We present efficient and scalable parallel algorithms for performing mathematical operations
for low-rank tensors represented in the tensor train (TT) format. We consider algorithms for …

Sparse tensor contractions appear commonly in many applications. Efficiently computing a
two sparse tensor product is challenging: It not only inherits the challenges from common …

Tensor analysis can comprehensively retain multidomain characteristics, which has been
employed in EEG studies. However, existing EEG tensor has large dimension, making it …

This paper studies a general framework for high-order tensor SVD. We propose a new
computationally efficient algorithm, tensor-train orthogonal iteration (TTOI), that aims to …

The tensor cross interpolation (TCI) algorithm is a rank-revealing algorithm for decomposing
low-rank, high-dimensional tensors into tensor trains/matrix product states (MPS). TCI learns …

Sparse tensor contraction sequence has been widely employed in many fields, such as
chemistry and physics. However, how to efficiently implement the sequence faces multiple …

Hyperspectral superresolution reconstruction technique obtains a high-resolution
hyperspectral image (HR-HSI) by fusing both a low-resolution hyperspectral image (LR-HSI) …

An efficient algorithm for compressing a given many-body potential energy surface (PES) of
molecular systems into a grid-based matrix product operator (MPO) is proposed. The PES is …

Tensor train decomposition is a powerful tool for dealing with high-dimensional, large-scale
tensor data, which is not suffering from the curse of dimensionality. To accelerate the …

We study low rank approximation of tensors, focusing on the tensor train and Tucker
decompositions, as well as approximations with tree tensor networks and more general …