CG_DESCENT is a state-of-the-art algorithm to solve large-scale unconstrained
minimization problems. However, research activities on CG_DESCENT in some other …

In this paper, based on the projection strategy, we propose a derivative-free iterative method
for large-scale nonlinear monotone equations with convex constraints, which can generate a …

In this paper, based on the three-term conjugate gradient method and the hybrid technique,
we propose a hybrid three-term conjugate gradient projection method by incorporating the …

In this paper, we present a projection method to solve monotone nonlinear equations with
convex constraints. This method can be viewed as an extension of CG_DESCENT method …

In this paper, a derivative-free conjugate gradient method for solving nonlinear equations
with convex constraints is proposed. The proposed method can be viewed as an extension …

It is well known that nonlinear conjugate gradient methods are very effective for large-scale
smooth optimization problems. However, their efficiency has not been widely investigated for …

In this paper, a conjugate gradient algorithm for systems of large-scale nonlinear equations
is designed by the following steps:(i) A three-terms conjugate gradient direction dk is …

In this work, we provide a note on the spectral gradient projection method for solving
nonlinear equations. Motivated by recent extensions of the spectral gradient method for …

In this paper, we propose a derivative-free method for solving large-scale nonlinear
monotone equations. It combines the modified Perry's conjugate gradient method (IE …

The goal of this paper is to extend the modified Hestenes-Stiefel method to solve large-scale
nonlinear monotone equations. The method is presented by combining the hyperplane …