A modified PRP conjugate gradient algorithm with nonmonotone line search for nonsmooth convex optimization problems

摘要

It is well-known that nonlinear conjugate gradient (CG) methods are preferred to solve large-scale smooth optimization problems due to their simplicity and low storage. However, the CG methods for nonsmooth optimization have not been studied. In this paper, a modified Polak–Ribière–Polyak CG algorithm which combines with a nonmonotone line search technique is proposed for nonsmooth convex minimization. The search direction of the given method not only possesses the sufficiently descent property but also belongs to a trust region. Moreover, the search direction has not only the gradients information but also the functions information. The global convergence of the presented algorithm is established under suitable conditions. Numerical results show that the given method is competitive to other three methods.