Comparison of the AIM conjugate gradient method under exact and inexact line search for solving unconstrained optimization problems - Za’aba, Fatin Nadhirah; Marjugi, Siti Mahani (Malaysian Mathematical Science Society, 2021)
The nonlinear conjugate gradient (CG) method is essential in solving large-scale unconstrained optimization problems due to its simplicity and low memory requirement. Numerous studies and improvements have been made recently to improve this strategy. Hence, this study will create a modified CG method with inexact line search, Strong Wolfe-Powell conditions. The global convergence and sufficient descent properties are established by using an inexact line search. The numerical result demonstrates that the modified method with inexact line search is superior and more efficient when compared to other CG methods.