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Modified Conjugate Gradient Method for Unconstrained Optimization

by Thamera K. Alkhashab
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 86 - Number 15
Year of Publication: 2014
Authors: Thamera K. Alkhashab
10.5120/15065-3509

Thamera K. Alkhashab . Modified Conjugate Gradient Method for Unconstrained Optimization. International Journal of Computer Applications. 86, 15 ( January 2014), 42-46. DOI=10.5120/15065-3509

@article{ 10.5120/15065-3509,
author = { Thamera K. Alkhashab },
title = { Modified Conjugate Gradient Method for Unconstrained Optimization },
journal = { International Journal of Computer Applications },
issue_date = { January 2014 },
volume = { 86 },
number = { 15 },
month = { January },
year = { 2014 },
issn = { 0975-8887 },
pages = { 42-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume86/number15/15065-3509/ },
doi = { 10.5120/15065-3509 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:04:20.200112+05:30
%A Thamera K. Alkhashab
%T Modified Conjugate Gradient Method for Unconstrained Optimization
%J International Journal of Computer Applications
%@ 0975-8887
%V 86
%N 15
%P 42-46
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Conjugate gradient method holds an important role in solving unconstrained Optimizations , especially for large scale problems. Numerous studies and modific ations have been done to improve this method . In this paper , we propose a new conjugate gradient meth od which is computed by modifying Dai and Yuan formula . This new formula for the denominator is introduced and the numerator of Dai and Yuan for mula is retrained , but still possesses global converge nce properties. Numerical results based on number of iterations and number of function evaluations by usin g exact line search have shown that the new formul a is an efficient when we comparative it with the oth er conjugate gradient methods.

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Index Terms

Computer Science
Information Sciences

Keywords

Conjugate gradient methods global convergence unconstrained optimization exact line search

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