Uniqueness and perturbation bounds for sparse non-negative tensor equations

We discuss the uniqueness and the perturbation analysis for sparse non-negative tensor equations arriving from data sciences. By two different techniques, we may get better ranges of parameters to guarantee the uniqueness of the solution of the tensor equation. On the other hand, we present some perturbation bounds for the tensor equation. Numerical examples are given to show the efficiency of the theoretical results.

THE EXPLICIT SOLUTION OF HOMOGENEOUS LINEAR ORDINARY DIFFERENTIAL EQUATIONS WITH CONSTANT COEFFICIENTS

In this paper by using tensor analysis we give the explicit expressions of the solution of the initial-value problem of homogeneous linear differential equations with constant coefficients and the nth-order homogeneous linear differential equation with constant coefficients. In fact, we give the general formula for calculating the elements of the matrix exp[At] . We also give the results when the characteristic equation has the repeated roots. The present method is simpler and better than the other methods.

Relations between cubic equation, stress tensor decomposition, and von Mises yield criterion

Inspired by Cardano＇s method for solving cubic scalar equations, the addi- tive decomposition of spherical/deviatoric tensor （DSDT） is revisited from a new view- point. This decomposition simplifies the cubic tensor equation, decouples the spher- ical/deviatoric strain energy density, and lays the foundation for the von Mises yield criterion. Besides, it is verified that under the precondition of energy decoupling and the simplest form, the DSDT is the only possible form of the additive decomposition with physical meanings.

THE LINEAR BI-SPATIAL TENSOR EQUATIONφ_(ij)A^iXB^j= C

A linear bi-spatial tensor equation which contains many of ten encotuntered equations as particular cases is thoroughly studied Explicit solutions are obtained. No conditions on eigenvalues of coefficient tensors are imposed.

PRINCIPAL AXIS INTRINSIC METHOD AND THE HIGH DIMENSIONAL TENSOR EQUATION AX－XA＝C

The present paper spreads the principal axis intrinsic method to the highdimensional case and discusses the solution of the tensor equation AX --XA = C

Further study on tensor absolute value equations

In this paper,we consider the tensor absolute value equations(TAVEs),which is a newly introduced problem in the context of multilinear systems.Although the system of the TAVEs is an interesting generalization of matrix absolute value equations(AVEs),the well-developed theory and algorithms for the AVEs are not directly applicable to the TAVEs due to the nonlinearity(or multilinearity)of the problem under consideration.Therefore,we first study the solutions existence of some classes of the TAVEs with the help of degree theory,in addition to showing,by fixed point theory,that the system of the TAVEs has at least one solution under some checkable conditions.Then,we give a bound of solutions of the TAVEs for some special cases.To find a solution to the TAVEs,we employ the generalized Newton method and report some preliminary results.

Weighted Moore-Penrose inverses and fundamental theorem of even-order tensors with Einstein product

We treat even-order tensors with Einstein product as linear operators from tensor space to tensor space, define the mill spaces and the ranges of tensors, and study their relationship. We extend the fundamental theorem of linear algebra for matrix spaces to tensor spaces. Using the new relationship, we characterize the least-squares （M） solutions to a multilineax system and establish the relationship between the minimum-norm （N） leastsquares （M）solution of a multilinear system and the weighted Moore-Penrose inverse of its coefficient tensor. We also investigate a class of even-order tensors induced by matrices and obtain some interesting properties.

Generalized inverses of tensors via a general product of tensors

We define the {i}-inverse （i = 1,2, 5） and group inverse of tensors based on a general product of tensors. We explore properties of the generalized inverses of tensors on solving tensor equations and computing formulas of block tensors. We use the {1}-inverse of tensors to give the solutions of a multilinear system represented by tensors. The representations for the {1}-inverse and group inverse of some block tensors are established.

Reducible solution to a quaternion tensor equation

We establish necessary and sufficient conditions for the existence of the reducible solution to the quaternion tensor equation A*N C*NB=C via Einstein product using Moore-Penrose inverse,and present an expression of the reducible solution to the equation when it is solvable.Moreover,to have a general solution,we give the solvability conditions for the quaternion tensor equation A1*N C1*MB1+a1*C2*MB2+A2*NC3*MB2=e,which plays a key role in investigating the reducible solution to A*NC*NB=e.The expression of such a solution is also presented when the consistency conditions are met.In addition,we show a numerical example to illustrate this result.

On Nonnegative Solution of Multi-Linear System with Strong M_(z)-Tensors

A class of structured multi-linear system defined by strong M_(z)-tensors is considered.We prove that the multi-linear system with strong M_(z)-tensors always has a nonnegative solution under certain condition by the fixed point theory.We also prove that the zero solution is the only solution of the homogeneous multi-linear system for some structured tensors,such as strong M-tensors,H^(+)-tensors,strictly diagonally dominant tensors with positive diagonal elements.Numerical examples are presented to illustrate our theoretical results.