T-positive semidefiniteness of third-order symmetric tensors and T-semidefinite programming

TL;DR

This paper introduces T-positive semidefiniteness for third-order symmetric tensors, extends properties of positive semidefinite matrices, and develops T-semidefinite programming, demonstrating its effectiveness in polynomial optimization.

Contribution

It defines T-positive semidefiniteness for third-order tensors, extends matrix semidefinite properties, and formulates TSDP, converting it into complex SDP for optimization applications.

Findings

TSDP can be solved via complex SDP conversion

TSDP relaxation outperforms traditional SDP in polynomial optimization

Preliminary results show effectiveness in finding global minima

Abstract

The T-product for third-order tensors has been used extensively in the literature. In this paper, we first introduce the first-order and second-order T-derivatives for the multi-vector real-valued function with the tensor T-product; and inspired by an equivalent characterization of a twice continuously T-differentiable multi-vector real-valued function being convex, we present a definition of the T-positive semidefiniteness of third-order symmetric tensors. After that, we extend many properties of positive semidefinite matrices to the case of third-order symmetric tensors. In particular, analogue to the widely used semidefinite programming (SDP for short), we introduce the semidefinite programming over the third-order symmetric tensor space (T-semidefinite programming or TSDP for short), and provide a way to solve the TSDP problem by converting it into an SDP problem in the complex domain. Furthermore, we give several examples which can be formulated (or relaxed) as TSDP problems, and report preliminary numerical results for two unconstrained polynomial optimization problems. Experiments show that finding the global minimums of polynomials via the TSDP relaxation outperforms the traditional SDP relaxation for the test examples.