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

页码：9 / 15

页数：7

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