弱Wolfe线搜索下一类含参量共轭梯度法的全局收敛性  

Global Convergence of a Class of Conjugate Gradient Algorithms Containing a Parameter with Weak Wolfe Line Search

作　　者：张秀军[1] 贾志刚[1] 

机构地区：[1]信阳师范学院数学与信息科学学院,河南信阳464000

出　　处：《怀化学院学报》2009年第5期24-27,共4页Journal of Huaihua University

基　　金：信阳师范学院青年科研基金项目(20080208)

摘　　要：基于算法的下降性要求给出了一类求解无约束优化问题的含参量共轭梯度类型公式和算法,并证明了该算法在弱Wolfe线搜索下的下降性和全局收敛性.数值实验结果表明算法是有效的.A new class of conjugate gradient algorithms containing a parameter for solving nonlinear unconstrained optimization problem was proposed based on the descent condition of the algorithm. The descent property and global convergence of the new conjugate gradient method with weak Wolfe line search were proved. The efficiency of the new method was proved by the numerical results.

关 键 词：无约束优化 共轭梯度法 弱Wolfe线搜索 全局收敛性 

分 类 号：O224[理学—运筹学与控制论]