Title : ( An extended Dai-Liao conjugate gradient method with global convergence for nonconvex functions )
Authors: Mohammad Reza Arazm , Saman Babaie–Kafaki , Reza Ghanbari ,
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Abstract
Using an extension of some previously proposed modified secant equations in the Dai-Liao approach, a modified nonlinear conjugate gradient method is proposed. As interesting features, the method employs the objective function values in addition to the gradient information and satisfies the sufficient descent property with proper choices for its parameter. Global convergence of the method is established without convexity assumption on the objective function. Results of numerical comparisons are reported. They demonstrate efficiency of the proposed method in the sense of the Dolan-Moré performance profile.
Keywords
, Unconstrained optimization, large-scale optimization, conjugate gradient method, sufficient descent property, nonconvexity, global convergence.
@article{paperid:1066986,
author = {Mohammad Reza Arazm and Saman Babaie–Kafaki and Ghanbari, Reza},
title = {An extended Dai-Liao conjugate gradient method with global convergence for nonconvex functions},
journal = {Glasnik Matematicki},
year = {2017},
volume = {52},
number = {2},
month = {July},
issn = {0017-095X},
pages = {361--375},
numpages = {14},
keywords = {Unconstrained optimization; large-scale optimization; conjugate gradient method; sufficient descent property; nonconvexity; global convergence.},
}
%0 Journal Article
%T An extended Dai-Liao conjugate gradient method with global convergence for nonconvex functions
%A Mohammad Reza Arazm
%A Saman Babaie–Kafaki
%A Ghanbari, Reza
%J Glasnik Matematicki
%@ 0017-095X
%D 2017
