A family of supermemory gradient projection methods for constrained optimization

被引：5
|
作者：
Wang, YJ [1 ]
Wang, CY
Xiu, NH
机构：
[1] Qufu Normal Univ, Inst Operat Res, Qufu 273165, Shandong Prov, Peoples R China
[2] Nanjing Normal Univ, Sch Math & Comp Sci, Nanjing 210097, Peoples R China
[3] No Jiaotong Univ, Dept Appl Math, Beijing 100044, Peoples R China
来源：
OPTIMIZATION | 2002年 / 51卷 / 06期
基金：
中国国家自然科学基金;
关键词：
projection; supermemory gradient; stationary point;
D O I：
10.1080/0233193021000066464
中图分类号：
C93 [管理学]; O22 [运筹学];
学科分类号：
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要：
A family of supermemory gradient projection methods for solving the convex constrained optimization problem is presented in this article. It is proven to have stronger convergence properties than the traditional gradient projection method. In particular, it is shown to be globally convergent if the objective function is convex.
引用
收藏
页码：889 / 905
页数：17
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