Abstract
The tensor equation \(\mathcal {A}{\varvec{x}}^{m-1}={\varvec{b}}\) with the tensor \(\mathcal {A}\) of order m and dimension n and the vector \({\varvec{b}}\), has practical applications in several fields including signal processing, high-dimensional PDEs, high-order statistics, and so on. In this paper, a class of exponential accelerated iterative methods is proposed for solving the tensor equation mentioned above in the sense that the coefficient tensor \(\mathcal {A}\) is a symmetric and nonsingular or singular \(\mathcal {M}\)-tensor. The obtained iterative schemes involve the classical Newton’s method as a special case. It is shown that the proposed method for nonsingular case is superlinearly convergent, while for singular cases, it is linearly convergent. The performed numerical experiments demonstrate that our methods outperform some existing ones.
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References
Kolda, T., Bader, B.: Tensor decompositions and applications. SIAM Rev. 51, 455–500 (2009)
Lathauwer, L., Castaing, J., Cardoso, J.: Fourth-order cumulant-based blind identification of underdetermined mixtures. IEEE Trans. Sig. Proc. 55, 2965–2973 (2007)
Qi, L., Luo, Z.: Tensor analysis: spectral theory and special tensors. SIAM, Philadelphia (2017)
Qi, L.: Eigenvalues of a real supersymmetric tensor. J. Symb. Comput. 40, 1302–1324 (2005)
Ding, W., Qi, L., Wei, Y.: \(\cal{M} \)-tensors and nonsingular \(\cal{M} \)-tensors. Linear Algebra Appl. 439(10), 3264–3278 (2013)
Zhang, L., Qi, L., Zhou, G.: \(\cal{M} \)-tensors and some applications. SIAM J. Matrix Anal. Appl. 35(2), 437–452 (2014)
Li, X., Ng, M.: Solving sparse non-negative tensor equations: algorithms and applications. Front. Math. China 10(3), 649–680 (2015)
Ding, W., Wei, Y.: Solving multi-linear systems with \(\cal{M} \)-tensors. J. Sci. Comput. 68(2), 689–715 (2016)
Luo, Z., Qi, L., Xiu, N.: The sparsest solutions to Z-tensor complementarity problems. Optim. Lett. 11, 471–482 (2017)
Azimzadeh, P., Bayraktar, E.: High order Bellman equations and weakly chained diagonally dominant tensors. SIAM J. Matrix Anal. Appl. 40(1), 276–298 (2019)
Han, L.: A homotopy method for solving multilinear systems with M-tensors. Appl. Math. Lett. 69, 49–54 (2017)
Xie, Z., Jin, X., Wei, Y.: Tensor methods for solving symmetric M-tensor systems. J Sci Comput 74, 412–425 (2018)
Li, D., Xie, S., Xu, H.: Splitting methods for tensor equations. Numer. Linear Algebra Appl. 24(5), e2102 (2017)
Cui, L., Li, M., Song, Y.: Preconditioned tensor splitting iterations method for solving multi-linear systems. Appl. Math. Lett. 96, 89–94 (2019)
Liu, D., Li, W., Vong, S.: The tensor splitting with application to solve multi-linear systems. J. Comput. Appl. Math. 330(1), 75–94 (2018)
He, H., Ling, C., Qi, L., Zhou, G.: A globally and quadratically convergent algorithm for solving multilinear systems with \(\cal{M} \)-tensors. J. Sci. Comput. 73(3), 1718–1741 (2018)
Wang, X., Che, M., Wei, Y.: Neural networks based approach solving multi-linear systems with M-tensors. Appl. Math. Lett. 351, 33–42 (2019)
Lv, C., Ma, C.: A Levenberg-Marquardt method for solving semi-symmetric tensor equations. J. Comput. Appl. Math. 332, 13–25 (2018)
Liang, M., Zheng, B., Zhao, R.: Alternating iterative methods for solving tensor equations with applications. Numer. Algor. 80, 1437–1465 (2019)
Liang, M., Zheng, B., Zheng, Y., Zhao, R.: A two-step accelerated Levenberg-Marquardt method for solving multlinear systems in tensor-train format. J. Comput. Appl. Math. 382, 113069 (2021)
Ortega, J., Rheinboldt, W.: Iterative solution of nonlinear equations in several variables. Academic Press, New York (1970)
Chen, J., Li, W.: On new exponential quadratically convergent iterative formulae. Appl. Math. Comput. 180, 242–246 (2006)
Chen, J., Li, W.: An exponential regula falsi method for solving nonlinear equations. Numer. Algor. 41, 327–338 (2006)
Kahya, E.: A class of exponential quadratically convergent iterative formulae for unconstrained optimization. Appl. Math. Comput. 186, 1010–1017 (2007)
Smietanski, M.: On a new exponential iterative method for solving nonsmooth equations. Numer. Linear Algebra Appl. 26(5), e2255 (2019)
Levenberg, K.: A method for the solution of certain nonlinear problems in least squares. Quarterly Appl. Math. 2, 164–168 (1944)
Marquardt, D.: An algorithm for least-squares estimation of nonlinear parameters. J. Soc. Ind. Appl. Math. 11, 431–441 (1963)
Yamashita, N., Fukushima, M.: On the rate of convergence of the Levenberg-Marquardt method. Computing 15, 239–249 (2001)
Zhou, W.: On the convergence of the modified Levenberg-Marquardt method with a nonmonotone second order Armijo type line search. J. Comput. Appl. Math. 239, 152–161 (2013)
Golub, G., Van Loan, C.: Matrix computations, 4th edn. Johns Hopkins University Press, Baltimore (2013)
Bader, B., Kolda, T., et al.: Tensor toolbox for MATLAB, Version 3.2. (2021). http://www.tensortoolbox.org
Li, T., Wang, Q., Zhang, X.: Gradient based iterative methods for solving symmetric tensor equations. Numer. Linear Algebra Appl. 29(2), e2414 (2022)
Ng, M., Qi, L., Zhou, G.: Finding the largest eigenvalue of a nonnegative tensor. SIAM J. Matrix Anal. Appl. 31(3), 1090–1099 (2009)
Matsuno, Y.: Exact solutions for the nonlinear Klein-Gordon and Liouville equations in four-dimensional Euclidean space. J. Math. Phys. 28(10), 2317–2322 (1987)
Zwillinger, D.: Handbook of differential equations, 3rd edn. Academic Press Inc, Boston (1997)
Oseledets, I.: Tensor train decomposition. SIAM J. Sci. Comput. 33(5), 2295–2317 (2011)
Acknowledgements
The authors are thankful to the handling editor and the referees for their constructive comments and suggestions, which greatly improve the quality of this paper.
Funding
This work was supported by National Natural Science Foundation of China (Nos. 11961057, 12201267, 12361081), the Natural Science Foundation of Gansu Province (Nos. 21JR1RE287, 22JR5RA559) the Innovation Foundation of Education Department of Gansu Province (Nos. 2021B-221, 2023B-135), and the Science Foundation of Tianshui Normal University (No. CXJ2021-01).
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M. Liang provided the methodology along with the problem under consideration and re-editing and correcting the manuscript, L. Dai implemented the scheme and edited the manuscript, and R. Zhao completed the numerical experiments of the related algorithms.
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Liang, M., Dai, L. & Zhao, R. Iterative methods for solving tensor equations based on exponential acceleration. Numer Algor (2023). https://doi.org/10.1007/s11075-023-01692-w
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DOI: https://doi.org/10.1007/s11075-023-01692-w