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Approximation theory is a substantial field of mathematical analysis that emerged in the 19th century and has been developed by mathematicians across the globe ever since. Its importance has increased over time, as it provides solutions to…

General Mathematics · Mathematics 2025-01-22 Reşat Aslan

In this paper, we are dealing with a new type of Baskakov-Schurer-Szasz operators (\ref{eq1}). Approximation properties of this operators are explored: the rate of convergence in terms of the usual moduli of smoothness is given, the…

Functional Analysis · Mathematics 2015-08-24 Vishnu Narayan Mishra , Preeti Sharma

Recently, W. M. Schmidt and L. Summerer introduced a new theory which allowed them to recover the main known inequalities relating the usual exponents of Diophantine approximation to a point in $\mathbb{R}^n$, and to discover new ones. They…

Number Theory · Mathematics 2016-04-26 Damien Roy

In this paper we construct Stancu type q-Kantrovich-Sz\'asz-Mirakjan operators generated by Dunkl generalization of the exponential function. We obtain some approximation results using the Korovkin approximation theorem and the weighted…

Classical Analysis and ODEs · Mathematics 2016-03-29 M. Mursaleen , Taqseer Khan , Nasiruzzaman

In a series of recent papers, W. M. Schmidt and L. Summerer developed a new theory by which they recover all major generic inequalities relating exponents of Diophantine approximation to a point in $\mathbb{R}^n$, and find new ones. Given a…

Number Theory · Mathematics 2016-07-05 Damien Roy

In this paper, we study the convergence in variation for the generalized sampling operators based upon averaged-type kernels and we obtain a characterization of absolutely continuous functions. This result is proved exploiting a relation…

Functional Analysis · Mathematics 2017-10-13 Laura Angeloni , Danilo Costarelli , Gianluca Vinti

We investigate the classes of functions whose minimization diagrams can be approximated efficiently in \Re^d. We present a general framework and a data-structure that can be used to approximate the minimization diagram of such functions.…

Computational Geometry · Computer Science 2013-04-03 Sariel Har-Peled , Nirman Kumar

In this paper, we deal with the complex Baskakov-Szasz-Durrmeyer mixed operators and study Voronovskaja type results with quantitative estimates for these operators attached to analytic functions of exponential growth in the open disk of…

Classical Analysis and ODEs · Mathematics 2015-06-24 Sorin G. Gal , Vijay Gupta

This paper is devoted to study the approximation properties and rate of approximation of the Szasz-Mirakjan-Kantrovich-Stancu type polynomials generated by the Dunkl generalization of the exponential function with respect to q -calculus. We…

Classical Analysis and ODEs · Mathematics 2016-03-21 M. Mursaleen , Md. Nasiruzzaman

In this paper, we introduce a Shurer type genaralization of (p,q)-Bernstein-Kantorovich operators based on (p,q)-integers and we call it as (p,q)-Bernstein-Schurer Kantorovich operators. We study approximation properties for these operators…

Classical Analysis and ODEs · Mathematics 2015-06-09 M. Mursaleen , Faisal Khan

An approach is suggested defining effective sums of divergent series in the form of self-similar exponential approximants. The procedure of constructing these approximants from divergent series with arbitrary noninteger powers is developed.…

Statistical Mechanics · Physics 2009-10-31 V. I. Yukalov , S. Gluzman

In this paper, we introduce a new family of Szasz-Mirakyan-Durrmeyer operators defined on the half-line [0,\infty), constructed using Laguerre-type kernels. We discuss in detail the algebraic structure and analytical properties of these…

General Mathematics · Mathematics 2025-11-12 Prashantkumar G. Patel

For the past 30 years or so, machine learning has stimulated a great deal of research in the study of approximation capabilities (expressive power) of a multitude of processes, such as approximation by shallow or deep neural networks,…

Machine Learning · Computer Science 2025-01-07 Hrushikesh Mhaskar

In this paper, we extend several approximation theorems, originally formulated in the context of the standard $L^p$ norm, to the more general framework of variable exponent spaces. Our study is motivated by applications in neural networks,…

Functional Analysis · Mathematics 2025-04-22 Mitsuo Izuki , Takahiro Noi , Yoshihiro Sawano , Hirokazu Tanaka

Global information about dynamical systems can be extracted by analysing associated infinite-dimensional transfer operators, such as Perron-Frobenius and Koopman operators as well as their infinitesimal generators. In practice, these…

Numerical Analysis · Mathematics 2024-06-21 Liam Llamazares-Elias , Samir Llamazares-Elias , Jonas Latz , Stefan Klus

We show that any $n\times m$ matrix $A$ can be approximated in operator norm by a submatrix with a number of columns of order the stable rank of $A$. This improves on existing results by removing an extra logarithmic factor in the size of…

Functional Analysis · Mathematics 2018-07-19 Omer Friedland , Pierre Youssef

This paper studies approximation properties of linear sampling operators in general Banach lattices $X$. We obtain matching direct and inverse approximation estimates, convergence criteria, equivalence results involving special…

Functional Analysis · Mathematics 2026-01-28 Yurii Kolomoitsev

In this paper, we deal with the family of Steklov sampling operators in the general setting of Orlicz spaces. The main result of the paper is a modular convergence theorem established following a density approach. To do this, a Luxemburg…

Functional Analysis · Mathematics 2025-10-08 Danilo Costarelli , Erika Russo

We study approximation theorems for the Euler characteristic of the Vietoris-Rips and Cech filtration. The filtration is obtained from a Poisson or binomial sampling scheme in the critical regime. We apply our results to the smooth…

Probability · Mathematics 2021-09-21 Johannes Krebs , Benjamin Roycraft , Wolfgang Polonik

We propose using a discounted version of a convex combination of the log-likelihood with the corresponding expected log-likelihood such that when they are maximized they yield a filter, predictor and smoother for time series. This paper…

Methodology · Statistics 2026-04-29 Simon Donker van Heel , Neil Shephard