This article is cited in 1 paper

General numerical methods

New applications of matrix methods

N. L. Zamarashkin^a, I. V. Oseledets^ab, E. E. Tyrtyshnikov<sup>a

a</sup> Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences, Moscow
^b Skoltech, Moscow, Russia

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.

Key words: low-rank matrices, tensor decompositions, machine learning.

UDC: 519.6

Received: 24.11.2020
Revised: 24.11.2020
Accepted: 14.01.2021

DOI: 10.31857/S0044466921050197

 English version: Computational Mathematics and Mathematical Physics, 2021, 61:5, 669–673

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