Low-rank Tucker and CP tensor decompositions are powerful tools in data analytics. The
widely used alternating least squares (ALS) method, which solves a sequence of over …

The randomization-based feedforward neural network has raised great interest in the
scientific community due to its simplicity, training speed, and accuracy comparable to …

Molecular vibrational frequency analysis plays an important role in theoretical and
computational chemistry. However, in many cases, the analytical frequencies are …

We discretise a tangential tensor field equation using a surface-finite element approach with
a penalisation term to ensure almost tangentiality. It is natural to measure the quality of such …

Galerkin-based reduced-order models (G-ROMs) have provided efficient and accurate
approximations of laminar flows. In order to capture the complex dynamics of the turbulent …

The widely used alternating least squares (ALS) algorithm for the canonical polyadic (CP)
tensor decomposition is dominated in cost by the matricized-tensor times Khatri-Rao product …

The CP tensor decomposition is used in applications such as machine learning and signal
processing to discover latent low‐rank structure in multidimensional data. Computing a CP …

High-order optimization methods, including Newton's method and its variants as well as
alternating minimization methods, dominate the optimization algorithms for tensor …

We consider the problem of low-rank approximation of massive dense nonnegative tensor
data, for example, to discover latent patterns in video and imaging applications. As the size …

Canonical polyadic decomposition (CPD) is prevalent in chemometrics, signal processing,
data mining, and many more fields. While many algorithms have been proposed to compute …