New Applications of Matrix Methods

N. L. Zamarashkin, I. V. Oseledets, E. E. Tyrtyshnikov

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)669-673
Number of pages5
JournalComputational Mathematics and Mathematical Physics
Issue number5
Publication statusPublished - May 2021


  • low-rank matrices
  • machine learning
  • tensor decompositions


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