Computational experience with globally convergent descent methods for large sparse systems of nonlinear equations*
作者:
L. Lukšan,
J. Vlček,
期刊:
Optimization Methods and Software
(Taylor Available online 1998)
卷期:
Volume 8,
issue 3-4
页码: 201-223
ISSN:1055-6788
年代: 1998
DOI:10.1080/10556789808805677
出版商: Gordon and Breach Science Publishers
关键词: Nonlinear Equations;Armijo-Type Descent Methods;Newton-Like Methods Inexact Methods;Global Convergence;Nonsymmetric Linear Systems;Conjugate Gradient Type Methods;Residual Smoothing;Computational Experiments
数据来源: Taylor
摘要:
This paper is devoted to globally convergent Armijo-type descent methods for solving large sparse systems of nonlinear equations. These methods include the discrete Newtcin method and a broad class of Newton-like methods based on various approximations of the Jacobian matrix. We propose a general theory of global convergence together with a robust algorithm including a special restarting strategy. This algorithm is based cfn the preconditioned smoothed CGS method for solving nonsymmetric systems of linejtr equations. After reviewing 12 particular Newton-like methods, we propose results of extensive computational experiments. These results demonstrate high efficiency of tip proposed algorithm
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