Liu, Hailin; Cheng, Sui Sun; Li, Xiaoyong A conjugate gradient method with sufficient descent and global convergence for unconstrained nonlinear optimization. (English) Zbl 1223.65044 Appl. Math. E-Notes 11, 139-147 (2011). The authors propose some conjugate gradient methods in solving the unconstrained optimization problem with general nonlinear functions by using an inexact line search. The strong convexity of the objective function is not required. The global convergence of the methods is established. Some numerical examples are presented to show that the proposed methods are competitive among other conjugate gradient methods for unconstrained optimization. Reviewer: Hang Lau (Montréal) MSC: 65K05 Numerical mathematical programming methods 90C30 Nonlinear programming Keywords:unconstrained optimization; conjugate gradient methods; global convergence; inexact line search; numerical examples Software:CG_DESCENT PDFBibTeX XMLCite \textit{H. Liu} et al., Appl. Math. E-Notes 11, 139--147 (2011; Zbl 1223.65044) Full Text: EuDML EMIS