A Truncation Algorithm for Minimizing the Frobenius-Schatten Norm to Find a Sparse Matrix
- Авторлар: Wang L.1, Matveev I.1, Moroz I.2
-
Мекемелер:
- Nanjing University of Aeronautics and Astronautics
- Federal Research Center for Computer Science and Control
- Шығарылым: Том 57, № 3 (2018)
- Беттер: 434-442
- Бөлім: Systems Analysis and Operations Research
- URL: https://journals.rcsi.science/1064-2307/article/view/220128
- DOI: https://doi.org/10.1134/S1064230718030097
- ID: 220128
Дәйексөз келтіру
Аннотация
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.
Авторлар туралы
L. Wang
Nanjing University of Aeronautics and Astronautics
Хат алмасуға жауапты Автор.
Email: wlpmath@nuaa.edu.cn
ҚХР, Nanjing, 210016
I. Matveev
Nanjing University of Aeronautics and Astronautics
Email: wlpmath@nuaa.edu.cn
ҚХР, Nanjing, 210016
I. Moroz
Federal Research Center for Computer Science and Control
Email: wlpmath@nuaa.edu.cn
Ресей, Moscow, 119333