Open AccessGood Characteristics of The New Spectral Conjugate Gradient Method for Unconstrained Optimization
Author(s) -
Ahmed Hussien Sheekoo,
Ghada M. Al-Naemi
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1963/1/012079
Subject(s) - conjugate gradient method , nonlinear conjugate gradient method , benchmark (surveying) , convergence (economics) , line search , gradient descent , conjugate residual method , derivation of the conjugate gradient method , mathematical optimization , descent (aeronautics) , gradient method , property (philosophy) , computer science , nonlinear system , scale (ratio) , mathematics , work (physics) , artificial intelligence , artificial neural network , physics , philosophy , computer security , economic growth , geodesy , epistemology , quantum mechanics , economics , meteorology , radius , geography , thermodynamics
The spectral conjugate gradient (SCG) method is an effective method to solve large-scale nonlinear unconstrained optimization problems. In this work, we propose a new SCG method in which performance is numerically analyzed. We established the descent property and global convergence conditions based on assumptions through the strongWolfe-Powell line search. Numerical results were performed using benchmark functions widely used in many conventional functions to evaluate the efficiency of the proposed method. Subject Classification: 90C30, 90C06, 65K05, 65K10.