning · Statistics 2014-01-03 R. A. DeVore , V. N. Temlyakov

If a function $f$, acting on a Euclidean space $\mathbb{R}^n$, is "almost" orthogonally additive in the sense that $f(x+y)=f(x)+f(y)$ for all $(x,y)\in\bot\setminus Z$, where $Z$ is a "negligible" subset of the $(2n-1)$-dimensional manifold…

Classical Analysis and ODEs · Mathematics 2012-09-21 Tomasz Kochanek , Wirginia Wyrobek-Kochanek

Results on the upper and lower semicontinuity of functionals defined on spaces of convex and more general functions are established. In particular, the following result is obtained. Let $\phi(v; \cdot)$ be the density of the absolutely…

Functional Analysis · Mathematics 2025-12-10 Fernanda M. Baêta , Monika Ludwig

One of the challenges of modern engineering, and robotics in particular, is designing complex systems, composed of many subsystems, rigorously and with optimality guarantees. This paper introduces a theory of co-design that describes…

Logic in Computer Science · Computer Science 2016-10-13 Andrea Censi

We consider the convexity properties of distortion functionals, particularly the linear distortion, defined for homeomorphisms of domains in Euclidean $n$-spaces, $n\geq 3$. The inner and outer distortion functionals are lower…

Complex Variables · Mathematics 2023-02-06 Sayed Mohsen Hashemi , Gaven J. Martin

The point pair function $p_G$ defined in a domain $G\subsetneq\mathbb{R}^n$ is shown to be a quasi-metric and its other properties are studied. For a convex domain $G\subsetneq\mathbb{R}^n$, a new intrinsic quasi-metric called the function…

Metric Geometry · Mathematics 2021-03-05 Oona Rainio

The aim of this work is to provide formulae for the subdifferential and the conjungate function of the supremun function over an arbitrary family of functions. The work is principally motivated by the case when data functions are lower…

Optimization and Control · Mathematics 2018-05-28 Pedro Pérez-Aros

Real continuous submodular functions, as a generalization of the corresponding discrete notion to the continuous domain, gained considerable attention recently. The analog notion for entropy functions requires additional properties: a real…

Optimization and Control · Mathematics 2021-02-12 Laszlo Csirmaz

In this paper we address the graph matching problem. Following the recent works of \cite{zaslavskiy2009path,Vestner2017} we analyze and generalize the idea of concave relaxations. We introduce the concepts of conditionally concave and…

Optimization and Control · Mathematics 2018-12-27 Haggai Maron , Yaron Lipman

In this article we utilise abstract convexity theory in order to unify and generalize many different concepts from nonsmooth analysis. We introduce the concepts of abstract codifferentiability, abstract quasidifferentiability and abstract…

Optimization and Control · Mathematics 2018-10-03 M. V. Dolgopolik

A function in a class $\mathcal{F}(X)$ is said to be subdifferentially determined in $\mathcal{F}(X)$ if it is equal up to an additive constant to any function in $\mathcal{F}(X)$ with the same subdifferential. A function is said to be…

Optimization and Control · Mathematics 2018-10-16 Marc Lassonde

We consider sets and maps defined over an o-minimal structure over the reals, such as real semi-algebraic or subanalytic sets. A {\em monotone map} is a multi-dimensional generalization of a usual univariate monotone function, while the…

Logic · Mathematics 2013-08-19 Saugata Basu , Andrei Gabrielov , Nicolai Vorobjov

The notion of ordinal concavity of utility functions has recently been considered by Hafalir, Kojima, Yenmez, and Yokote in economics while there exist earlier related works in discrete optimization and operations research. In the present…

Combinatorics · Mathematics 2024-11-14 Satoru Fujishige , Fuhito Kojima , Koji Yokote

When analyzing complex networks a key target is to uncover their modular structure, which means searching for a family of modules, namely node subsets spanning each a subnetwork more densely connected than the average. This work proposes a…

Discrete Mathematics · Computer Science 2018-09-10 Giovanni Rossi

This paper defines weak-$\alpha$-supermodularity for set functions. Many optimization objectives in machine learning and data mining seek to minimize such functions under cardinality constrains. We prove that such problems benefit from a…

Data Structures and Algorithms · Computer Science 2015-02-24 Christos Boutsidis , Edo Liberty , Maxim Sviridenko

We introduce the set of quasi-Herglotz functions and demonstrate that it has properties useful in the modeling of non-passive systems. The linear space of quasi-Herglotz functions constitutes a natural extension of the convex cone of…

Numerical Analysis · Mathematics 2021-02-17 Yevhen Ivanenko , Mitja Nedic , Mats Gustafsson , B. L. G. Jonsson , Annemarie Luger , Sven Nordebo

This paper deals with approximate solutions of a nonsmooth semi-infinite programming with multiple interval-valued objective functions. We first introduce four types of approximate quasi Pareto solutions of the considered problem by…

Optimization and Control · Mathematics 2022-01-25 Nguyen Huy Hung , Hoang Ngoc Tuan , Nguyen Van Tuyen

This paper introduces the tensor representation of a network, here tensors are the primitive structures of the network. In view of tensor chains, two binary operations on tensor sets are defined: chain addition and reducing. Based on the…

Rings and Algebras · Mathematics 2022-03-15 Yanhui Wang , Dazhi Meng

We prove that if f : R^N --> R is quasiconvex and U is open in the density topology of R^N, then sup_U f = ess sup_U f, while inf_U f = ess inf_U f if and only if the equality holds when U = R^N. The first (second) property is typical of…

Metric Geometry · Mathematics 2019-08-15 Patrick J. Rabier
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