Abstract
In this paper, we combine the conjugate gradient method with the Barzilai and Borwein gradient method, and propose a Barzilai and Borwein scaling conjugate gradient method for nonlinear unconstrained optimization problems. The new method does not require to compute and store matrices associated with Hessian of the objective functions, and has an advantage of less computational efforts. Moreover, the descent direction property and the global convergence are established when the line search fulfills the Wolfe conditions. The limited numerical experiments and comparisons show that the proposed algorithm is potentially efficient.
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