Dai–Liao extensions of a descent hybrid nonlinear conjugate gradient method with application in signal processing

Recently, Jian, Han and Jiang proposed a descent hybrid conjugate gradient method which is globally convergent without convexity assumption on the objective function, being also sensibly promising in computational point of view. Here, we develop one-parameter descent extensions of the method based on the Dai–Liao approach. We show that one of the given methods satisfies the sufficient descent condition when the parameter is chosen properly. Also, we establish global convergence of the method without convexity assumption. At last, practical merits of the methods are investigated by numerical experiments on a set of CUTEr test functions as well as the signal processing problems. The results show computational efficiency of the proposed methods.