During the last years, low‐rank tensor approximation has been established as a new tool in
scientific computing to address large‐scale linear and multilinear algebra problems, which …

We investigate approximations to eigenfunctions of a certain class of elliptic operators in Rd
by finite sums of products of functions with separated variables and especially conditions …

Tensor completion is an important topic in the area of image processing and computer vision
research, which is generally built on extraction of the intrinsic structure of the tensor data …

We consider the problem of constructing a canonical polyadic (CP) decomposition for a
tensor network, rather than a single tensor. We illustrate how it is possible to reduce the …

Abstract (EN) Quantification of stochastic or quantum systems by a joint probability density or
wave function is a notoriously difficult computational problem, since the solution depends on …

The prevalence of accidents that raise substantial concerns, due to their potential to cause
severe injuries, loss of life, and long-term health implications for workers across various …

The ability to approximate a multivariate function/tensor as a sum of separable
functions/tensors is quite useful. Unfortunately, optimization-based algorithms to do so are …

High-dimensionality is one of the major challenges in kinetic modeling and simulation of
realistic physical systems. The most appropriate numerical scheme needs to balance …

Nonlinear least squares iterative solver is considered for real-valued sufficiently smooth
functions. The algorithm is based on successive solution of orthogonal projections of the …

A nonlinear least squares iterative solver developed earlier by the author is modified to fit
the derivative-free optimization paradigm. The proposed algorithm is based on easily …