@Article{NM-15-357,
author = {G. Yu, L. Guan and Z. Wei },
title = {A globally convergent Polak-Ribière-Polyak conjugate gradient method with Armijo-type line search},
journal = {Numerical Mathematics, a Journal of Chinese Universities},
year = {2006},
volume = {15},
number = {4},
pages = {357--366},
abstract = {
In this paper, we propose a globally convergent
Polak-Ribi\`{e}re-Polyak (PRP) conjugate gradient method for
nonconvex minimization of differentiable functions by employing an
Armijo-type line search which is simpler and less demanding than
those defined in [4,10]. A favorite property of this method is that
we can
 choose the initial stepsize as the one-dimensional minimizer of a quadratic model
$\Phi(t):=f(x_k)+t g_k^Td_k+\frac{1}{2}t^2d_k^TQ_kd_k$, where $Q_k$
is a positive definite matrix that carries some second order
information of the objective function $f$. So, this line search may
make the stepsize $t_k$ more easily accepted.
 Preliminary numerical results show that this
method is efficient.
},
issn = {},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/nm/8042.html}
}