Improved Fletcher–Reeves and Dai–Yuan conjugate gradient methods with the strong Wolfe line search

The conjugate gradient methods (CGMs) are very effective iterative methods for solving large-scale unconstrained optimization. The aim of this work is to improve the Fletcher–Reeves and Dai–Yuan CGMs. First, based on the conjugate parameters of the Fletcher–Reeves (FR) method and the Dai–Yuan (DY) method, and combining the second inequality of the strong Wolfe line search, two new conjugate parameters are constructed. Second, using the two new...