The nonlinear conjugate gradient method is a very useful technique for solving large scale minimization problems and has wide applications in many fields. In this paper, we present a new algorithm of nonlinear conjugate gradient method with strong convergence for unconstrained minimization problems. The new algorithm can generate an adequate trust region radius automatically at each iteration and has global convergence and linear convergence rate under some mild conditions. Numerical results show that the new algorithm is efficient in practical computation and superior to other similar methods in many situations.

Some computable schemes for descent methods without line search are proposed. Convergence properties are presented. Numerical experiments concerning large scale unconstrained minimization problems are reported.