Discrete mollification and automatic numerical differentiation

被引：60

作者：

Murio, DA ^{[1
]}

Mejia, CE

Zhan, S

机构：

[1] Univ Cincinnati, Dept Math Sci, Cincinnati, OH 45221 USA

[2] Univ Nacl Colombia, Dept Matemat, Medellin, Colombia

来源：

COMPUTERS & MATHEMATICS WITH APPLICATIONS | 1998年 / 35卷 / 05期

关键词：

ill-posed problems; numerical differentiation; automatic filtering;

D O I：

10.1016/S0898-1221(98)00001-7

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

An automatic method for numerical differentiation, based on discrete mollification and the principle of generalized cross validation is presented. With data measured at a discrete set of points of a given interval, the method allows for the approximate recovery of the derivative function an the entire domain. No information about the noise is assumed. Error estimates are included together with several numerical examples of interest.

引用

页码：1 / 16

页数：16

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