Semidefinite Relaxation Methods for Tensor Absolute Value Equations

In this paper, we consider the tensor absolute value equations (TAVEs). When one tensor is row diagonal with odd order, we show that the TAVEs can be reduced to an algebraic equation; when it is row diagonal and nonsingular with even order, we prove that the TAVEs is equivalent to a polynomial complementary problem. When no tensor is row diagonal, we formulate the TAVEs equivalently as polynomial optimization problems in two different ways. Each of them can be solved by Lasserre’s hierarchy of semidefinite relaxations. The finite convergence properties are also discussed. Numerical experiments show the efficiency of the proposed methods.