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A conjugate gradient method with sufficient descent and global convergence for unconstrained nonlinear optimization. (English) Zbl 1223.65044

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.

MSC:

65K05 Numerical mathematical programming methods
90C30 Nonlinear programming

Software:

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