A new family of conjugate gradient methods for small-scale unconstrained optimization

Abstract

Conjugate gradient (CG) methods are famous for solving nonlinear unconstrained optimization problems because they only need low computer memory. In this paper, we propose a new CG coefficient (βk) which has global convergence properties using exact line search. Numerical results based on the number of iterations have shown that the new βk performs better than some other CG methods.