Image de couverture de livre

Improved Convergence and Approximation properties of Baskakov-Durrmeyer Operators

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Kaur, Jaspreet, Goyal, Meenu
Format:	Preprint
Publié:	2024
Sujets:	Numerical Analysis
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Asymptotic properties for a general class of Szasz-Mirakjan-Durrmeyer operators
par: Abel, Ulrich, et autres
Publié: (2024)

Fractional $α$-Bernstein-Kantorovich operators of order $β$: A new construction and approximation results
par: Kaur, Jaspreet, et autres
Publié: (2024)

On the $L^p$-Convergence and Denoising Performance of Durrmeyer-Type Max-Min Neural Network Operators
par: Şahin, Berke, et autres
Publié: (2026)

Commutativity and spectral properties for a general class of Szasz-Mirakjan-Durrmeyer operators
par: Abel, Ulrich, et autres
Publié: (2024)

Spectral Approximation to Fractional Integral Operators
par: Liu, Xiaolin, et autres
Publié: (2025)

On the representation of energy-preserving quadratic operators with application to Operator Inference
par: Gkimisis, Leonidas, et autres
Publié: (2025)

Exponential Convergence of Deep Composite Polynomial Approximation for Cusp-Type Functions
par: Yeon, Kingsley, et autres
Publié: (2025)

Approximation by Certain Complex Nevai Operators : Theory and Applications
par: Majethiya, Priyanka, et autres
Publié: (2025)

Convergence Analysis of Block Newton Methods for 1D Shallow Neural Network Approximation
par: Cai, Zhiqiang, et autres
Publié: (2026)

Universal Approximation of Operators with Transformers and Neural Integral Operators
par: Zappala, Emanuele, et autres
Publié: (2024)

Approximating Analytic Spectra of Hyperbolic Systems with Summation-by-Parts Finite Difference Operators
par: Erickson, Brittany A.
Publié: (2024)

A Numerical Study of Combining RBF Interpolation and Finite Differences to Approximate Differential Operators
par: Rogan, Adrijan, et autres
Publié: (2025)

On the Convergence of a Spline Collocation Method for Nonlinear Fractional Boundary Value Problems with the Riesz-Caputo Operator
par: Sorgentone, Chiara, et autres
Publié: (2026)

Approximation properties of neural ODEs
par: De Marinis, Arturo, et autres
Publié: (2025)

Numerical Approximations and Convergence Analysis of Piecewise Diffusion Markov Processes, with Application to Glioma Cell Migration
par: Buckwar, Evelyn, et autres
Publié: (2024)

A PDE Perspective on Approximating Nonlocal Periodic Operators with Applications on Neural Networks for Critical SQG Equations
par: Abdo, Elie, et autres
Publié: (2024)

Convergence Rates for the Trotter Splitting for Unbounded Operators
par: Becker, Simon, et autres
Publié: (2024)

Quantitative Approximation for Neural Operators in Nonlinear Parabolic Equations
par: Furuya, Takashi, et autres
Publié: (2024)

On the Approximation of Operator-Valued Riccati Equations in Hilbert Spaces
par: Cheung, James
Publié: (2023)

Guaranteed Approximation Bounds for Mixed-Precision Neural Operators
par: Tu, Renbo, et autres
Publié: (2023)

Weak Convergence of Finite Element Approximations of Stochastic Linear Schrödinger equation driven by additive Wiener noise
par: Prasad, Mangala
Publié: (2025)

Guaranteed Stable Quadratic Models and their applications in SINDy and Operator Inference
par: Goyal, Pawan, et autres
Publié: (2023)

CUR Low Rank Approximation of a Matrix at Sublinear Cost
par: Go, Soo, et autres
Publié: (2019)

On a New Modification of Baskakov Operators with Higher Order of Approximation
par: Gadjev, Ivan, et autres
Publié: (2024)

Convergence of Calderón residuals
par: Hiptmair, Ralf, et autres
Publié: (2025)

Operator SVD with Neural Networks via Nested Low-Rank Approximation
par: Ryu, J. Jon, et autres
Publié: (2024)

Fourier Neural Operators for Non-Markovian Processes:Approximation Theorems and Experiments
par: Lee, Wonjae, et autres
Publié: (2025)

A Posteriori Error Estimation Improved by a Reconstruction Operator for the Stokes Optimal Control Problem
par: Li, Jingshi, et autres
Publié: (2025)

Convergence Rates for Learning Pseudo-Differential Operators
par: Chen, Jiaheng, et autres
Publié: (2026)

Learning reduced-order Quadratic-Linear models in Process Engineering using Operator Inference
par: Gosea, Ion Victor, et autres
Publié: (2024)

QRP Variation of Cross--Approximation Iterations for Low Rank Approximation
par: Pan, Victor Y., et autres
Publié: (2018)

Approximating Numerical Fluxes Using Fourier Neural Operators for Hyperbolic Conservation Laws
par: Kim, Taeyoung, et autres
Publié: (2024)

A Deep Learning Framework for Multi-Operator Learning: Architectures and Approximation Theory
par: Weihs, Adrien, et autres
Publié: (2025)

Approximation with SiLU Networks: Constant Depth and Exponential Rates for Basic Operations
par: Ayena, Koffi O.
Publié: (2025)

Dimension-Free Convergence of Diffusion Models for Approximate Gaussian Mixtures
par: Li, Gen, et autres
Publié: (2025)

A Semi-Convergent Stage-Wise Framework with Provable Global Convergence for Adaptive Total Variation Regularization
par: Luo, Liang, et autres
Publié: (2025)

Convergence of the fully discrete JKO scheme
par: Hraivoronska, Anastasiia, et autres
Publié: (2025)

Convergence of a Ramshaw-Mesina Iteration
par: Çibik, Aytekin, et autres
Publié: (2024)

Convergence of the directional diffusion splitting method
par: Drebotiy, R., et autres
Publié: (2024)

Convergence of the Discrete Minimum Energy Path
par: Liu, Xuanyu, et autres
Publié: (2022)