Solving maximum-entropy sampling problems using factored masks

被引：0
|
作者：
Samuel Burer
Jon Lee
机构：
[1] University of Iowa,Department of Management Sciences
[2] IBM,Department of Mathematical Sciences, T.J. Watson Research Center
来源：
Mathematical Programming | 2007年 / 109卷
关键词：
Entropy; Branch and bound; Nonlinear programming; Eigenvalue; 90C26; 90C22; 90C27; 90C57;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
We present a practical approach to Anstreicher and Lee’s masked spectral bound for maximum-entropy sampling, and we describe favorable results that we have obtained with a Branch-and-Bound algorithm based on our approach. By representing masks in factored form, we are able to easily satisfy a semidefiniteness constraint. Moreover, this representation allows us to restrict the rank of the mask as a means for attempting to practically incorporate second-order information.
引用
收藏
页码：263 / 281
页数：18
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