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We provide three new proofs of the strong concavity of the dual function of some convex optimization problems. For problems with nonlinear constraints, we show that the the assumption of strong convexity of the objective cannot be weakened…

Optimization and Control · Mathematics 2021-05-04 Vincent Guigues

Deep neural networks have emerged as powerful tools for learning operators defined over infinite-dimensional function spaces. However, existing theories frequently encounter difficulties related to dimensionality and limited…

Machine Learning · Computer Science 2026-05-12 Jianfei Li , Shuo Huang , Han Feng , Ding-Xuan Zhou , Gitta Kutyniok

Convex functionals are ubiquitous in applied analysis, appearing as value functions, risk measures, super-hedging prices, and loss functionals in machine learning. In many applications, however, the functional is only observed through…

Functional Analysis · Mathematics 2026-05-12 Anastasis Kratsios

This note studies, and partially solves, 3 elementary questions about continuous rational functions on real (and p-adic) algebraic varieties: Can one restrict such a function to a subvariety? Can one extend such a function from a…

Algebraic Geometry · Mathematics 2013-01-25 János Kollár

In this research article, we formulate and prove multidimensional Widder--Arendt theorem and integrated form of multidimensional Widder--Arendt theorem for functions with values in sequentially complete locally convex spaces. Established…

Functional Analysis · Mathematics 2025-11-25 Marko Kostic

Our contribution in this paper is two folded. We consider first the case of linear programming with real coefficients and give a method which allows the computation of a new upper bound on the distance from the origin to a feasible point.…

Optimization and Control · Mathematics 2020-10-30 Beniamin Costandin , Marius Costandin , Petru Dobra

Let $\Omega$ be an unbounded domain in $\mathbb{R}\times\mathbb{R}^{d}.$ A positive harmonic function $u$ on $\Omega$ that vanishes on the boundary of $\Omega$ is called a Martin function. In this note, we show that, when $\Omega$ is…

Analysis of PDEs · Mathematics 2019-09-12 A. -K. Gallagher , J. Lebl , K. Ramachandran

Every real Bank-Laine function of finite order, whose zeros are all real but neither bounded above nor bounded below, either has an explicit representation in terms of trigonometric functions or has zeros with exponent of convergence at…

Complex Variables · Mathematics 2020-07-21 J. K. Langley

Many supervised machine learning methods are naturally cast as optimization problems. For prediction models which are linear in their parameters, this often leads to convex problems for which many mathematical guarantees exist. Models which…

Machine Learning · Computer Science 2021-10-18 Francis Bach , Lenaïc Chizat

We solve two main questions on linear structures of (non-)norm-attaining Lipschitz functions. First, we show that for every infinite metric space $M$, the set consisting of Lipschitz functions on $M$ which do not strongly attain their norm…

Functional Analysis · Mathematics 2024-04-12 Geunsu Choi , Mingu Jung , Han Ju Lee , Oscar Roldan

This paper is devoted to investigating the sequence of some linear functionals in the space $BV$ of finite variation functions. We prove that under certain conditions this sequence is bounded. We also prove that this result is sharp. In…

Functional Analysis · Mathematics 2023-08-09 L-E. Persson , V. Tsagareishvili , G. Tutberidze

We discuss conditions under which a convex cone $\K\subset \R^{\Omega}$ admits a probability $m$ such that $\sup_{k\in \K} m(k)\leq0$. Based on these, we also characterize linear functionals that admit the representation as finitely…

Functional Analysis · Mathematics 2021-03-26 Gianluca Cassese

We develop a functional extension of an extremal principle by Schneider (Monatsh. Math., 1967) by introducing generalized outer linearizations of convex functions. Given a coercive convex function on $\mathbb{R}^n$, a generalized outer…

Functional Analysis · Mathematics 2026-05-06 Steven Hoehner , Fabian Mussnig

We analyze the topological properties of the set of functions that can be implemented by neural networks of a fixed size. Surprisingly, this set has many undesirable properties. It is highly non-convex, except possibly for a few exotic…

General Topology · Mathematics 2020-01-24 Philipp Petersen , Mones Raslan , Felix Voigtlaender

Functional data analysis is a fast evolving branch of statistics. Estimation procedures for the popular functional linear model either suffer from lack of robustness or are computationally burdensome. To address these shortcomings, a…

Methodology · Statistics 2021-08-27 Ioannis Kalogridis , Stefan Van Aelst

Given a plane graph $G$ (i.e., a planar graph with a fixed planar embedding) and a simple cycle $C$ in $G$ whose vertices are mapped to a convex polygon, we consider the question whether this drawing can be extended to a planar…

Computational Geometry · Computer Science 2013-08-16 Tamara Mchedlidze , Martin Nöllenburg , Ignaz Rutter

A new nonparametric estimator of a convex regression function in any dimension is proposed and its convergence properties are studied. We start by using any estimator of the regression function and we \emph{convexify} it by taking the…

Statistics Theory · Mathematics 2010-06-16 Néstor E. Aguilera , Liliana Forzani , Pedro Morin

In the present article we describe a class of algebraic curves on which rational functions of two arguments may reach all their possible limiting values. We also solve a similar question for functions that can be represented as a uniform…

Classical Analysis and ODEs · Mathematics 2007-05-23 Yaacov Tzeitlin

In this paper, we relate the set of asymptotic critical values of a polynomial function $f$ with the set of discontinuity of two functions, the multivalued function which associate to each value $t$ the set of tangent directions at infinity…

Algebraic Geometry · Mathematics 2021-05-24 Si Tiep Dinh , Tien Son Pham

We study Min-Max affine approximants of a continuous convex or concave function $f:\Delta\subset \mathbb R^k\xrightarrow{} \mathbb R$ where $\Delta$ is a convex compact subset of $\mathbb R^k$. In the case when $\Delta$ is a simplex we…

Optimization and Control · Mathematics 2021-08-19 Steven B. Damelin , David L. Ragozin , Michael Werman