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Some fixed point results of classical theory, such as Banach's Fixed Point Theorem, have been previously extended by other authors to asymmetric spaces in recent years. The aim of this paper is to extend to asymmetric spaces some others…

General Topology · Mathematics 2023-05-17 L. Benítez-Babilonia , R. Felipe , L. Rubio

Given any ${\bf{a}}: = \left( {a_1 ,a_2 , \ldots ,a_n } \right)$ and ${\bf{b}}: = \left( {b_1 ,b_2 , \ldots ,b_n } \right)$ in $\mathbb{R}^n$. The $\textbf{n}$-fold convex function defined on $\left[ {{\bf{a}},{\bf{b}}} \right]$,…

Classical Analysis and ODEs · Mathematics 2016-04-08 Mohammad W. Alomari

It has been recently discovered that a convex function can be determined by its slopes and its infimum value, provided this latter is finite. The result was extended to nonconvex functions by replacing the infimum value by the set of all…

Functional Analysis · Mathematics 2025-10-21 Aris Daniilidis , David Salas , Sebastián Tapia-García

We study differentiability properties of functions defined in the euclidean space in terms of a conical square function which is analogue to the classical square function introduced by Stein and Zygmund in the sixties. Pointwise…

Classical Analysis and ODEs · Mathematics 2014-04-08 Artur Nicolau

A new version of the convexification method is developed analytically and tested numerically for a 1-D coefficient inverse problem in the frequency domain. Unlike the previous version, this one does not use the so-called "tail function",…

Numerical Analysis · Mathematics 2018-10-17 Michael V. Klibanov , Aleksandr E. Kolesov , Anders Sullivan , Lam Nguyen

In this paper we extend the Balian--Low theorem, which is a version of the uncertainty principle for Gabor (Weyl--Heisenberg) systems, to functions of several variables. In particular, we first prove the Balian--Low theorem for arbitrary…

Functional Analysis · Mathematics 2015-06-26 John J. Benedetto , Wojciech Czaja , Andrei Ya. Maltsev

The subgradient method is a classical and foundational approach in non-smooth convex optimization; its simplicity, robustness, and role as a conceptual and algorithmic starting point have made it the backbone of many significant…

Optimization and Control · Mathematics 2026-05-26 G. C. Bento , J. X. Cruz Neto , J. O. Lopes , I. D. L. Melo

The well-known Baker-Campbell-Hausdorff theorem in Lie theory says that the logarithm of a noncommutative product e X e Y can be expressed in terms of iterated commutators of X and Y. This paper provides a gentle introduction t{\'o}…

Rings and Algebras · Mathematics 2018-05-03 Shanzhong Sun , Yong Li , David Sauzin

We explain the exact meaning of a statement we made in a previous paper on invariants, namely that a complex-valued function of the data of the functional equation of an $L$-function is an invariant if and only if it is stable under the…

Number Theory · Mathematics 2026-03-17 Jerzy Kaczorowski , Alberto Perelli

In discrete convex analysis, the scaling and proximity properties for the class of L$^\natural$-convex functions were established more than a decade ago and have been used to design efficient minimization algorithms. For the larger class of…

Combinatorics · Mathematics 2017-12-13 Satoko Moriguchi , Kazuo Murota , Akihisa Tamura , Fabio Tardella

This article shows a very elementary and straightforward proof of the Implicit Function Theorem for differentiable maps $F(x,y)$ defined on a finite-dimensional Euclidean space. There are no hypothesis on the continuity of the partial…

Classical Analysis and ODEs · Mathematics 2022-02-15 Oswaldo R. B. de Oliveira

In an unbounded plane, straight lines are used extensively for mathematical analysis. They are tools of convenience. However, those with high slope values become unbounded at a faster rate than the independent variable. So, straight lines,…

Machine Learning · Computer Science 2024-12-24 Vijay Prakash S

We provide a simple convergence proof for AdaGrad optimizing non-convex objectives under only affine noise variance and bounded smoothness assumptions. The proof is essentially based on a novel auxiliary function $\xi$ that helps eliminate…

Machine Learning · Computer Science 2023-09-29 Bohan Wang , Huishuai Zhang , Zhi-Ming Ma , Wei Chen

In this paper we are concerned with the space of tempered ultrahyperfunctions corresponding to a proper open convex cone. A holomorphic extension theorem (the version of the celebrated edge of the wedge theorem) will be given for this…

Functional Analysis · Mathematics 2009-10-28 Daniel H. T. Franco

Stochastic Gradient Descent (SGD) based methods have been widely used for training large-scale machine learning models that also generalize well in practice. Several explanations have been offered for this generalization performance, a…

Machine Learning · Computer Science 2021-02-11 Yikai Zhang , Wenjia Zhang , Sammy Bald , Vamsi Pingali , Chao Chen , Mayank Goswami

We show that if the Hessian of a $C^{1,1}$ function has uniformly positive determinant almost everywhere then its index is locally constant, as conjectured by \v{S}ver\'ak in 1992. We deduce this result as a consequence of a more general…

Analysis of PDEs · Mathematics 2025-06-05 André Guerra , Riccardo Tione

We obtain some Liouville type theorems for positive harmonic functions on compact Riemannian manifolds with nonnegative Ricci curvature and strictly convex boundary and partially verifies Wang's conjecture (J. Geom. Anal. 31 (2021)). For…

Analysis of PDEs · Mathematics 2025-09-12 Xiaohan Cai

Asymptotic expansions for the Bateman and Havelock functions defined respectively by the integrals \[\frac{2}{\pi}\int_0^{\pi/2} \!\!\!\begin{array}{c} \cos\\\sin\end{array}\!(x\tan u-\nu u)\,du\] are obtained for large real $x$ and large…

Classical Analysis and ODEs · Mathematics 2021-09-03 R B Paris

This article establishes existence, non-existence and Liouville-type theorems for nonlinear equations of the form $$-div (|x|^{a} D u ) = f(x,u), ~ u > 0,\, \mbox{ in } \Omega,$$ where $N \geq 3$, $\Omega$ is an open domain in…

Analysis of PDEs · Mathematics 2021-03-17 John Villavert

In this note we generalize the Ball-James rigidity theorem for gradient differential inclusions to the setting of a general linear differential constraint. In particular, we prove the rigidity for approximate solutions to the two-state…

Analysis of PDEs · Mathematics 2018-03-28 Guido De Philippis , Luca Palmieri , Filip Rindler