Open AccessNew class of hybrid conjugate gradient coefficients with guaranteed descent and efficient line search
Author(s) -
Ibrahim Mohammed Sulaiman,
Sukono Sukono,
Sudradjat Supian,
Mustafa Mamat
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/621/1/012021
Subject(s) - conjugate gradient method , line search , convergence (economics) , nonlinear conjugate gradient method , gradient descent , descent (aeronautics) , conjugate residual method , class (philosophy) , line (geometry) , mathematical optimization , conjugate , computer science , representation (politics) , derivation of the conjugate gradient method , gradient method , mathematics , simplicity , scale (ratio) , algorithm , artificial neural network , artificial intelligence , mathematical analysis , engineering , computer security , geometry , physics , radius , philosophy , law , aerospace engineering , economic growth , epistemology , quantum mechanics , political science , politics , economics
Hybrid conjugate gradient (CG) techniques are one of the most prominent procedure for obtaining the solution of large-scale unconstrained optimization problems. This is due to its simplicity, global convergence, and low memory requirement. Numerous modifications have been done recently to improve the performance of these methods. In this paper, we proposed new class of hybrid CG coefficients with guaranteed descent under exact line search. Numerical results are presented to illustrate the efficiency of the proposed methodscompared to other classical CG coefficients.