A nonmonotone scaled conjugate gradient algorithm for large-scale unconstrained optimization

作者：Ou, Yigui^*; Zhou, Xin

来源：International Journal of Computer Mathematics, 2018, 95(11): 2212-2228.

DOI：10.1080/00207160.2017.1368498

• Unconstrained optimization
• nonmonotone line search
• scaled conjugate gradient algorithm
• convergence analysis
• numerical comparisons

This paper proposes a nonmonotone scaled conjugate gradient algorithm for solving large-scale unconstrained optimization problems, which combines the idea of scaled memoryless Broyden-Fletcher-Goldfarb-Shanno preconditioned conjugate gradient method with the nonmonotone technique. An attractive property of the proposed method is that the search direction always provides sufficient descent step at each iteration. This property is independent of the line search used. Under appropriate assumptions, the method is proven to possess global convergence for nonconvex smooth functions, and R-linear convergence for strongly convex functions. Preliminary numerical results and related comparisons show the efficiency of the proposed method in practical computation.

• 出版日期2018
• 单位海南大学