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Combination of compressed sensing and the optimum interpolation approximation: Theory and application for fast 2D NMR measurement

International Conference on Digital Signal Processing, 2017
Yuichi Kida, Takuro Kida
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7. Application to linear interpolation and approximation theory

Modern Umbral Calculus, 2019
Francesco Aldo Costabile
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Interpolation and Approximation Theory

A Concise Introduction to Numerical Analysis, 2018
Anita Faul
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On the Limit Regularity in Sobolev and Besov Scales Related to Approximation Theory

Journal of Fourier Analysis and Applications, 2019
We study the interrelation between the limit $$L_p(\Omega )$$ L p ( Ω ) -Sobolev regularity $$\overline{s}_p$$ s ¯ p of (classes of) functions on bounded Lipschitz domains $$\Omega \subseteq \mathbb {R}^d$$ Ω ⊆ R d , $$d\ge 2$$ d ≥ 2 , and the limit ...
Petru A. Cioica-Licht, Markus Weimar
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Some Interpolation Analogues as a Tool for Signals Approximation in Network Technologies

2024 IEEE 5th International Conference on Advanced Trends in Information Theory (ATIT)
Processing of periodic signals is an important element of research in a number of applied disciplines. This includes astronomy, medicine, economics, network technologies, etc.
Olexandr Koval, Oleg Barabash, Yuriy Kharkevych, Andriy Makarchuk, I. Kal’chuk, Galyna Kharkevych   +5 more
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Faster Diffusion Models via Higher-Order Approximation

arXiv.org
In this paper, we explore provable acceleration of diffusion models without any additional retraining. Focusing on the task of approximating a target data distribution in $\mathbb{R}^d$ to within $\varepsilon$ total-variation distance, we propose a ...
Gen Li, Yuchen Zhou, Yuting Wei, Yuxin Chen   +3 more
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General superconvergence for kernel-based approximation

arXiv.org
Kernel interpolation is a fundamental technique for approximating functions from scattered data, with a well-understood convergence theory when interpolating elements of a reproducing kernel Hilbert space.
Toni Karvonen, G. Santin, T. Wenzel
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InterGCNet: An Interpolation Geometric Constructive Neural Network for Industrial Data Modeling

IEEE Transactions on Instrumentation and Measurement
For industrial data modeling, how to build data-driven models on resource-constrained industrial devices is a research hotspot. However, existing literature is either high resource consumption or poor prediction performance.
Jing Nan, Yan Qin, Anushiya Arunan, Wei Dai, Chau Yuen   +4 more
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On approximation theory of nonlocal differential operators

, 2021
Haicheng Yu, Shaofan Li
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Approximation and interpolation methods in the theory of entire functions of several variables

1972
Artykuł w: Annales Universitatis Mariae Curie-Skłodowska. Sectio A, Mathematica. Vol. 22/23/24 (1968/1969/1970), s. 7-15 ; streszcz. pol., ros. ; Artykuł w: Annales Universitatis Mariae Curie-Skłodowska. Sectio A, Mathematica. Vol. 22/23/24 (1968/1969/1970), s. 7-15 ; streszcz. pol., ros.
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