A modified BFGS method and its superlinear convergence in nonconvex minimization with general line search rule

被引：10
|
作者：
Guo Q. [1 ]
Liu J.-G. [2 ]
Wang D.-H. [3 ]
机构：
[1] School of Science, Dalian Nationalities University
[2] Institute of System Engineering, Dalian University of Technology
[3] Harbin Normal University
来源：
Journal of Applied Mathematics and Computing | 2008年 / 28卷 / 1-2期
关键词：
BFGS algorithm; Global convergence; Non-convex function; Quasi-Newton method; Unconstrained programme;
D O I：
10.1007/s12190-008-0117-5
中图分类号：
学科分类号：
摘要：
In this paper, a modification of the BFGS algorithm for unconstrained nonconvex optimization is proposed. The idea of the algorithm is to modify the approximate Hessian matrix for obtaining the descent direction and guaranteeing the efficacious of the new quasi-Newton iteration equation Bk+1 sk =yk, where yk is the sum of yk and tk||g(xk)||sk. The global convergence property of the algorithm associated with the general line search rule is prove. © 2008 Korean Society for Computational and Applied Mathematics.
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收藏
页码：435 / 446
页数：11
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