A Modified Hybrid Conjugate Gradient Method for Unconstrained Optimization | Zendy

Minglei Fang | Zendy; Min Wang | Zendy; Min Sun | Zendy; Rong Chen | Zendy

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A Modified Hybrid Conjugate Gradient Method for Unconstrained Optimization

Author(s) -

Minglei Fang,

Min Wang,

Min Sun,

Rong Chen

Publication year - 2021

Publication title -

journal of mathematics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.252

H-Index - 13

eISSN - 2314-4785

pISSN - 2314-4629

DOI - 10.1155/2021/5597863

Subject(s) - conjugate gradient method , nonlinear conjugate gradient method , conjugate residual method , derivation of the conjugate gradient method , mathematics , line search , gradient method , convergence (economics) , biconjugate gradient method , conjugate , mathematical optimization , line (geometry) , algorithm , gradient descent , computer science , mathematical analysis , artificial neural network , geometry , artificial intelligence , computer security , economics , radius , economic growth

The nonlinear conjugate gradient algorithms are a very effective way in solving large-scale unconstrained optimization problems. Based on some famous previous conjugate gradient methods, a modified hybrid conjugate gradient method was proposed. The proposed method can generate decent directions at every iteration independent of any line search. Under the Wolfe line search, the proposed method possesses global convergence. Numerical results show that the modified method is efficient and robust.

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