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A Semidefinite Relaxation Method for Partially Symmetric Tensor Decomposition

Author

Listed:
  • Guyan Ni

    (Department of Mathematics, National University of Defense Technology, Changsha, Hunan 410073, China)

  • Ying Li

    (Department of Mathematics, National University of Defense Technology, Changsha, Hunan 410073, China)

Abstract

In this paper, we establish an equivalence relation between partially symmetric tensors and homogeneous polynomials, prove that every partially symmetric tensor has a partially symmetric canonical polyadic (CP)-decomposition, and present three semidefinite relaxation algorithms. The first algorithm is used to check whether there exists a positive partially symmetric real CP-decomposition for a partially symmetric real tensor and give a decomposition if it has. The second algorithm is used to compute general partial symmetric real CP-decompositions. The third algorithm is used to compute positive partially symmetric complex CP-decomposition of partially symmetric complex tensors. Because for different parameters s , m i , n i , partially symmetric tensors T ∈ S [ m ] F [ n ] represent different kinds of tensors. Hence, the proposed algorithms can be used to compute different types of tensor real/complex CP-decomposition, including general nonsymmetric CP-decomposition, positive symmetric CP-decomposition, positive partially symmetric CP-decomposition, general partially symmetric CP-decomposition, etc. Numerical examples show that the algorithms are effective.

Suggested Citation

  • Guyan Ni & Ying Li, 2022. "A Semidefinite Relaxation Method for Partially Symmetric Tensor Decomposition," Mathematics of Operations Research, INFORMS, vol. 47(4), pages 2931-2949, November.
    • Handle: RePEc:inm:ormoor:v:47:y:2022:i:4:p:2931-2949
    DOI: 10.1287/moor.2021.1231
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