Skip to main content
Log in

A nonnegativity preserving algorithm for multilinear systems with nonsingular \({\mathcal M}\)-tensors

  • Original Paper
  • Published:
Numerical Algorithms Aims and scope Submit manuscript

Abstract

This paper addresses multilinear systems of equations which arise in various applications such as data mining and numerical partial differential equations. When the multilinear system under consideration involves a nonsingular \({\mathscr{M}}\)-tensor and a nonnegative right-hand side vector, it may have multiple nonnegative solutions. In this paper, we propose an algorithm which can always preserve the nonnegativity of solutions. Theoretically, we show that the sequence generated by the proposed algorithm is a nonnegative componentwise nonincreasing sequence and converges to a nonnegative solution of the system. Numerical results further support the novelty of the proposed method.

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
Fig. 3

Similar content being viewed by others

Notes

  1. http://homepages.umflint.edu/~lxhan/software.html

References

  1. Bader, B.W., Kolda, T.G., et al.: MATLAB Tensor Toolbox Version 2.6. Available online. http://www.sandia.gov/tgkolda/TensorToolbox/ (2015)

  2. Ballani, J., Grasedyck, L.: A projection method to solve linear systems in tensor format. Numer. Linear Algebra Appl. 20(1), 27–43 (2013)

    Article  MathSciNet  Google Scholar 

  3. Berman, A., Plemmons, R.: Nonnegative matrices in the mathematical sciences. SIAM philadelphia (1994)

  4. Boussé, M., Vervliet, N., Domanov, I., Debals, O., De Lathauwer, L.: Linear systems with a canonical polyadic decomposition constrained solution: algorithms and applications. Numer. Linear Algebra Appl. 25(6), e2190 (2018)

    Article  MathSciNet  Google Scholar 

  5. Brazell, M., Li, N., Navasca, C., Tamon, C.: Solving multilinear systems via tensor inversion. SIAM J. Matrix Anal. Appl. 34(2), 542–570 (2013)

    Article  MathSciNet  Google Scholar 

  6. Ding, W., Qi, L., Wei, Y.: \({\mathscr{M}}\)-tensor and nonsingular \({\mathscr{M}}\)-tensors. Linear Algebra Appl. 439, 3264–3278 (2013)

    Article  MathSciNet  Google Scholar 

  7. Ding, W., Wei, Y.: Solving multilinear systems with \({\mathscr{M}}\)-tensors. J. Sci. Comput. 68, 689–715 (2016)

    Article  MathSciNet  Google Scholar 

  8. Du, S., Zhang, L., Chen, C., Qi, L.: Tensor absolute value equations. Sci. China Math. 61, 1695–1710 (2018)

    Article  MathSciNet  Google Scholar 

  9. Gowda, M., Luo, Z., Qi, L., Xiu, N.: Z-tensors and complementarity problems. arxiv:1510.07933v2 (2015)

  10. Han, L.: A homotopy method for solving multilinear systems with \({\mathscr{M}}\)-tensors. Appl. Math. Lett. 69, 49–54 (2017)

    Article  MathSciNet  Google Scholar 

  11. He, H., Ling, C., Qi, L., Zhou, G.: A globally and quadratically convergent algorithm for solving multilinear systems with \({\mathscr{M}}\)-tensors. J. Sci. Comput. 76, 1718–1741 (2018)

    Article  MathSciNet  Google Scholar 

  12. Li, D., Guan, H., Wang, X.: Finding a nonnegative solution to an M-tensor equation. arXiv:1811.11343 (2018)

  13. Li, D., Xie, S., Xu, H.: Splitting methods for tensor equations. Numer. Linear Algebra Appl. 24, e2102 (2017)

    Article  MathSciNet  Google Scholar 

  14. Li, W., Liu, D., Vong, S.: Comparison results for splitting iterations for solving multi-linear systems. Appl. Numer. Math. 134, 105–121 (2018)

    Article  MathSciNet  Google Scholar 

  15. Li, X., Ng, M.: Solving sparse non-negative tensor equations: algorithms and applications. Front. Math. China 10, 649–680 (2015)

    Article  MathSciNet  Google Scholar 

  16. Li, Z., Dai, Y., Gao, H.: Alternating projection method for a class of tensor equations. J. Comput. Appl. Math. 346, 490–504 (2019)

    Article  MathSciNet  Google Scholar 

  17. Liang, M., Zheng, B., Zhao, R.: Alternating iterative methods for solving tensor equations with applications. Numer. Algor. 80, 1437–1465 (2019)

    Article  MathSciNet  Google Scholar 

  18. Lim, L.: Singular values and eigenvalues of tensors: a variational approach. In: Proceedings of the IEEE International Workshop on Computational Advances in Multi-Sensor Addaptive Processing, CAMSAP05, pp 129-132, IEEE Computer Society Press, Piscataway (2005)

  19. Ling, C., Yan, W., He, H., Qi, L.: Further study on tensor absolute value equations. Sci China Math (2019)

  20. Liu, D., Li, W., Vong, S.: The tensor splitting with application to solve multi-linear systems. J. Comput. Appl. Math. 330, 75–94 (2018)

    Article  MathSciNet  Google Scholar 

  21. Luo, Z., Qi, L., Xiu, N.: The sparsest solution to Z-tensor complementarity problems. Optim. Lett. 11, 471–482 (2017)

    Article  MathSciNet  Google Scholar 

  22. Lv, C., Ma, C.: A Levenberg-Marquardt method for solving semi-symmetric tensor equations. J. Comput. Appl. Math. 332, 13–25 (2018)

    Article  MathSciNet  Google Scholar 

  23. Qi, L.: Eigenvalues of a real supersymmetric tensor. J. Symbolic Comput. 40(6), 1302–1324 (2005)

    Article  MathSciNet  Google Scholar 

  24. Qi, L., Luo, Z.: Tensor analysis: spectral theory and special tensors. SIAM philadelphia (2017)

  25. Wang, X., Che, M., Wei, Y.: Existence and uniqueness of positive solution for H+-tensor equations. Appl. Math. Lett. 98, 191–198 (2019)

    Article  MathSciNet  Google Scholar 

  26. Wang, X., Che, M., Wei, Y.: Neural networks based approach solving multi-linear systems with \({\mathscr{M}}\)-tensors. Neurocomputing 351, 33–42 (2019)

    Article  Google Scholar 

  27. Xie, Z., Jin, X., Wei, Y.: Tensor methods for solving symmetric \({\mathscr{M}}\)-tensor systems. J. Sci. Comput. 74, 412–425 (2018)

    Article  MathSciNet  Google Scholar 

  28. Yan, W., Ling, C., Ling, L., He, H.: Generalized tensor equations with leading structured tensors. Appl. Math. Comput. 361, 311–324 (2019)

    MathSciNet  MATH  Google Scholar 

  29. Zhang, L., Qi, L., Zhou, G.: \({\mathscr{M}}\),-tensors and some applications. SIAM J. Matrix Anal. Appl. 35(2), 437–452 (2014)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

The authors are grateful to Professor Donghui Li for his valuable comments on the convergence of our algorithm. Also, they would like to thank the referees for their close reading and valuable comments, which helped us improve the quality of this paper. H. He and C. Ling were supported in part by National Natural Science Foundation of China (Nos. 11771113 and 11971138) and Natural Science Foundation of Zhejiang Province (Nos. LY19A010019, LY20A010018, and LD19A010002).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hongjin He.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bai, X., He, H., Ling, C. et al. A nonnegativity preserving algorithm for multilinear systems with nonsingular \({\mathcal M}\)-tensors. Numer Algor 87, 1301–1320 (2021). https://doi.org/10.1007/s11075-020-01008-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11075-020-01008-2

Keywords

Mathematics Subject Classification (2010)

Navigation