A SCALED CONJUGATE GRADIENT METHOD WITH MOVING ASYMPTOTES FOR UNCONSTRAINED OPTIMIZATION PROBLEMS
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所属单位：理学院
发表刊物：JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION
关键字：Scaling conjugate gradient moving asymptotes the Wolfe condition unconstrained optimization
摘要：In this paper, a scaled method that combines the conjugate gradient with moving asymptotes is presented for solving the large-scaled nonlinear unconstrained optimization problem. A diagonal matrix is obtained by the moving asymptote technique, and a scaled gradient is determined by multiplying the gradient with the diagonal matrix. The search direction is either a scaled conjugate gradient direction or a negative scaled gradient direction under different conditions. This direction is sufficient descent if the step size satisfies the strong Wolfe condition. A global convergence analysis of this method is also provided. The numerical results show that the scaled method is efficient for solving some large-scaled nonlinear problems.
ISSN号：1547-5816
是否译文：否
发表时间：2017-04-01
合写作者：Zhou, Guanghui,Zeng, Meilan
通讯作者：倪勤