A Truncation Algorithm for Minimizing the Frobenius-Schatten Norm to Find a Sparse Matrix
- 作者: Wang L.1, Matveev I.1, Moroz I.2
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隶属关系:
- Nanjing University of Aeronautics and Astronautics
- Federal Research Center for Computer Science and Control
- 期: 卷 57, 编号 3 (2018)
- 页面: 434-442
- 栏目: Systems Analysis and Operations Research
- URL: https://journals.rcsi.science/1064-2307/article/view/220128
- DOI: https://doi.org/10.1134/S1064230718030097
- ID: 220128
如何引用文章
详细
A problem of optimizing a matrix sparse in the joint Frobenius-Schatten norm is considered. The least rows are proposed to be truncated according to the lower bound to fight the ill-conditionality of the matrix. Truncation not only helps avoid incorrect termination of the algorithm but it also reduces the computational complexity. Convergence analysis ensures that a truncation algorithm finds an approximate solution to the problem. The numerical experiments show the advantage of the truncation method over the previous algorithm.
作者简介
L. Wang
Nanjing University of Aeronautics and Astronautics
编辑信件的主要联系方式.
Email: wlpmath@nuaa.edu.cn
中国, Nanjing, 210016
I. Matveev
Nanjing University of Aeronautics and Astronautics
Email: wlpmath@nuaa.edu.cn
中国, Nanjing, 210016
I. Moroz
Federal Research Center for Computer Science and Control
Email: wlpmath@nuaa.edu.cn
俄罗斯联邦, Moscow, 119333