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\iteman{io-port 05119550}
\itemau{Sun, Qingying; Liu, Qiu}
\itemti{Global convergence of modified HS conjugate gradient method.}
\itemso{J. Appl. Math. Comput. 22, No. 3, 289-297 (2006).}
\itemab
Summary: In this paper, Hestenes-Stiefel (HS) conjugate gradient method for minimizing a continuously differentiable function $f$ on $\Bbb R^n$ is modified to have global convergence property. Firstly, it is shown that, using reverse modulus of continuity function and forcing function, the new method for solving unconstrained optimization can work for a continuously differentiable function with Curry-Altman's step size rule and a bounded level set. Secondly, by using comparing technique, some general convergence properties of the new method with Armijo step size rule are established. Numerical results show that the new algorithms are efficient.
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\itemut{Nonlinear programming; memory gradient method; Curry-Altman's step size rule; Armijo step size rule; convergence; numerical experiment}
\itemli{doi:10.1007/BF02832054}
\end