A modifications of conjugate gradient method for unconstrained optimization problems
DOI:
https://doi.org/10.14419/ijet.v7i2.14.11146Keywords:
Conjugate Gradient Parameter, Inexact Line Search, Strong Wolfe-Powell Line Search, Global Convergence, Unconstrained Optimization.Abstract
The Conjugate Gradient (CG) methods play an important role in solving large-scale unconstrained optimization problems. Several studies have been recently devoted to improving and modifying these methods in relation to efficiency and robustness. In this paper, a new parameter of CG method has been proposed. The new parameter possesses global convergence properties under the Strong Wolfe-Powell (SWP) line search. The numerical results show that the proposed formula is more efficient and robust compared with Polak-Rribiere Ployak (PRP), Fletcher-Reeves (FR) and Wei, Yao, and Liu (WYL) parameters.
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Received date: April 6, 2018
Accepted date: April 6, 2018
Published date: April 6, 2018