An alternating structured trust region algorithm for separable optimization problems with nonconvex constraints

被引：2
|
作者：
Xue, Dan [1 ]
Sun, Wenyu [1 ]
Qi, Liqun [2 ]
机构：
[1] Nanjing Normal Univ, Sch Math Sci, Jiangsu Key Lab NSLSCS, Nanjing 210046, Jiangsu, Peoples R China
[2] Hong Kong Polytech Univ, Dept Appl Math, Kowloon, Hong Kong, Peoples R China
来源：
COMPUTATIONAL OPTIMIZATION AND APPLICATIONS | 2014年 / 57卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Nonconvex programming; Trust region methods; Alternating direction methods; Separable structure; Filter method; GLOBAL CONVERGENCE; DIRECTIONS METHOD; LEAST-SQUARES; MINIMIZATION; PENALTY;
D O I：
10.1007/s10589-013-9597-9
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：
In this paper, we propose a structured trust-region algorithm combining with filter technique to minimize the sum of two general functions with general constraints. Specifically, the new iterates are generated in the Gauss-Seidel type iterative procedure, whose sizes are controlled by a trust-region type parameter. The entries in the filter are a pair: one resulting from feasibility; the other resulting from optimality. The global convergence of the proposed algorithm is proved under some suitable assumptions. Some preliminary numerical results show that our algorithm is potentially efficient for solving general nonconvex optimization problems with separable structure.
引用
收藏
页码：365 / 386
页数：22
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