A new CG algorithm based on a scaled memoryless BFGS update with adaptive search strategy, and its application to large-scale unconstrained optimization problems

Abstract

Conjugate gradient method is an effective method for solving large-scale unconstrained optimization problems. This paper proposes a new conjugate gradient algorithm based on the self-scaling memoryless BFGS update, which uses the Wolfe line search. The descent and global convergence of the method are given under mild conditions. Numerical experiments show that our new conjugate gradient method is effective.