A New Hybrid HS-DY Conjugate Gradient Method

摘要：

Conjugate gradient method is one of the most useful methods for solving unconstrained optimization problem. In this paper, we propose a hybrid conjugate gradient method for unconstrained optimization based on the Hestenes-Stiefel and Dai-Yuan conjugate gradient Algorithms. By searching a particular direction, the new algorithm satisfies the descent condition. Furthermore under the Wolfe line search conditions, we prove that the new method can support the global convergence. The initial numerical experiments show that the new algorithm is efficient.

关键词： Unconstrained optimization Conjugate gradient method Wolfe line search Global convergence

作者: Junli Dong Baocong Jiao Lanping Chen

作者单位: School of Mathematical Sciences, Capital Normal University Beijing, 100048, China

会议类型: 国际会议

会议名称: The Fourth International Joint Conference on Computational Science and Optimization(第四届计算科学与优化国际大会 CSO 2011)

会议地点: 昆明、丽江

会议语种:英文

页码: 94-98

在线出版日期: 2011-04-15（万方平台首次上网日期，不代表论文的发表时间）