文献类型：期刊文献

英文题名：A conjugate gradient method with descent direction for unconstrained optimization

作者：Yuan, Gonglin[1];Lu, Xiwen[2];Wei, Zengxin[1]

机构：[1]Guangxi Univ, Coll Math & Informat Sci, Nanning 530004, Guangxi, Peoples R China;[2]E China Univ Sci & Technol, Sch Sci, Shanghai 200237, Peoples R China

年份：2009

卷号：233

期号：2

起止页码：519

外文期刊名：JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS

收录：;EI(收录号：20093612285006);WOS:【SCI-EXPANDED(收录号:WOS:000270619900039)】；

基金：This work is supported by China NSF grands 10761001 and the Scientific Research Foundation of Guangxi University (Grant No. X081082).

语种：英文

外文关键词：Search direction; Line search; Conjugate gradient method; Global convergence; Unconstrained optimization

摘要：A modified conjugate gradient method is presented for solving unconstrained optimization problems, which possesses the following properties: (i) The sufficient descent property is satisfied without any line search; (ii) The search direction will be in a trust region automatically; (iii) The Zoutendijk condition holds for the Wolfe-Powell line search technique; (iv) This method inherits an important property of the well-known Polak-Ribiere-Polyak (PRP) method: the tendency to turn towards the steepest descent direction if a small step is generated away from the solution, preventing a sequence of tiny steps from happening. The global convergence and the linearly convergent rate of the given method are established. Numerical results show that this method is interesting. (C) 2009 Elsevier B.V. All rights reserved.