An efficient conjugate gradient method with strong convergence properties for non-smooth optimization

In this paper, we introduce an efficient conjugate gradient method for solving nonsmooth optimization problems by using the moreau-yosida regularization approach. the search directions generated by our proposed procedure satisfy the sufficient descent property, and more importantly, belong to a suitable trust region. our proposed method is globally convergent under mild assumptions. our numerical comparative results on a collection of test problems show the efficiency and superiority of our proposed method. we have also examined the ability and the effectiveness of our approach for solving some real-world engineering problems from image processing field. the results confirm better performance of our method