Abstract
Conjugate gradient (CG) methods are important for large-scale unconstrained optimization due to its low memory requirements and global convergence properties. Numerous researches has been done to proposed new CG coefficients and to improve the efficiency. In this paper, we proposed a new CG coefficient based on the original Hestenes-Steifel CG coefficient. The global convergence result is established using exact line search. Most of our numerical results show that our method is very efficient when compared to the early CG coefficients for a given standard test problems.
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