A Combination of FR and HS Coefficient in Conjugate Gradient Method for Unconstrained Optimization
The conjugate gradient (CG) method is one of the most popular methods for solving large-scale problems of unconstrained optimization. In this paper, a new CG method based on combination of two classical CG methods of Fletcher-Reeves (FR), and Hestence-Stiefel (HS) is proposed. This method possess the global convergence properties and the sufficient descent condition. The tests of the new CG method by using MATLAB are measured in terms of central processing unit (CPU) time and iteration numbers with strong Wolfe-Powell inexact line search. Results presented have shown that the new CG method performs better compare to other CG methods.
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