Open AccessThe convergence properties of a new hybrid conjugate gradient parameter for unconstrained optimization models
Author(s) -
Ibrahim Mohammed Sulaiman,
Mustafa Mamat,
Mohammed Yusuf Waziri,
Usman Abbas Yakubu,
Maulana Malik
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 - 10.1088/1742-6596/1734/1/012012
Subject(s) - conjugate gradient method , nonlinear conjugate gradient method , convergence (economics) , gradient descent , benchmark (surveying) , line search , conjugate residual method , conjugate , gradient method , computer science , derivation of the conjugate gradient method , algorithm , mathematical optimization , representation (politics) , mathematics , artificial neural network , artificial intelligence , mathematical analysis , politics , political science , law , computer security , radius , geodesy , geography , economics , economic growth
The hybrid conjugate gradient (CG) algorithms are among the efficient modifications of the conjugate gradient methods. Some interesting features of the hybrid modifications include inherenting the nice convergence properties and efficient numerical performance of the existing CG methods. In this paper, we proposed a new hybrid CG algorithm that inherits the features of the Rivaie et al. (RMIL*) and Dai (RMIL+) conjugate gradient methods. The proposed algorithm generates a descent direction under the strong Wolfe line search conditions. Preliminary results on some benchmark problems reveal that the proposed method efficient and promising.