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In this paper, we consider a broad class of nonsmooth and nonconvex fractional programs, where the numerator can be written as the sum of a continuously differentiable convex function whose gradient is Lipschitz continuous and a proper…

Optimization and Control · Mathematics 2022-01-19 Radu Ioan Boţ , Minh N. Dao , Guoyin Li

The method of self-similar factor approximants is completed by defining the approximants of odd orders, constructed from the power series with the largest term of an odd power. It is shown that the method provides good approximations for…

Mathematical Physics · Physics 2009-11-13 V. I. Yukalov , E. P. Yukalova

This paper studies the convexity properties of nonsmooth extended-real-valued weakly convex functions, a class of functions that is central to modern optimization and its applications. We establish new characterizations of convexity using…

Optimization and Control · Mathematics 2026-03-27 Vo Thanh Phat

The article is devoted to some adaptive methods for variational inequalities with relatively smooth and relatively strongly monotone operators. Starting from the recently proposed proximal variant of the extragradient method for this class…

Optimization and Control · Mathematics 2023-08-02 S. S. Ablaev , F. S. Stonyakin , M. S. Alkousa , D. A. Pasechnyuk

The proximal point algorithm is a widely used tool for solving a variety of convex optimization problems such as finding zeros of maximally monotone operators, fixed points of nonexpansive mappings, as well as minimizing convex functions.…

Optimization and Control · Mathematics 2018-04-19 Laurentiu Leustean , Adriana Nicolae , Andrei Sipos

We consider strongly-convex-strongly-concave saddle-point problems with general non-bilinear objective and different condition numbers with respect to the primal and the dual variables. First, we consider such problems with smooth composite…

Optimization and Control · Mathematics 2021-06-15 Vladislav Tominin , Yaroslav Tominin , Ekaterina Borodich , Dmitry Kovalev , Alexander Gasnikov , Pavel Dvurechensky

We introduce a new type of Bernstein operators, which can be used to approximate the functions with inner singularities. The direct and inverse results of the weighted approximation of this new type of combinations are given.

Functional Analysis · Mathematics 2012-08-21 Wen-ming Lu , Lin Zhang

We consider a class of monotone operators which are appropriate for symbolic representation and manipulation within a computer algebra system. Various structural properties of the class (e.g., closure under taking inverses, resolvents) are…

Optimization and Control · Mathematics 2018-05-28 Florian Lauster , D. Russell Luke , Matthew K. Tam

A formula for the sub\-differential of the sum of a series of convex functions defined on a Banach space was provided by X. Y. Zheng in 1998. In this paper, besides a slight extension to locally convex spaces of Zheng's results, we provide…

Optimization and Control · Mathematics 2015-06-04 C. Vallee , C. Zalinescu

A new type of combinations of Bernstein operators is given in [1]. Here, we introduce another one, which can be used to approximate the functions with singularities. The direct and inverse results of the weighted approximation of this new…

Functional Analysis · Mathematics 2011-06-28 Wen-ming Lu , Lin Zhang

In this paper we consider the approximation of a function by its interpolating multilinear spline and the approximation of its derivatives by the derivatives of the corresponding spline. We derive formulas for the uniform approximation…

Numerical Analysis · Mathematics 2013-08-27 Ryan Anderson , Yuliya Babenko , Tetiana Leskevych

A weakly infeasible semidefinite program (SDP) has no feasible solution, but it has approximate solutions whose constraint violation is arbitrarily small. These SDPs are ill-posed and numerically often unsolvable. They are also closely…

Optimization and Control · Mathematics 2022-07-11 Gábor Pataki , Aleksandr Touzov

Based on the needs of convergence proofs of preconditioned proximal point methods, we introduce notions of partial strong submonotonicity and partial (metric) subregularity of set-valued maps. We study relationships between these two…

Optimization and Control · Mathematics 2020-03-02 Tuomo Valkonen

Motivated by the extensive application of approximate gradients in machine learning and optimization, we investigate inexact subgradient methods subject to persistent additive errors. Within a nonconvex semialgebraic framework, assuming…

Optimization and Control · Mathematics 2025-05-14 Jérôme Bolte , Tam Le , Éric Moulines , Edouard Pauwels

In this paper, we are devoted to studying some sharp bounds for Hardy type operators on mixed radial-angular type function spaces. In addition, we will establish the sharp weak-type estimates for the fractional Hardy operator and its…

Functional Analysis · Mathematics 2024-02-06 Ronghui Liu , Yanqi Yang , Shuangping Tao

We give alternative proofs to certain results in the paper "Weak limits of almost invariant projections" by using ultraproducts of operators.

Operator Algebras · Mathematics 2014-11-14 March Boedihardjo

Weak values have been shown to be helpful especially when considering them as the outcomes of weak measurements. In this paper we show that in principle, the real and imaginary parts of the weak value of any operator may be elucidated from…

Quantum Physics · Physics 2018-10-17 Eliahu Cohen , Eli Pollak

In this paper, we provide tight lower bounds for the oracle complexity of minimizing high-order H\"older smooth and uniformly convex functions. Specifically, for a function whose $p^{th}$-order derivatives are H\"older continuous with…

Optimization and Control · Mathematics 2025-06-10 Cedar Site Bai , Brian Bullins

The relationship between the operator norms of fractional integral operators acting on weighted Lebesgue spaces and the constant of the weights is investigated. Sharp boundsare obtained for both the fractional integral operators and the…

Classical Analysis and ODEs · Mathematics 2012-05-08 Michael Lacey , Kabe Moen , Carlos Perez , Rodolfo H. Torres

In this paper, we use Proximal Cubic regularized Newton Methods (PCNM) to optimize the sum of a smooth convex function and a non-smooth convex function, where we use inexact gradient and Hessian, and an inexact subsolver for the cubic…

Optimization and Control · Mathematics 2019-02-27 Chaobing Song , Ji Liu , Yong Jiang