TY - JOUR
ID - 668827
TI - A New Hybrid Conjugate Gradient Method Based on Secant Equation for Solving Large Scale Unconstrained Optimization Problems
JO - Iranian Journal of Optimization
JA - IJO
LA - en
SN - 2588-5723
AU - Salihu, Nasiru
AU - Odekunle, Mathew Remilekun
AU - Waziri, Mohammed Yusuf
AU - Halilu, Abubakar Sani
AD - Department of Mathematics, School of Physical Science, Moddibo Adama University of Technology, Yola.
AD - Department of Mathematics, School of Physical Sciences,
Modibbo Adama University of Technology, Yola, Nigeria.
AD - Department of Mathematical Sciences, Faculty of Sciences,
Bayero University, Kano, Nigeria.
AD - Department of Mathematics and Computer Science,
Sule Lamido University, Ka n Hausa, Nigeria.
Y1 - 2020
PY - 2020
VL - 12
IS - 1
SP - 33
EP - 44
KW - Unconstrained optimization
KW - conjugate gradient algorithm
KW - large scale optimization problem
KW - secant equation
KW - Global convergence
DO -
N2 - There exist large varieties of conjugate gradient algorithms. In order to take advantage of the attractive features of Liu and Storey (LS) and Conjugate Descent (CD) conjugate gradient methods, we suggest hybridization of these methods in which the parameter is computed as a convex combination of and respectively which the conjugate gradient (update) parameter was obtained from Secant equation. The algorithm generates descent direction and when the iterate jam, the direction satisfy sufficient descent condition. We report numerical results demonstrating the efficiency of our method. The hybrid computational scheme outperform or comparable with known conjugate gradient algorithms. We also show that our method converge globally using strong Wolfe condition.
UR - http://ijo.iaurasht.ac.ir/article_668827.html
L1 - http://ijo.iaurasht.ac.ir/article_668827_8b405eaf7329d8e6f787449dc8080e1b.pdf
ER -