Skip to main content
Log in

Separable Symmetric Tensors and Separable Anti-symmetric Tensors

  • Original Paper
  • Published:
Communications on Applied Mathematics and Computation Aims and scope Submit manuscript

Abstract

In this paper, we first initialize the S-product of tensors to unify the outer product, contractive product, and the inner product of tensors. Then, we introduce the separable symmetry tensors and separable anti-symmetry tensors, which are defined, respectively, as the sum and the algebraic sum of rank-one tensors generated by the tensor product of some vectors. We offer a class of tensors to achieve the upper bound for \(\texttt {rank}({\mathcal {A}}) \leqslant 6\) for all tensors of size \(3\times 3\times 3\). We also show that each \(3\times 3\times 3\) anti-symmetric tensor is separable.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Notes

  1. A general permutation tensor can be defined without the restriction of a constant dimension in the first m modes. Here we simplify it to fit our purpose.

References

  1. Baerheim, R.: Harmonic decomposition of the anisotropic elasticity tensor. Q. J. Mech. Appl. Math. 46, 391–418 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  2. Carroll, J.D., Chang, J.: Analysis of individual differences in multidimensional scaling via an n-way generalisation of “Eckart-Young” decomposition. Psychometrika 35, 283–319 (1970)

    Article  MATH  Google Scholar 

  3. Comon, P., Golub, G., Lim, L.-H., Mourrain, B.: Symmetric tensors and symmetric tensor rank. SIAM J. Matrix Anal. Appl. 30, 1254–1279 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  4. Gu, L., Wang, X., Zhang, G.: Quantum higher order singular value decomposition. In: 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), Bari, Italy, 6-9m October, 2019, Quantum Information Processing 20, 190 (2021)

  5. Harshman, R.A.: Foundations of the PARAFAC procedure: models and conditions for an “explanatory’’ multi-modal factor analysis. UCLA Working Pap. Phonet. 16, 1–84 (1970)

    Google Scholar 

  6. Hartley, R., Schaffalitzky, F.: Reconstruction from projections using Grassmann tensors. Int. J. Comput. Vision 83, 274–293 (2009)

    Article  MATH  Google Scholar 

  7. Hitchcock, F.L.: The expression of a tensor or a polyadic as a sum of products. J. Math. Phys. Camb. 6, 164–189 (1927)

    Article  MATH  Google Scholar 

  8. Hitchcock, F.L.: Multilple invariants and generalized rank of a p-way matrix or tensor. J. Math. Phys. Camb. 7, 39–70 (1927)

    Article  MATH  Google Scholar 

  9. Hu, S., Huang, Z., Ling, C., Qi, L.: On determinants and eigenvalue theory of tensors. J. Symbolic Comput. 50, 508–531 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  10. Kilmer, M.E., Martin, C.D.: Factorization strategies for third-order tensors. Linear Algebra Appl. 435, 641–658 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  11. Kolda, T.: Numerical optimization for symmetric tensor decomposition. Math. Program. 151, 225–248 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  12. Kolda, T., Bader, B.W.: Tensor decompositions and applications. SIAM Rev. 51, 455–500 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  13. Lavrauw, M., Pavan, A., Zanella, C.: On the rank of \(3\times 3\times 3\)-tensors. Linear Multilinear Algebra 61, 648–652 (2013)

    Article  MATH  Google Scholar 

  14. Lim, L.-H.: Singular values and eigenvalues of tensors: a variational approach. In: Proceedings of 1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), pp. 129–132 (2005)

  15. Qi, L.: Eigenvalues of a real supersymmetric tensor. J. Symb. Comput. 40, 1302–1324 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  16. Qi, L.: Symmetric nonnegative tensors and copositive tensors. Linear Algebra Appl. 439, 228–238 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  17. Qi, L., Zhang, G., Braun, D., Waldraff, F.B., Giraud, O.: Regularly decomposable tensors and classical spin states. Commun. Math. Sci. 15, 1651–1665 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  18. Qi, L., Luo, Z.: Tensor Analysis: Spectral Theory and Special Tensors. SIAM, Philadelpha (2017)

    Book  MATH  Google Scholar 

  19. Shashua, A., Hazan, T.: Non-negative tensor factorization with applications to statistics and computer vision. In: Proceedings of the 22nd International Conference on Machine Learning (ICML), Bonn, Germany, pp. 792–799 (2005)

  20. Shashua, A., Zass, R., Hazan, T.: Multi-way Clustering Using Super-symmetric Non-negative Tensor Factorization. In: Leonardis, A., Bischof, H., Pinz, A. (eds) Computer Vision–ECCV 2006. ECCV 2006. Lecture Notes in Computer Science, vol. 3954, pp. 595–608. Springer, Berlin, Heidelberg (2006)

  21. Vidal, R.: Generalized Principal Component Analysis (GPCA): an Algebraic Geometric Approach to Subspace Clustering and Motion Segmentation, Ph.D. Thesis. Electrical Engineering and Computer Sciences, University of California, Berkeley (2003)

  22. Wang, X., Gu, L., Lee, H., Zhang, G.: Quantum context-aware recommendation systems based on tensor singular value decomposition. Quant. Inf. Process 20, 190 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  23. Wang, X., Gu, L., Lee, H., Zhang, G.: Quantum tensor singular value decomposition. J. Phys. Commun. 5(7), 075001 (2021)

    Article  Google Scholar 

  24. Weinberg, S.: Gravitation and Cosmology: Principles and Applications of the General Theory of Relativity. Wiley, New York (1972)

    Google Scholar 

  25. Schulz, W.C.: Theory and Applications of Grassmann Algebra. Transgalactic Publishing Company, Flagstaff (2011)

    Google Scholar 

  26. Xu, C.: Tensor symmetrization and its applications in generalized principal component analysis, to appear in Pacific Journal of Optimizations, 18(3) (2022)

  27. Xu, C., He, L., Lin, Z.: Commutation matrices and commutation tensors. Linear Multilinear Algebra 68, 1721–1742 (2020)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Professor Fuzhen Zhang of Nova Southeastern University for his remarks for the proof of Theorem 8. Thanks are also given to the anonymous referees for their valuable suggestions and remarks which lead to the improvement of the manuscript.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Changqing Xu.

Ethics declarations

Conflict of Interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xu, C., Xu, K. Separable Symmetric Tensors and Separable Anti-symmetric Tensors. Commun. Appl. Math. Comput. 5, 1509–1523 (2023). https://doi.org/10.1007/s42967-022-00217-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42967-022-00217-x

Keywords

Mathematics Subject Classification

Navigation