Journal of Applied Mathematics and Computation

ISSN Print: 2576-0645 Downloads: 63472 Total View: 1058110
Frequency: quarterly ISSN Online: 2576-0653 CODEN: JAMCEZ
Email: jamc@hillpublisher.com
Article 10.26855/jamc.2018.09.002

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
Email: snezanadjordjevic1971@gmail.com
Published: September 27,2018

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

 

How to cite this paper

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

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