a hybrid conjugate gradient method based on an extended least-squares model

Inspired by andrei's approach of combining the conjugate gradient parameters convexly, a hybridization of the hestenes–stiefel and dai–yuan conjugate gradient methods is proposed. the hybridization parameter is determined by using the ellipsoid norm as an extension of the euclidean norm, in a least-squares framework. efficiency of the suggested hybrid conjugate gradient method in the sense of the dolan–moré performance profile is depicted as well.