Abstract
In recent years, the method of solving the multi-linear systems has received much attention. In this paper, the multi-linear systems with more general \({\mathcal {Z}}\)-tensors which have wider applications than \({\mathcal {M}}\)-tensors are studied. Two different SOR-type splitting methods and a general preconditioner that can include some known preconditioners are provided. In addition, some comparisons of spectral radius between the preconditioned iterative tensors and the original one are given. Numerical examples are given to illustrate our theoretical results and demonstrate the efficiency of the proposed preconditioned methods.
Similar content being viewed by others
References
Bai X, He H, Ling C, Zhou G (2021) A nonnegativity preserving algorithm for multilinear systems with nonsingular \({\cal{M}}\)-tensors. Numer Algorithms 87:1301–1320
Bu F, Ma C (2020) The tensor splitting methods for solving tensor absolute value equation. Comput Appl Math 39:178
Che M, Qi L, Wei Y (2016) Positive-definite tensors to nonlinear complementarity problems. J Opt Theory Appl 168:475–487
Che M, Wei Y (2020) Theory and computation of complex tensors and its applications, Singapore. Springer, Asia
Cui L, Li M, Song Y (2019) Preconditioned tensor splitting iterations method for solving multi-linear systems. Appl Math Lett 96:89–94
Cui L, Zhang X, Wu S (2020) A new preconditioner of the tensor splitting iterative method for solving multi-linear systems with \({\cal{M}}\)-tensors. Comput Appl Math 39:173
Cui L, Zhang X, Zheng Y (2021) A preconditioner based on a splitting-type iteration method for solving complex symmetric indefinite linear systems. Jpn J Ind Appl Math 38:965–978
Daniel K, Christine T (2010) Krylov subspace methods for linear systems with tensor product structure. SIAM J Matrix Anal Appl 31:1688–1714
Ding W, Qi L, Wei Y (2013) \({\cal{M}}\)-tensors and nonsingular \({\cal{M}}\)-tensors. Linear Algebra Appl 439:3264–3278
Ding W, Wei Y (2016) Solving multi-linear system with \({\cal{M}}\)-tensors. J Sci Comput 68:689–715
Du S, Zhang L, Chen C, Qi L (2018) Tensor absolute value equations. Sci China Math 61:1695–1710
Feng X, He Y, Meng J (2009) Application of modified homotopy perturbation method for solving the augmented systems. J Comput Appl Math 231:288–301
Feng X, Shao L (2010) On the generalized SOR-like methods for saddle point problems. J Appl Math Inform 28:663–677
Gleich D, Lim L, Yu Y (2015) Multilinear PageRank. SIAM J Matrix Anal Appl 36:1507–1541
Grasedyck L (2004) Existence and computation of low Kronecker-rank approximations for large linear systems of tensor product structure. Computing 72:247–265
Hajarian M (2020) Conjugate gradient-like methods for solving general tensor equation with Einstein product. J Franklin Inst 357:4272–4285
Han L (2017) A homotopy method for solving multilinear systems with \({\cal{M}}\)-tensors. Appl Math Lett 69:49–54
He H, Ling C, Qi L, Zhou G (2018) A globally and quadratically convergent algorithm for solving multilinear systems with \({\cal{M}}\)-tensors. J Sci Comput 76:1718–1741
Li J, Li W, Vong SW, Luo Q, Xiao M (2020) A Riemannian optimization approach for solving the generalized eigenvalue problem for nonsquare matrix pencils. J Sci Comput 82:67
Li W, Liu D, Vong SW (2018) Comparison results for splitting iterations for solving multi-linear systems. Appl Numer Math 134:105–121
Li W, Ng M (2014) On the limiting probability distribution of a transition probability tensor. Linear Multilinear Algebra 62:362–385
Li W, Liu D, Ng M, Vong SW (2017) The uniqueness of multilinear PageRank vectors. Numer Linear Algebra Appl 24:e2017:e2017e2017
Lim L (2005) Singular values and eigenvalues of tensors: a variational approach, IEEE CAMSAP 2005-First International Workshop on Computational Advances in Multi-Sensor Adaptive Processing 2005:129–132
Liu D, Li W, Vong SW (2018) The tensor splitting with application to solve multi-linear systems. J Comput Appl Math 330:75–94
Liu D, Li W, Vong SW (2018) Relaxation methods for solving the tensor equation arising from the higher-order Markov chains. Numer Linear Algebra Appl 330:75–94
Liu D, Li W, Vong SW (2020) A new preconditioned SOR method for solving multi-linear systems with an \({\cal{M}}\)-tensor. Calcolo 57:15
Lv C, Ma C (2018) A Levenberg-Marquardt method for solving semi-symmetric tensor equations. J Comput Appl Math 332:13–25
Mo C, Wei Y (2021) On nonnegative solution of multi-linear system with strong \({\cal{M}}_z\)-tensors. Numer Math Theor Meth Appl 14:176–193
Ng M, Qi L, Zhou G (2009) Finding the largest eigenvalue of a nonnegative tensor. SIAM J Matrix Anal Appl 31:1090–1099
Noutsos D, Tzoumas M (2006) On optimal improvements of classical iterative schemes for Z-matrices. J Comput Appl Math 188:89–106
Pearson K (2010) Essentially positive tensors. Int J Algebra 4:421–427
Qi L (2005) Eigenvalues of a real supersymmetric tensor. J Symbolic Comput 40:1302–1324
Qi L, Luo Z (2017) Tensor analysis: Spectral theory and special tensors. SIAM, Philadelphia
Wang X, Che M, Wei Y (2019) Neural networks based approach solving multi-linear systems with \({\cal{M}}\)-tensors. Neurocomputing 351:33–42
Wang X, Che M, Wei Y (2020) Preconditioned tensor splitting AOR iterative methods for \({cal{H}}\)-tensor equations. Numer Linear Algebra Appl
Wang X, Huang T, Fu Y (2007) Comparison results on preconditioned SOR-type iterative method for Z-matrices linear systems. J Comput Appl Math 206:726–732
Wang X, Wei Y (2016) \({\cal{H}}\)-tensors and nonsingular \({\cal{H}}\)-tensors. Front Math China 11:557–575
Wang X, Che M, Wei Y (2020) Neural network approach for solving nonsingular multi-linear tensor systems. J Comput Appl Math 368:112569
Wang X, Mo C, Che M, Wei Y (2021) Accelerated dynamical approaches for finding the unique positive solution of \( \cal{KS} \)-tensor equations. Numer. Algorithms 88:1787–1810
Xie Z, Jin X, Wei Y (2018) Tensor methods for solving symmetric \({\cal{M}}\)-tensor systems. J Sci Comput 74:412–425
Zhang H, Zhao X, Jiang T, Ng M, Huang T (2021) Multi-scale features tensor train minimization for multi-dimensional images recovery and recognition. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2021.310884
Zhang L (2020) Modified block preconditioner for generalized saddle point matrices with highly singular (1,1) blocks. Linear Multilinear Algebra 68:152–160
Zhang L, Qi L, Zhou G (2014) \({\cal{M}}\)-tensors and some applications. SIAM J Matrix Anal Appl 35:437–452
Zhao R, Zheng B, Liang M, Xu Y (2020) A locally and cubically convergent algorithm for computing \( {\cal{Z}} \)-eigenpairs of symmetric tensors. Numer Linear Algebra Appl 27:e2284
Zhang Y, Liu Q, Chen Z (2020) Preconditioned Jacobi type method for solving multi-linear systems with \({\cal{M}}\)-tensors. Appl. Math. Lett. 104:437–452
Acknowledgements
The authors are grateful to the handling Associate Editor and anonymous referees for useful comments and suggestions that contributed to improving the quality of the manuscript. This research is supported in part by National Natural Science Foundations of China(No.11601134,11571095), Foundation of Henan Educational Committee(No.21A110013), Foundation of Henan Normal University (No.2021PL03), Natural Science Foundations of Henan (No.202300410236), 2020 Scientific Research Project for Postgraduates of Henan Normal University (No.YL202021).
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by yimin wei.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Cui, LB., Fan, YD. & Zheng, YT. A general preconditioner accelerated SOR-type iterative method for multi-linear systems with \({\mathcal {Z}}\)-tensors. Comp. Appl. Math. 41, 26 (2022). https://doi.org/10.1007/s40314-021-01712-2
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s40314-021-01712-2