DocumentCode :
175723
Title :
Two modified PRP conjugate gradient methods with sufficient descent property for unconstrained optimization
Author :
Yubin Zhou ; Zhongbo Sun ; Xudong Shi ; Yinghui Teng
Author_Institution :
Coll. of Humanities & Sci., Northeast Normal Univ., Changchun, China
fYear :
2014
fDate :
May 31 2014-June 2 2014
Firstpage :
1005
Lastpage :
1009
Abstract :
In this paper, we propose two modified conjugate gradient methods, which produce sufficient descent direction at every iteration. The theoretical analysis shows that the algorithms are global convergence under some suitable conditions. The numerical results show that both algorithms are efficient for the given test problems from the Matlab library.
Keywords :
conjugate gradient methods; convergence; iterative methods; optimisation; Matlab library; descent property; global convergence; iteration; modified PRP conjugate gradient methods; unconstrained optimization; Algorithm design and analysis; Convergence; Educational institutions; Equations; Gradient methods; MATLAB; Conjugate gradient method; Global convergence; Sufficient descent direction; Unconstrained optimization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control and Decision Conference (2014 CCDC), The 26th Chinese
Conference_Location :
Changsha
Print_ISBN :
978-1-4799-3707-3
Type :
conf
DOI :
10.1109/CCDC.2014.6852311
Filename :
6852311
Link To Document :
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