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Journal of Applied Mathematics and Computation

New Hybrid Conjugate Gradient Method as A Convex Combination of HS and FR Methods

Author:Snezana S. Djordjevic Date:September 27,2018 Hits:

Abstract

In this paper we present a new hybrid conjugate gradient algorithm for unconstrained optimization. This method is a convex combination of Hestenes-Stiefel conjugate gradient method and Fletcher-Reeves conjugate gradient method. The parameter  is chosen in such a way that the search direction satisfies the condition of the Newton direction. The strong Wolfe line search conditions are used. The global convergence of new method is proved.

Numerical comparisons show that the present hybrid conjugate gradient algorithm is the efficient one.

References

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New Hybrid Conjugate Gradient Method as A Convex Combination of HS and FR Methods
Snezana S. Djordjevic

1Faculty of Technology, University of Nis, 16000 Leskovac, Serbia
*Corresponding author: Snezana S. Djordjevic, Faculty of Technology, University of Nis, 16000 Leskovac, Serbia
Email: snezanadjordjevic1971@gmail.com
How to cite this paper: Snezana, S.D. (2018) New Hybrid Conjugate Gradient Method as A Convex Combination of HS and FR Methods. Journal of Applied Mathematics and Computation, 2(9), 366-378.
DOI: 10.26855/jamc.2018.09.002

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