New search directions of conjugate gradient methods for unconstrained nonlinear optimization

Abstract

This thesis deals with solving the unconstrained nonlinear problems using conjugate gradient methods, where we proposed two new types of these methods. We established the descent condition for the search directions, and we proved the global convergence of the proposed algorithms under the strong Wolfe line search. The obtained results were further supported by various numerical experiments with large dimensions.