A Novel hybridization of CG-techniques for Solving Unconstrained Optimization Problems

Conjugate gradient methods are an extremely helpful way for handling large scale non-linear optimization issues. In this paper, based on the three famous Dai-yuan (DY), Liu–Storey (LS)and Conjugate-Descent (CD) conjugate gradient methods, a new hybrid CG method is proposed. Under strong wolf line search, we prove the sufficient descent and global convergence features. The new formula is more efficient than other traditional conjugate gradient approaches, according to numerical results.