Solving A Large Scale Nonlinear Unconstrained Optimization With Exact Line Search Direction By Using New Coefficient Of Conjugate Gradient Methods

摘要

Conjugate gradient (CG) methods are one of the tools in optimization. Due to its low computational memory requirement, this method is used in solving several of nonlinear unconstrained optimization problems from designs, economics, physics and engineering. In this paper, a new modification of CG family coefficient (β_k) is proposed and posses global convergence under exact line search direction. Numerical experimental results based on the number of iterations and central processing unit (CPU) time show that the newβ_k performs better than some other well known CG methods under some standard test functions.