Abstract
The conventional conjugate gradient method solves linear and quadratic optimization problems but most real life problems consist of nonquadratic functions of several variables. In this work a nonlinear conjugate gradient algorithm for solving large scale optimization problems is presented. The new algorithm is a modification of the Fletcher-Reeves conjugate gradient method and it is proved to achieve global convergence under the strong Wolfe-Powell inexact line search technique. Computational experiments show that the new algorithm presented performs better than the Fletcher-Reeves exact line search algorithm in solving high dimensional nonlinear optimization problems.
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