A Modified Hybrid Conjugate Gradient Method for Unconstrained Optimization
Minglei Fang, Min Wang, Min Sun, Rong Chen
Source abstract
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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