摘要
针对无约束优化问题,通过修正共轭梯度参数,构造新的搜索方向,提出两类修正的WYL共轭梯度法.在每次迭代过程中,两类算法产生的搜索方向均满足充分下降性.在适当条件下,证明了算法的全局收敛性.数值结果表明算法是可行的和有效的.
In this paper,according to modified conjugate gradient parameter,two novel directions are developed and analyzed for classical WYL conjugate gradient method.Moreover,based on the directions,two modified WYL conjugate gradient methods are proposed for unconstrained optimization problems.Furthermore,the directions of this context are sufficient descents at each iteration.The globally convergent properties are proved under some suitable condition.Numerical results show that these methods are feasible and effective.
作者
孙颖异
李健
孙中波
王增辉
SUN Yingyi;LI Jian;SUN Zhongbo;WANG Zenghui(College of Information Technology,Jilin Agriculture University,Changchun 130118,China;Department of Control Engineering,Changchun University of Technology,Changchun 130012,China;College of Humanities and Sciences of Northeast Normal University,Changchun 130117,China)
出处
《应用数学》
CSCD
北大核心
2019年第2期415-422,共8页
Mathematica Applicata
基金
国家自然科学基金(61873304
11701209)
吉林省科技发展计划项目(20180201058GX
20190302025GX)
吉林省科技厅项目(2016052010JH)
中国博士后基金面上项目(2018M641784)
关键词
共轭梯度法
全局收敛
无约束优化
充分下降方向
Conjugate gradient method
Global convergence
Unconstrained optimization
Sufficient descent direction
