Some new scaled conjugate gradient methods via symmetric rank-one update for unconstrained optimization

This work demonstrates some of the scaled conjugate gradient algorithms based on order-one symmetric modernization. We adopted the outstanding attributes of the symmetric rank-one update (SR1) in providing superior Hessian approximations that lead to the development of a conjugate gradient with descent property and having no resources for the matrices. The obtained numerical results depict the superiority of the method.