使用可变缩放的不连续权函数的移动最小二乘逼近

IF 1.1 Q1 MATHEMATICS
Mohammad KARİMNEJAD ESFAHANİ, Stefano DE MARCHI, Francesco MARCHETTİ
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引用次数: 0

摘要

不连续函数出现在图像重构、信号处理、最优控制问题、接口问题、工程应用等诸多应用中。因此,这些函数的精确逼近和插值是非常重要的。在本文中,我们设计了一种移动最小二乘方法用于分散数据逼近,该方法将权重函数中的不连续性纳入其中。其思想是控制数据点对近似值的影响,不仅考虑到它们与评估点的距离,而且考虑到底层函数的不连续。我们还对一个合适的分段Sobolev空间给出了误差估计。数值实验结果与理论推导的收敛速率一致。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Moving least squares approximation using variably scaled discontinuous weight function
Functions with discontinuities appear in many applications such as image reconstruction, signal processing, optimal control problems, interface problems, engineering applications and so on. Accurate approximation and interpolation of these functions are therefore of great importance. In this paper, we design a moving least-squares approach for scattered data approximation that incorporates the discontinuities in the weight functions. The idea is to control the influence of the data sites on the approximant, not only with regards to their distance from the evaluation point, but also with respect to the discontinuity of the underlying function. We also provide an error estimate on a suitable piecewise Sobolev Space. The numerical experiments are in compliance with the convergence rate derived theoretically.
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来源期刊
Constructive Mathematical Analysis
Constructive Mathematical Analysis Mathematics-Analysis
CiteScore
2.40
自引率
0.00%
发文量
18
审稿时长
6 weeks
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