摘要
共轭梯度法是求解大规模无约束优化问题有效方法之一.本文首先概述共轭梯度法基本理论、算法框架和收敛性分所需的假设和收敛性定理,然后从经典共轭梯度法的改进和特殊共轭梯度法两方面介绍了光滑无约束优化共轭梯度法的一些最新研究进展.最后,探讨了共轭梯度法可能的研究方向.
The conjugate gradient method（CGM） is one of the effective methods for solving large-scale unconstrained optimization problems.This paper firstly gives an introduction to the basic theory of CGMs,algorithm framework and the assumption and theory of convergence property.Then,it introduces some recent research processes in smooth unconstrained CGMs from two cases,the improvement of classical CGMs and special CGMs.Finally,some research directions of CGMs are pointed out.
出处
《玉林师范学院学报》
2016年第2期3-10,共8页
Journal of Yulin Normal University
基金
广西自然科学基金项目(2013GXNSFFAA019009)
