A new edition of conjugate gradient methods for large-scale unconstrained optimization - Mustafa, Mamat; Ibrahim, Jusoh; Mohd, Rivaie (HIKARI Ltd, 2014)
Conjugate gradient (CG) methods are famous for solving nonlinear unconstrained optimization problems because they required low computational memory. In this paper, we propose a new CG coefficient ( βk ) which possesses global convergence properties using exact line search. The given method satisfies sufficient descent condition under strong Wolfe line search. Numerical results based on the number of iterations and central processing unit (CPU) time, have shown that the new βk performs better than some other well known CG methods.