Abstract
A new family of CG –algorithms for large-scale unconstrained optimization is introduced in this paper using the spectral scaling for the search directions, which is a generalization of the spectral gradient method proposed by Raydan [14].
Two modifications of the method are presented, one using Barzilai line search, and the others take at each iteration (where is step- size). In both cases tested for the Wolfe conditions, eleven test problems with different dimensions are used to compare these algorithms against the well-known Fletcher –Revees CG-method, with obtaining a robust numerical results.
