Tensors, as geometric objects that describe linear or multi-linear relations between
geometric vectors, scalars, and other tensors, have provided a concise mathematical …

Finding the sparsest solutions to a tensor complementarity problem is generally NP-hard
due to the nonconvexity and noncontinuity of the involved ℓ _0 ℓ 0 norm. In this paper, a …

A great deal of attention has been paid to solve the system of tensor equations in recent
years for its applications in various fields. In this paper, the Kaczmarz-like method, which is …

In this paper, firstly, we introduce the variant tensor splittings, and present some equivalent
conditions for a strong M-tensor based on the tensor splitting. Secondly, the existence and …

In this paper, we equivalently reformulate the tensor complementarity problem as a system
of fixed point equations. Based on this system, we propose the (extend) randomized …

This work, with its three parts, reviews the state-of-the-art of studies for the tensor
complementarity problem and some related models. In the first part of this paper, we have …

In order to process the large-scale data, a useful tool in dimensionality reduction of a matrix,
the CUR decomposition has been developed, which can compress the huge matrix with its …

The notion of the Moore–Penrose inverse of a matrix has been extended to tensor case with
Einstein product, recently. In this paper, we continue this work and propose the full rank …

In the present paper, we are interested in developing iterative Krylov subspace methods in
tensor structure to solve a class of multilinear systems via the Einstein product. In particular …

Tensor systems involving tensor-vector products (or polynomial systems) are considered.
We solve these tensor systems, especially focusing on symmetric M M-tensor systems, by …