Most important computational problems nowadays are those related to processing of the
large data sets and to numerical solution of the high-dimensional integral-differential
equations. These problems arise in numerical modeling in quantum chemistry, material science,
and multiparticle dynamics, as well as in machine learning, computer simulation of stochastic
processes and many other applications related to big data analysis.
Modern tensor numerical methods enable solution of the multidimensional
partial differential equations (PDE) in {\mathbb{R}^{d}} by reducing them to one-dimensional
calculations.
Thus, they allow to avoid the so-called “curse of dimensionality”, i.e. exponential
growth of the computational complexity in the dimension size d, in the course of numerical
solution of high-dimensional problems.
At present, both tensor numerical methods and multilinear algebra
of big data continue
to expand actively to further theoretical and applied research topics.
This issue of CMAM is devoted to the recent developments in the theory of tensor
numerical methods and their applications in scientific computing and data analysis.
Current activities in this emerging field on the effective numerical modeling
of temporal and stationary multidimensional PDEs and beyond are presented in the following
ten articles, and some future trends are highlighted therein.