New adaptive conjugate gradient methods choices for unconstrained optimization

In this paper, we present two conjugate gradient methods choices for solving unconstrained optimization problems. This attempts is to find suitable choices for parameter of a nonlinear conjugate gradient method proposed by Dai and Liao based on the matrix analysis and using the memoryless BFGS updating formula. Numerical results show that the proposed method is efficient for the unconstrained problems in the CUTEr collection.