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Several results in functional analysis are extended to the setting of $L^0$-modules, where $L^0$ denotes the ring of all measurable functions $x\colon \Omega\to \mathbb{R}$. The focus is on results involving compactness. To this end, a…

Functional Analysis · Mathematics 2017-11-28 Asgar Jamneshan , Jose Miguel Zapata

This article supplements recent work of the authors. (1) A criterion for failure of covariant finiteness of a full subcategory of $\Lambda\text{-mod}$ is given, where $\Lambda$ is a finite dimensional algebra. The criterion is applied to…

Representation Theory · Mathematics 2014-07-10 B. Huisgen-Zimmermann , S. O. Smalø

Let $(\Omega,{\cal F},P)$ be a probability space and $L^{0}({\cal F},R)$ the algebra of equivalence classes of real-valued random variables on $(\Omega,{\cal F},P)$. When $L^{0}({\cal F},R)$ is endowed with the topology of convergence in…

Functional Analysis · Mathematics 2011-03-22 Guo TieXin , Zeng XiaoLin

In this paper we propose a stochastic primal dual fixed point method (SPDFP) for solving the sum of two proper lower semi-continuous convex function and one of which is composite. The method is based on the primal dual fixed point method…

Optimization and Control · Mathematics 2020-04-21 YaNanZhu , XiaoqunZhang

We propose a novel foundation for calculus that focuses on the notion of approximations while avoiding the use of limits altogether. Continuity is defined as approximation at a point, while differentiability is defined as approximation with…

History and Overview · Mathematics 2025-10-27 Michael P. Lamoureux , Matt Yedlin

Convolution admits a natural formulation as a functional operation on matrices. Motivated by the functional and entrywise calculi, this leads to a framework in which convolution defines a matrix transform that preserves positivity. Within…

Functional Analysis · Mathematics 2026-01-01 Javad Mashreghi , Mostafa Nasri , Prateek Kumar Vishwakarma

In online convex optimization (OCO), Lipschitz continuity of the functions is commonly assumed in order to obtain sublinear regret. Moreover, many algorithms have only logarithmic regret when these functions are also strongly convex.…

Machine Learning · Computer Science 2021-01-01 Yihan Zhou , Victor S. Portella , Mark Schmidt , Nicholas J. A. Harvey

The non-Leibniz formalism is introduced in this article. The formalism is based on the generalized differentiation operator (kappa-operator) with a non-zero Leibniz defect. The Leibniz defect of the introduced operator linearly depends on…

General Physics · Physics 2017-12-06 V. Kobelev

Robust principal component analysis seeks to recover a low-rank matrix from fully observed data with sparse corruptions. A scalable approach fits a low-rank factorization by minimizing the sum of entrywise absolute residuals, leading to a…

Optimization and Control · Mathematics 2026-01-30 Pinxi Gong , Lexiao Lai , Jianhao Ma

We study in detail the zero set of a regular function of a quaternionic or octonionic variable. By means of a division lemma for convergent power series, we find the exact relation existing between the zeros of two octonionic regular…

Complex Variables · Mathematics 2010-08-26 Riccardo Ghiloni , Alessandro Perotti

Many natural combinatorial quantities can be expressed by counting the number of homomorphisms to a fixed relational structure. For example, the number of 3-colorings of an undirected graph $G$ is equal to the number of homomorphisms from…

Computational Complexity · Computer Science 2017-10-03 Hubie Chen

We use the core model for sequences of measures to prove a new lower bound for the consistency strength of the failure of the SCH: THEOREM (i) If there is a singular strong limit cardinal $\kappa$ such that $2^\kappa > kappa^+$ then there…

Logic · Mathematics 2016-09-06 William J. Mitchell

We investigate Lagrangian duality for nonconvex optimization problems. To this aim we use the $\Phi$-convexity theory and minimax theorem for $\Phi$-convex functions. We provide conditions for zero duality gap and strong duality. Among the…

Optimization and Control · Mathematics 2020-11-19 Ewa M. Bednarczuk , Monika Syga

We generalize and improve the original characterization given by Valadier [20, Theorem 1] of the subdifferential of the pointwise supremum of convex functions, involving the subdifferentials of the data functions at nearby points. We remove…

Optimization and Control · Mathematics 2017-07-13 R. Correa , A. Hantoute , M. A. López

We prove that assuming suitable cardinal arithmetic, if B is a Boolean algebra every homomorphic image of which is isomorphic to a factor, then B has locally small density. We also prove that for an (infinite) Boolean algebra B, the number…

Logic · Mathematics 2008-02-03 Saharon Shelah

Recently, $l_{2,1}$ matrix norm has been widely applied to many areas such as computer vision, pattern recognition, biological study and etc. As an extension of $l_1$ vector norm, the mixed $l_{2,1}$ matrix norm is often used to find…

Machine Learning · Computer Science 2013-03-19 Liping Wang , Songcan Chen

We introduce a unified elliptic extension of CL-type Clausen functions based on logarithmic primitives of the Jacobi theta function. The resulting elliptic Clausen family satisfies the same integral recursion as the classical circular case,…

General Mathematics · Mathematics 2026-02-13 Ken Nagai

In this article we study convex non-autonomous variational problems with differential forms and corresponding function spaces. We introduce a general framework for constructing counterexamples to the Lavrentiev gap, which we apply to…

Analysis of PDEs · Mathematics 2023-05-09 Anna Kh. Balci , Mikhail Surnachev

Consider a real-valued function that can only be observed with stochastic noise at a finite set of design points within a Euclidean space. We wish to determine whether there exists a convex function that goes through the true function…

Other Statistics · Statistics 2018-07-30 Nanjing Jian , Shane G. Henderson

In this note we provide a full conjugacy and subdifferential calculus for convex convex-composite functions in finite-dimensional space. Our approach, based on infimal convolution and cone-convexity, is straightforward and yields the…

Optimization and Control · Mathematics 2019-08-22 James V. Burke , Tim Hoheisel , Quang V. Nguyen