Dud, A Derivative-Free Algorithm for Nonlinear Least Squares
作者:
MaryL. Ralston,
RobertI. Jennrich,
期刊:
Technometrics
(Taylor Available online 1978)
卷期:
Volume 20,
issue 1
页码: 7-14
ISSN:0040-1706
年代: 1978
DOI:10.1080/00401706.1978.10489610
出版商: Taylor & Francis Group
关键词: Nonlinear Least Squares;Derivative-free;Fitting Differential Equations
数据来源: Taylor
摘要:
Derivative-free nonlinear least squares algorithms which make efficient use of function evaluations are important for fitting models defined by systems of nonlinear differential equations. A new Gauss-Newton-like algorithm with these properties is developed. The performance of the new algorithm (called Dud for “doesn't use derivatives”) is evaluated on a number of standard test problems from the literature. On these problems Dud competes favorably with even the best derivative-based algorithms.
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