Variational Methods for Non-Linear Least-Squares
作者:
AlM.,
FletcherR.,
期刊:
Journal of the Operational Research Society
(Taylor Available online 1985)
卷期:
Volume 36,
issue 5
页码: 405-421
ISSN:0160-5682
年代: 1985
DOI:10.1057/jors.1985.68
出版商: Taylor&Francis
关键词: non-linear least-squares;Gauss-Newton method;BFGS method;variational method;sizing;hybrid method;large residual problem
数据来源: Taylor
摘要:
AbstractWe consider Newton-like line search descent methods for solving non-linear least-squares problems. The basis of our approach is to choose a method, or parameters within a method, by minimizing a variational measure which estimates the error in an inverse Hessian approximation. In one approach we consider sizing methods and choose sizing parameters in an optimal way. In another approach we consider various possibilities for hybrid Gauss-Newton/BFGS methods. We conclude that a simple Gauss-Newton/BFGS hybrid is both efficient and robust and we illustrate this by a range of comparative tests with other methods. These experiments include not only many well known test problems but also some new classes of large residual problem.
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