A decomposition method for large-scale box constrained optimization

被引：39
|
作者：
Yu, Jing [1 ]
Li, Mingqiang [2 ]
Wang, Yongli [2 ]
He, Guoping [2 ]
机构：
[1] Beihang Univ, Sch Econ & Management, Beijing 100191, Peoples R China
[2] Shandong Univ Sci & Technol, Coll Informat Sci & Engn, Qingdao 266510, Peoples R China
来源：
APPLIED MATHEMATICS AND COMPUTATION | 2014年 / 231卷
基金：
中国国家自然科学基金;
关键词：
Box constrained optimization; Decomposition method; Working set identification; Large-scale; KKT-violating index; TRUST REGION ALGORITHMS; POINT NEWTON METHODS; STRICT COMPLEMENTARITY; CONVERGENCE; MINIMIZATION; BOUNDS;
D O I：
10.1016/j.amc.2013.12.169
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
A decomposition method for solving large-scale box constrained optimization is proposed. The algorithm is motivated by the successful use of the decomposition method presented by Joachims for training support vector machines. In particular, a new technique, based on the new definition "KKT-violating index", is introduced for working set identification. Finally, the numerical experiments and implementation details show that this method is practical for large-scale problems. (C) 2014 Elsevier Inc. All rights reserved.
引用
收藏
页码：9 / 15
页数：7
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