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

作者：Liu, J. K.^*; Feng, Y. M.; Zou, L. M.

来源：Computers & Mathematics with Applications, 2019, 77(3): 731-739.

DOI：10.1016/j.camwa.2018.10.002

• Unconstrained optimization
• Spectral conjugate gradient method
• Sufficient descent
• Standard Wolfe line search
• Global convergence

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 vertical bar beta(k) vertical bar <= beta(FR)(k) holds. The preliminary numerical results are presented to show effectiveness of the proposed method.

• 出版日期2019-2-1
• 单位重庆三峡学院