Abstract: Modern directions in the development of matrix methods and their applications described in the present issue are overviewed. Special attention is given to methods associated with separation of variables, special decompositions of matrices and tensors implementing this technique, related algorithms, and their applications to multidimensional problems in computational mathematics, data analysis, and machine learning.
|Number of pages||5|
|Journal||Computational Mathematics and Mathematical Physics|
|Publication status||Published - May 2021|
- low-rank matrices
- machine learning
- tensor decompositions