A Truncation Algorithm for Minimizing the Frobenius-Schatten Norm to Find a Sparse Matrix
- Autores: Wang L.1, Matveev I.1, Moroz I.2
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Afiliações:
- Nanjing University of Aeronautics and Astronautics
- Federal Research Center for Computer Science and Control
- Edição: Volume 57, Nº 3 (2018)
- Páginas: 434-442
- Seção: Systems Analysis and Operations Research
- URL: https://journals.rcsi.science/1064-2307/article/view/220128
- DOI: https://doi.org/10.1134/S1064230718030097
- ID: 220128
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Resumo
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.
Sobre autores
L. Wang
Nanjing University of Aeronautics and Astronautics
Autor responsável pela correspondência
Email: wlpmath@nuaa.edu.cn
República Popular da China, Nanjing, 210016
I. Matveev
Nanjing University of Aeronautics and Astronautics
Email: wlpmath@nuaa.edu.cn
República Popular da China, Nanjing, 210016
I. Moroz
Federal Research Center for Computer Science and Control
Email: wlpmath@nuaa.edu.cn
Rússia, Moscow, 119333