A globally convergent hybrid conjugate gradient method with strong Wolfe conditions for unconstrained optimization
Keyword(s):
Abstract 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.
2017 ◽
Vol 7
(2)
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pp. 177-185
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2019 ◽
Vol 11
(1)
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pp. 168781401882236
An efficient hybrid conjugate gradient method with descent properties under strong Wolfe line search
2021 ◽
Vol 1988
(1)
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pp. 012002
2014 ◽
Vol 989-994
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pp. 1802-1805
2018 ◽
Vol 106
(2)
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pp. 529-542