A New Conjugate Gradient Trust Region Method and Its Convergence

摘要：

Conjugate gradient methods are widely used for large scale unconstrained optimization. A new class of conjugate gradient trust region method is proposed, in which trust region technique is used for guaranteeing the global convergence of the algorithm, and more utilizable information on conjugate gradient vectors is used for accelerating convergence of the algorithm. The global convergence, superlinear convergence and quadratic convergence properties of the algorithm are proved under favorable conditions, respectively. Numerical experiments show that the new algorithm is robust and effective.

关键词： unconstrained optimization conjugate gradient method global convergence convergence rate

作者: Yueting Yang Wenyu Li Jing Gao

作者单位: Department of Mathematics,Beihua University,Jilin City,132013,China

会议类型: 国际会议

会议名称: The Third International Joint Conference on Computational Science and Optimization(第三届计算科学与优化国际大会 CSO 2010)

会议地点: 黄山

会议语种:英文

页码: 38-41

在线出版日期: 2010-05-28（万方平台首次上网日期，不代表论文的发表时间）