A new family of spectral CG-algorithm

Abstract EN

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 α =1 at each iteration (where α is stepsize).

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.