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In the current article, we establish a distinct version of the operators defined by Berwal \emph{et al.}, which is the Kantorovich type modification of $\alpha$-Bernstein operators to approximate Lebesgue's integrable functions. We define…

Classical Analysis and ODEs · Mathematics 2024-09-27 Jaspreet Kaur , Meenu Goyal , Khursheed J. Ansari

The main goal of this paper is to achieve a simultaneous treatment of the even and odd truncated matricial Stieltjes moment problems in the most general case. These results are generalizations of results of Chen and Hu [5,17] which…

Complex Variables · Mathematics 2016-04-27 Bernd Fritzsche , Bernd Kirstein , Conrad Mädler

Stein's method compares probability distributions through the study of a class of linear operators called Stein operators. While mainly studied in probability and used to underpin theoretical statistics, Stein's method has led to…

This paper concerns quasi-stochastic approximation (QSA) to solve root finding problems commonly found in applications to optimization and reinforcement learning. The general constant gain algorithm may be expressed as the…

Optimization and Control · Mathematics 2024-04-02 Caio Kalil Lauand , Sean Meyn

In this note, some inequalities involving operator means of sectorial matrices are proved which are generalizations and refinements of previous known results. Among them, let $A$ and $B$ be two accretive matrices with…

Functional Analysis · Mathematics 2023-05-09 M. Khosravi , A. Sheikhhosseini , S. Malekinejad

This paper discusses the approximation by %semigroups of operators of class ($\mathscr{C}_0$) on the sphere and focuses on a class of so called exponential-type multiplier operators. It is proved that such operators form a strongly…

Classical Analysis and ODEs · Mathematics 2014-09-15 Yuguang Wang , Feilong Cao

Our main aim is to investigate the approximation properties for the summation integral type operators in a statistical sense. In this regard, we prove the statistical convergence theorem using well known Korovkin theorem and the degree of…

Functional Analysis · Mathematics 2019-12-24 Rishikesh Yadav , Ramakanta Meher , Vishnu Narayan Mishra

We study a decentralized variant of stochastic approximation, a data-driven approach for finding the root of an operator under noisy measurements. A network of agents, each with its own operator and data observations, cooperatively find the…

Machine Learning · Computer Science 2022-06-17 Sihan Zeng , Thinh T. Doan , Justin Romberg

Positive polynomial operator that approximates Urison operator, when integration domain is a "regular triangle" is investigated. We obtain Bernstein Polynomials as a particular case.

Numerical Analysis · Mathematics 2011-10-31 Volodymyr Makarov , Ihor Demkiv

We prove sharp Strichartz-type estimates in three dimensions, including some which hold in reverse spacetime norms, for the wave equation with potential. These results are also tied to maximal operator estimates studied by…

Analysis of PDEs · Mathematics 2016-08-31 Marius Beceanu , Michael Goldberg

This research includes the study of some positive sampling Kantorovich operators (SK operators) and their convergence properties. A comprehensive analysis of both local and global approximation properties is presented using sampling…

Computer Vision and Pattern Recognition · Computer Science 2025-08-21 Digvijay Singh , Rahul Shukla , Karunesh Kumar Singh

In this paper we study a second-order mean-field stochastic differential systems describing the movement of a particle under the influence of a time-dependent force, a friction, a mean-field interaction and a space and time-dependent…

Probability · Mathematics 2022-10-10 T. C. Son , D. Q. Le , M. H. Duong

Many stochastic differential equations (SDEs) in the literature have a superlinearly growing nonlinearity in their drift or diffusion coefficient. Unfortunately, moments of the computationally efficient Euler-Maruyama approximation method…

Probability · Mathematics 2020-11-25 Martin Hutzenthaler , Arnulf Jentzen

In the present article, we deal with the overconvergence of the Sz?asz-Durrmeyer-Chlodowsky operators. Here we study the approximation properties e.g. upper estimates, Voronovskaja type result for these operators attached to analytic…

Classical Analysis and ODEs · Mathematics 2016-05-19 Meenu Goyal , P. N. Agrawal

In this article, we analyse the Kantorovich type exponential sampling operators and its linear combination. We derive the Voronovskaya type theorem and its quantitative estimates for these operators in terms of an appropriate K-functional.…

Functional Analysis · Mathematics 2020-02-10 B. Shivam , A. Sathish Kumar

The rigorous linking of exact stochastic models to mean-field approximations is studied. Starting from the differential equation point of view the stochastic model is identified by its Kolmogorov equations, which is a system of linear ODEs…

Dynamical Systems · Mathematics 2011-09-19 András Bátkai , Istvan Z. Kiss , Eszter Sikolya , Péter L. Simon

In this article a stochastic particle system approximation to the parametric sensitivity in the Smoluchowski coagulation equation is introduced. The parametric sensitivity is the derivative of the solution to the equation with respect to…

Probability · Mathematics 2016-09-08 I. Bailleul , P. L. W. Man , M. Kraft

The concentration inequality approach for normal approximation by Stein's method is generalized to the multivariate setting. We use this approach to prove a non-smooth function distance for multivariate normal approximation for standardized…

Probability · Mathematics 2015-05-19 Louis H. Y. Chen , Xiao Fang

We establish $r$-variational estimates for discrete truncated Stein-Wainger type operators on $\ell^p$ for $1<p<\infty$. Notably, these estimates are sharp and enhance the results obtained by Krause and Roos (J. Eur. Math. Soc. 2022, J.…

Classical Analysis and ODEs · Mathematics 2026-01-27 Jiecheng Chen , Renhui Wan

To facilitate the numerical analysis of particle methods, we derive truncation error estimates for the approximate operators in a generalized particle method. Here, a generalized particle method is defined as a meshfree numerical method…

Numerical Analysis · Mathematics 2019-07-09 Yusuke Imoto