Journal article

A globally convergent hybrid conjugate gradient method with strong Wolfe conditions for unconstrained optimization


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Publication Details

Author list: Kaelo P, Mtagulwa P, Thuto M

Publisher: Springer Nature Switzerland

Publication year: 2020

Volume number: 14

Start page: 1

End page: 9

Number of pages: 9

ISSN: 2251-7456

URL: https://link.springer.com/article/10.1007/s40096-019-00310-y

Languages: English



In this paper, we develop a new hybrid conjugate gradient method that inherits the features of the Liu and Storey (LS), Hestenes and Stiefel (HS), Dai and Yuan (DY) and Conjugate Descent (CD) conjugate gradient methods. The new method generates a descent direction independently of any line search and possesses good convergence properties under the strong Wolfe line search conditions. Numerical results show that the proposed method is robust and efficient.


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Last updated on 2022-29-11 at 11:35