Tensor train decomposition is widely used in machine learning and quantum physics due to
its concise representation of high-dimensional tensors, overcoming the curse of …

Surrogate models can reduce computational costs for multivariable functions with an
unknown internal structure (black boxes). In a discrete formulation, surrogate modeling is …

In this paper, we consider the three-order tensor recovery problem within the tensor tubal
rank framework. Most of the recent studies under this framework can not handle the tubal …

Affine sum-of-ranks minimization (ASRM) generalizes the affine rank minimization (ARM)
problem from matrices to tensors. Here, the interest lies in the ranks of a family $\mathcal {K} …

Оперативный мониторинг природной и техногенной среды на множестве длин волн
электромагнитного излучения обеспечивает получение уникальных данных …

This thesis concerns the optimization and application of low-rank methods, with a special
focus on tensor trains (TTs). In particular, we develop methods for computing TT …