A spectral PRP conjugate gradient methods for nonconvex optimization problem based on modified line search
Zhong Wan Chaoming Hu Zhanlu Yang
In this paper, a new spectral PRP conjugate gradient algorithm is developed for solving nonconvex unconstrained optimization problems. The search direction in this algorithm is proved to be a sufficient descent direction of the objective function independent of line search. To rule out possible unacceptably short step in the Armijo line search, a modified Armijo line search strategy is presented. The obtained step length is improved by employing the properties of the approximate Wolfe conditions. Under some suitable assumptions, the global convergence of the developed algorithm is established. Numerical experiments demonstrate that this algorithm is promising.
keywords: sufficiently descent direction line search Unconstrained optimization conjugate gradient global convergence.

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