@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} }