A spectral conjugate gradient method for solving large-scale unconstrained optimization.

Abstract This paper establishes a spectral conjugate gradient method for solving unconstrained optimization problems, where the conjugate parameter and the spectral parameter satisfy a restrictive relationship. The search direction is sufficient descent without restarts in per-iteration. Moreover, this feature is independent of any line searches. Under the standard Wolfe line searches, the global convergence of the proposed method is proved when | β k | ≤ β k F R holds. The preliminary numerical results are presented to show effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]