Extending quasi-GMRES method to solve generalized Sylvester tensor equations via the Einstein product

Document Type : Research Article

Author

Department of Computer Science, Faculty of Computer and Industrial Engineering, Birjand University of Technology, Birjand, Iran.

Abstract

This paper aims to extend a Krylov subspace technique based on an in-complete orthogonalization of Krylov tensors (as a multidimensional exten-sion of the common Krylov vectors) to solve generalized Sylvester tensor equations via the Einstein product. First, we obtain the tensor form of the quasi-GMRES method, and then we lead to the direct variant of the proposed algorithm. This approach has the great advantage that it uses previous data in each iteration and has a low computational cost. More-over, an upper bound for the residual norm of the approximate solution is found. Finally, several experimental problems are given to show the acceptable accuracy and efficiency of the presented method.

Keywords

Main Subjects


[1] Bader, B.W. and Kolda, T.G. Efficient MATLAB computations with sparse and factored tensors, SIAM J. Sci. Comput. 30(1) (2007), 205–231.
[2] Bader, B.W. and Kolda, G.T. Tensor Toolbox for MATLAB, Version 3.6, Available online at https://ww.tensortoolbox.org., 2023. [3] Ballani, J. and Grasedyck, L. A projection method to solve linear systems in tensor form, Numer. Linear Algebra Appl. 20 (2013), 27–43.
[4] Behera, R. and Mishra, D. Further results on generalized inverses of tensors via the Einstein product, Linear Multilinear Algebra. 65 (2017), 1662–1682.
[5] Beik, F.P.A. and Ahmadi-Asl, S. Residual norm steepest descent based iterative algorithms for Sylvester tensor equations, J. Math. Model. 2 (2015), 115–131.
[6] Beik, F.P.A., Movahed, F.S. and Ahmadi-Asl, S. On the krylov subspace methods based on the tensor format for positive definite sylvester tensor equations, Numer. Linear Algebra. Appl. 23 (2016), 444–466.
[7] Brazell, M., Li, N., Navasca, C. and Tamon, C. Solving multilinear sys-tems via tensor inversion, SIAM J. Matrix Anal. Appl. 34 (2013), 542–570.
[8] Brown, P.N. and Hindmarsh, A.C. Reduced storage methods in sti ODE systems, Appl. Math. Comput. 31 (1989), 40.
[9] Chen, Z. and Lu, L.Z. A projection method and Kronecker product pre-condetioner for solving Sylvester tensor equations, Science China 55 (2012), 1281–1292.
[10] Dehdezi, E.K. Iterative methods for solving Sylvester transpose-tensor equation A ⋆N X ⋆M B + C ⋆M X T ⋆N D = E, Operations Research Forum. 2 (2021), 64.
[11] Dehdezi, E.K. and Karimi, S. Extended conjugate gradient squared and conjugate residual squared methods for solving the generalized coupled Sylvester tensor equations, T. I. Meas. Control. 43 (2021), 519–527.
[12] Dehdezi, E.K. and Karimi, S. A gradient based iterative method and asso-ciated preconditioning technique for solving the large multilinear systems, Calcolo. 58 (2021), 51.
[13] Ding, F. and Chen. T. Gradient based iterative algorithms for solving a class of matrix equations, IEEE Trans Autom Control. 50 (2005), 1216–1221.
[14] Ding, F. and Chen, T. Iterative least squares solutions of coupled Sylvester matrix equations, Syst. Control Lett. 54 (2005), 95–107.
[15] Ding, W. and Wei, Y. Solving multi-linear system with M-tensors, J Sci Comput. 68 (2016), 689–715.
[16] Erfanifar, R. and Hajarian, M. Several efficient iterative algorithms for solving nonlinear tensor equation X +AT ⋆M X −1 ⋆N A = I with Einstein product, Comput. Appl. Math. 43 (2024), 84.
[17] Graham, A. Kronecker products and matrix calculus: with applications, Courier Dover Publications, 2018.
[18] Guennouni, A.E., Jbilou, K. and Riquet, A.J. Block Krylov subspace methods for solving large Sylvester equations, Numeric. Algorithms. 29 (2002), 75–96.
[19] Grasedyck, L. Existence and computation of low Kronecker-rank approx-imations for large linear systems of tensor product structure, Computing. 72 (2004), 247–265.
[20] Heyouni, M., Movahed, F.S. and Tajaddini, A. A tensor format for the generalized hessenberg method for solving sylvester tensor equations, J. Comput. Appl. Math. 377 (2020), 112878.
[21] Huang, B. and Ma, C. Global least squares methods based on tensor form to solve a class of generalized Sylvester tensor equations, Appl. Math. and Comput. 369 (2020), 124892.
[22] Huang, B. and Ma, C. An iterative algorithm to solve the generalized Sylvester tensor equations, Linear Multilinear Algebra. 68(6) (2020), 1175–1200.
[23] Huang, B., Xie, Y. and Ma, C. Krylov subspace methods to solve a class of tensor equations via the Einstein product, Numer. Linear Algebra Appl. 40(4) (2019), e2254.
[24] Jia, Z. On IGMRES(q), incomplete generalized minimal residual methods for large unsymmetric linear systems, Technical Report 94-047, Depart-ment of Mathematics, University of Bielefeld, Sonderforschungsbereich 343, 1994. Last revision March, 1995.
[25] Kolda, T.G. and Bader, B.W. Tensor decompositions and applications, SIAM Rev. 51 (2009), 455–500.
[26] Lai, W.M., Rubin, D. and Krempl, E. Introduction to continuum me-chanics, Oxford: Butterworth Heinemann, 2009.
[27] Li, B.W., Sun, Y.S. and Zhang, D.W. Chebyshev collocation spectral methods for coupled radiation and conduction in a concentric spherical participating medium, ASME J Heat Transfer. 131 (2009), 062701–62709.
[28] Li, B.W., Tian, S., Sun, Y.S. and Hu, Z.M. Schur-decomposition for 3D matrix equations and its application in solving radiative discrete ordinates equations discretized by Chebyshev collocation spectral method, J. Comput. Phys. 229 (2010), 1198–1212.
[29] Li, T., Wang, Q.-W. and Zhang, X.-F. A Modified Conjugate Residual Method and Nearest Kronecker Product Preconditioner for the General-ized Coupled Sylvester Tensor Equations, Mathematics. 10 (2022), 1730.
[30] Liang, L., Zheng, B. and Zhao, R.J. Tensor inversion and its applica-tion to the tensor equations with Einstein product, Linear Multilinear Algebra. 67 (2018), 843–870.
[31] Malek, A. and Masuleh, S.H.M. Mixed collocation-finite difference method for 3D microscopic heat transport problems, J. Comput. Appl. Math. 217 (2008), 137–147.
[32] Malek, A., Bojdi, Z.K. and Golbarg, P.N.N. Solving fully three-dimensional microscale dual phase lag problem using mixed-collocation finite difference discretization, J. Heat Transfer. 134 (2012), 0945041–0945046.
[33] Masuleh, S.H.M. and Phillips, T.N. Viscoelastic flow in an undulating tube using spectral methods, Computers & Fluids 33 (2004), 10751095. [34] Qi, L. and Luo, Z. Tensor analysis: Spectral theory and special tensors, SIAM, Philadelphia, 2017.
[35] Saad, Y. Iterative methods for sparse linear systems, PWS press, New York, 1995.
[36] Saad, Y. and Wu, K. DQGMRES: a direct quasi-minimal residual algo-rithm based on incomplete orthogonalization, Numeric. Linear Algebra Appl. 3(4) (1996), 329–343.
[37] Shi, X., Wei, Y. and Ling, S. Backward error and perturbation bounds for high order Sylvester tensor equation, Linear Multilinear Algebra. 61 (2013), 1436–1446.
[38] Sun, L., Zheng, B., Bu, C. and Wei, Y. Moore-Penrose inverse of tensors via Einstein product, Linear Multilinear Algebra. 64 (2016), 686–698.
[39] Tzou, D.V. Macro to Micro Heat Transfer, Taylor & Francis: Washing-ton, 1996.
[40] Wang, Q.W. and Wang, X. A system of coupled two-sided sylvester-type tensor equations over the quaternion algebra, Taiwanese J. Math. 24 (2020), 1399–1416.
[41] Wang, Q.W. and Xu, X. Iterative algorithms for solving some tensor equations, Linear Multilinear Algebra. 67(7) (2019), 1325–1349.
[42] Wang, Q.W., Xu, X.J. and Duan, X.F. Least squares solution of the quaternion sylvester tensor equation, Linear Multilinear Algebra. 69 (2021), 104–130.
[43] Xie, M.Y. and Wang, Q.W. Reducible solution to a quaternion tensor equation, Front. Math. China. 15 (2020), 1047–1070.
[44] Zhang, X.-F. and Wang, Q.-W. Developing iterative algorithms to solve Sylvester tensor equations, Appl. Math. Comput. 409 (2021), 126403.
[45] Zhang, X.-F., Ding, W. and Li, T. Tensor form of GPBiCG algorithm for solving the generalized Sylvester quaternion tensor equations, J. Franklin Institute. 360 (2023), 5929–5946.
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