Skip to main content
Log in

Successive Partial-Symmetric Rank-One Algorithms for Almost Unitarily Decomposable Conjugate Partial-Symmetric Tensors

  • Published:
Journal of the Operations Research Society of China Aims and scope Submit manuscript

Abstract

In this paper, we introduce the almost unitarily decomposable conjugate partial-symmetric tensors, which are different from the commonly studied orthogonally decomposable tensors by involving the conjugate terms in the decomposition and the perturbation term. We not only show that successive rank-one approximation algorithm exactly recovers the unitary decomposition of the unitarily decomposable conjugate partial-symmetric tensors. The perturbation analysis of successive rank-one approximation algorithm for almost unitarily decomposable conjugate partial-symmetric tensors is also provided to demonstrate the robustness of the algorithm.

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.

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. Siam Rev. 51(3), 455–500 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  2. Anandkumar, A., Ge, R., Hsu, D.J., Kakade, S.M., Telgarsky, M.: Tensor decompositions for learning latent variable models. J. Mach. Learn. Res. 15(1), 2773–2832 (2014)

    MathSciNet  MATH  Google Scholar 

  3. McCullagh, P.: Tensor methods in Statistics. Chapman and Hall, London (1987)

    MATH  Google Scholar 

  4. Batselier, K., Liu, H., Wong, N.: A constructive algorithm for decomposing a tensor into a finite sum of orthonormal rank-1 terms. SIAM J. Matrix Anal. Appl. 36(3), 1315–1337 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  5. Kolda, T.G.: Orthogonal tensor decompositions. SIAM J. Matrix Anal. Appl. 23(1), 243–255 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  6. Robeva, E.: Orthogonal decomposition of symmetric tensors. SIAM J. Matrix Anal. Appl. 37(1), 86–102 (2016)

    Article  MathSciNet  Google Scholar 

  7. Wang, L., Chu, M., Yu, B.: Orthogonal low rank tensor approximation: alternating least squares method and its global convergence. SIAM J. Matrix Anal. Appl. 36(1), 1–19 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  8. Kolda, T.G., Bader, B.W., Kenny, J.: Higher-order web link analysis using multilinear algebra. In: IEEE International Conference on Data Mining, IEEE Computer Society, pp. 242–249 (2005)

  9. Wang, Y., Qi, L.: On the successive supersymmetric rank-1 decomposition of higher-order supersymmetric tensors. Numer Linear Algebra Appl. 14(6), 503–519 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  10. Zhang, T., Golub, G.H.: Rank-one approximation to high order tensors. SIAM J. Matrix Anal. Appl. 23(2), 534–550 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  11. Mu, C., Hsu, D., Goldfarb, D.: Successive rank-one approximations for nearly orthogonally decomposable symmetric tensors. SIAM J. Matrix Anal. Appl. 36(4), 1638–1659 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  12. Kolda, T.G.: Symmetric Orthogonal Tensor Decomposition is Trivial (2015). arXiv:1503.01375

  13. Aittomaki, T., Koivunen, V.: Beampattern optimization by minimization of quartic polynomial. In: Proceedings of 2009 IEEE/SP 15th Workshop on Statistical Signal Processing, pp. 437–440 (2009)

  14. Hilling, J.J., Sudbery, A.: The geometric measure of multipartite entanglement and the singular values of a hypermatrix. J. Math. Phys. 51(7), 072102 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  15. Josz, C.: Application of Polynomial Optimization to Electricity Transmission Networks. Ph.D. Dissertation, Université Pierre et Marie Curie, Paris (2016)

  16. Boralevi, A., Draisma, J., Horobet, E., Robeva, E.: Orthogonal and unitary tensor decomposition from an algebraic perspective (2015). arXiv:1512.08031

  17. Jiang, B., Li, Z., Zhang, S.: Characterizing real-valued multivariate complex polynomials and their symmetric tensor representations. SIAM J. Matrix Anal. Appl. 37(1), 381–408 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  18. Mu, C., Hsu, D., Goldfarb, D.: Greedy approaches to symmetric orthogonal tensor decomposition. SIAM J. Matrix Anal. Appl. 38(4), 1210–1226 (2017)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tao-Ran Fu.

Additional information

This paper is dedicated to Professor Yin-Yu Ye in celebration of his 70th birthday.

This work was partially supported by the National Natural Science Foundation of China (No. 11571234).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fu, TR., Fan, JY. Successive Partial-Symmetric Rank-One Algorithms for Almost Unitarily Decomposable Conjugate Partial-Symmetric Tensors. J. Oper. Res. Soc. China 7, 147–167 (2019). https://doi.org/10.1007/s40305-018-0194-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40305-018-0194-6

Keywords

Mathematics Subject Classification

Navigation