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Memory gradient method with errors and generalized Armijo step
SUN Qing-ying, SANG Zhao-yang, Lv Wei
(College of Mathematics and Computational Science in China University of Petroleum,Dongying 257061,China)
Abstract:
A new class of memory gradient methods with errors and generalized Armijo step size rule were proposed for nonlinear unconstrained optimization assuming that the gradient of the function is uniformly continuous. Its global convergence property was proved. And a novel memory gradient method with quasi-Newton method and errors was given. Numerical results show that the new methods are efficient.
Key words:  unconstrained optimization  memory gradient method with errors  generalized Armijo step size rule  global convergence  numerical experiment