Abstract
Motivated by the effectiveness of Krylov projection methods and the CP decomposition of tensors, which is a low rank decomposition, we propose Arnoldi-based methods (block and global) to solve Sylvester tensor equation with low rank right-hand sides. We apply a standard Krylov subspace method to each coefficient matrix, in order to reduce the main problem to a projected Sylvester tensor equation, which can be solved by a global iterative scheme. We show how to extract approximate solutions via matrix Krylov subspaces basis. Several theoretical results such as expressions of residual and its norm are presented. To show the performance of the proposed approaches, some numerical experiments are given.
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Ali Beik, F.P., Movahed, F.S., Ahmadi-Asl, S.: On the Krylov subspace methods based on tensor format for positive definite Sylvester tensor equations. Numer. Linear Algebra Appl. 23(3), 444–466 (2016)
Ballani, J., Grasedyck, L.: A projection method to solve linear systems in tensor format. Numer. Linear Algebra Appl. 20(1), 27–43 (2013)
Beylkin, G., Mohlenkamp, M.J.: Algorithms for numerical analysis in high dimensions. SIAM J. Sci. Comput. 26(6), 2133–2159 (2005)
Calvetti, D., Reichel, L.: Application of ADI iterative methods to the restoration of noisy images. SIAM J. Matrix Anal. Appl. 17(1), 165–186 (1996)
Chen, M., Kressner, D.: Recursive blocked algorithms for linear systems with Kronecker product structure. arXiv:1905.09539 (2019)
Chen, Z., Lu, L.: A projection method and Kronecker product pre-conditioner for solving Sylvester tensor equations. Sci. Chin. Math. 55(6), 1281–1292 (2012)
Chen, Z., Lu, L.: A gradient based iterative solutions for sylvester tensor equations. Math. Probl. Eng., 2013 (2013)
Cichocki, A., Zdunek, R., Phan, A.H., Amari, S.i.: Non-Negative Matrix and Tensor Factorizations: Applications to Exploratory Multi-Way Data Analysis and Blind Source Separation. Wiley (2009)
Ding, F., Chen, T.: Gradient based iterative algorithms for solving a class of matrix equations. IEEE Trans. Autom. Control 50(8), 1216–1221 (2005)
El Guennouni, A., Jbilou, K., Riquet, A.: Block Krylov subspace methods for solving large Sylvester equations. Numer. Algor. 29(1–3), 75–96 (2002)
Grasedyck, L.: Existence and computation of low Kronecker-rank approximations for large linear systems of tensor product structure. Computing 72(3-4), 247–265 (2004)
Jbilou, K.: Low rank approximate solutions to large Sylvester matrix equations. Appl. Math. Comput. 177(1), 365–376 (2006)
Jbilou, K., Messaoudi, A., Sadok, H.: Global FOM and GMRES algorithms for matrix equations. Appl. Numer. Math. 31(1), 49–63 (1999)
Karimi, S., Dehghan, M.: Global least squares method based on tensor form to solve linear systems in Kronecker format. Trans. Instit. Measur. Control 40(7), 2378–2386 (2018)
Khoromskij, B.N.: Tensors-structured numerical methods in scientific computing: Survey on recent advances. Chemom. Intell. Lab. Syst. 110(1), 1–19 (2012)
Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Rev. 51(3), 455–500 (2009)
Kressner, D., Tobler, C.: Krylov subspace methods for linear systems with tensor product structure. SIAM J. Matrix Anal. Appl. 31(4), 1688–1714 (2010)
Lee, N., Cichocki, A.: Fundamental tensor operations for large-scale data analysis using tensor network formats. Multidim. Syst. Sign. Process. 29(3), 921–960 (2018)
Li, B.W., Tian, S., Sun, Y.S., 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(4), 1198–1212 (2010)
Lu, H., Plataniotis, K.N., Venetsanopoulos, A.N.: A survey of multi-linear subspace learning for tensor data. Pattern Recogn. 44(7), 1540–1551 (2011)
Malek, A., Momeni-Masuleh, S.H.: A mixed collocation finite difference method for 3d microscopic heat transport problems. J. Comput. Appl. Math. 217(1), 137–147 (2008)
Penzl, T., et al.: A Matlab toolbox for large Lyapunov and Riccati equations, model reduction problems, and linear quadratic optimal control problems. Software available at https://www.tu-chemnitz.de/sfb393/lyapack/ (2000)
Saad, Y.: Iterative Methods for Sparse Linear Systems, vol. 82. SIAM (2003)
Sun, Y.S., Ma, J., Li, B.W.: Chebyshev collocation spectral method for three-dimensional transient coupled radiative conductive heat transfer. J. Heat Transfer 134(9), 092701 (2012)
Trefethen, L.N., Bau, D. III: Numerical Linear Algebra, vol. 50. SIAM (1997)
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Bentbib, A.H., El-Halouy, S. & Sadek, E.M. Krylov subspace projection method for Sylvester tensor equation with low rank right-hand side. Numer Algor 84, 1411–1430 (2020). https://doi.org/10.1007/s11075-020-00874-0
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DOI: https://doi.org/10.1007/s11075-020-00874-0