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N-mode minimal tensor extrapolation methods

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Abstract

The purpose of this work is to present, using the n-mode product, a new approach to generalize, for tensor sequences, the well-known vector extrapolation methods MPE (minimal polynomial extrapolation method) and RRE (reduced rank extrapolation method). We define the notion of the n-mode minimal polynomial of a matrix with respect to a tensor. This polynomial will be used, through the iterative solution of some tensor linear systems, to introduce the tensor version of MPE and RRE. These methods involve only the terms of sequences that result from the used iterative methods. The implementation of these methods on some sequences of tensors confirms the effectiveness and applicability of our approach.

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References

  1. Wynn, P. : Acceleration techniques for iterated vector and matrix problems: Math. Comput. 16, 301–322 (1962)

  2. Aitken, A.: On Bernoulli’s numerical solution of algebraic equations. Proc. R. Soc. Edinb. 46, 289–305 (1927)

    Article  Google Scholar 

  3. Benchettou, O., Bentbib, A. H., Bouhamidi, A.: An accelerated tensorial double proximal gradient method for total variaitional regularization problem. J. Optim. Theory Appl. 1573–2878 (2023)

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

    Article  MathSciNet  Google Scholar 

  5. Brezinski, C.: Redivo Zaglia. M.: Extrapolation methods. Theory and Practice; North Holland Publishing, Amsterdam, The Netherlands (1991)

  6. Brezinski, C.: G\(\acute{e}\)n\(\acute{e}\)ralisations de la transformation de Shanks, de la table de \(Pad\acute{e}\), et de l’\(\epsilon \)-algorithme. Calcolo. 12, 317–360 (1975)

    Article  MathSciNet  Google Scholar 

  7. Brezinski, C., Redivo Zaglia, M., Serra-Capizzano, S.: Extrapolation methods for PageRank computation. Comptes Rendus Math. 340, 393–397 (2005)

    Article  MathSciNet  Google Scholar 

  8. Burrow, M.D.: The minimal polynomial of a linear transformation. Amer. Math. Monthly. 80, 1129–113 (1973)

    Article  MathSciNet  Google Scholar 

  9. Cabay, S., Jackson, L.W.: A polynomial extrapolation method for finding limits and antilimits for vector sequences. SIAM J. Numer. Anal. 13, 734–752 (1976)

    Article  MathSciNet  Google Scholar 

  10. Candes, E.J., Tao, T.: The power of convex relaxation near-optimal matrix completion. IEEE Trans. Inf. Theory 56, 2053–2080 (2010)

    Article  MathSciNet  Google Scholar 

  11. Chen, J., Saad, Y.: On the tensor SVD and the optimal low rank orthogonal approximation of tensors. SIAM J. Matrix Anal. Appl. https://doi.org/10.1137/070711621 (2009)

  12. Combettes, P.L., Pesquet, J.C.: Proximal splitting methods in signal processing. In: Fixed-Point Algorithms for Inverse Problems in Science and Engineering, pp. 185–212. Springer. (2011)

  13. De Lathauwer, L., De Moor, B., Vandewalle, J.A.: Multilinear singular value decomposition. SIAM J Matrix Anal. Appl. 21, 1253–1278 (2000)

    Article  MathSciNet  Google Scholar 

  14. Duminil, S., Sadok, H., Silvester, D.: Fast solvers for discretized Navier-Stokes problems using vector extrapolation. Numer. Algorithms. 66, 89–104 (2014)

    Article  MathSciNet  Google Scholar 

  15. El Ichi, A., Jbilou, K., Sadaka, R.: Tensor global extrapolation methods using the n-mode and the Einstein products. Mathematics. 8, 1298 (2020)

    Article  Google Scholar 

  16. Jbilou, K., Messaoudi, A.: Block extrapolation methods with applications. Appl. Numer. Math. 106, 154–164 (2016)

    Article  MathSciNet  Google Scholar 

  17. Jbilou, K., Sadok, H.: Analysis of some vector extrapolation methods for linear systems. Numer. Math. 70(1), 73–89 (1995)

    Article  MathSciNet  Google Scholar 

  18. Jbilou, K., Sadok, H.: LU-implementation of the modified minimal polynomial extrapolation method. IMA J. Numer. Anal. 19(4), 549–561 (1999)

    Article  MathSciNet  Google Scholar 

  19. Jbilou, K., Sadok, H.: Vector extrapolation methods. Appl. Numer. Comparison, J. Comput. Appl. Math. 122(1–2), 149–165 (2000)

  20. Jbilou, K., Sadok, H.: Matrix polynomial and epsilon-type extrapolation methods with applications. Numer. Algorithms 68(1), 107–119 (2015)

    Article  MathSciNet  Google Scholar 

  21. Kaniel, S., Stein, J.: Least-square acceleration of iterative methods for linear equations. J. Optim. Theory Appl. 14, 431–437 (1974)

    Article  MathSciNet  Google Scholar 

  22. Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Rev. 3, 455–500 (2009)

    Article  MathSciNet  Google Scholar 

  23. Mešina, M.: Convergence Acceleration for the Iterative Solution of the Equations X = AX + f. Comput. Methods Appl. Mech. Eng. 10, 165–173 (1977)

    Article  MathSciNet  Google Scholar 

  24. Nesterov, Y.: Gradient methods for minimizing composite functions. Math Program. 140(1), 125–161 (2013)

    Article  MathSciNet  Google Scholar 

  25. Pugachev, B.P.: Acceleration of the convergence of iterative processes and a method of solving systems of nonlinear equations. USSR Comput. Math. Math. Phys. 17, 199–207 (1978)

    Article  Google Scholar 

  26. Qing-Wen, W., Xiangjian, X.: Iterative algorithms for solving some tensor equations. Linear and Multilinear Algebra. 67(7), 1325–1349 (2019)

    Article  MathSciNet  Google Scholar 

  27. Ragnarsson, S., Van Loan, C.F.: Block tensor unfoldings. SIAM J. Matrix Anal. Appl. 33, 149–169 (2012)

    Article  MathSciNet  Google Scholar 

  28. Shanks, D.: Nonlinear transformations of divergent and slowly convergent sequences. J. Math. Phys. 34, 1–42 (1955)

    Article  MathSciNet  Google Scholar 

  29. Sidi, A., Ford, W.F., Smith, D.A.: Acceleration of convergence of vector sequences. SIAM J. Numer. Anal. 23, 178–196 (1986)

    Article  MathSciNet  Google Scholar 

  30. Sidi, A.: Vector extrapolation methods with applications. Soc. Ind. Appl. Math.https://doi.org/10.1137/1.9781611974966.ch1 31–64 (2017)

  31. Sridevi, G., Kumar, S.S.: Image inpainting based on fractional order nonlinear diffusion for image reconstruction, pp. 3802–381. Circuits, Systems and Signal Processing (2019)

  32. Wynn, P.: On a device for computing the \(e_m(S_n)\) transformation. Math. Tables Other Aids Comput. 10, 91–96 (1956)

    Article  MathSciNet  Google Scholar 

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Correspondence to Abdeslem Hafid Bentbib.

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Bentbib, A.H., Jbilou, K. & Tahiri, R. N-mode minimal tensor extrapolation methods. Numer Algor 95, 665–691 (2024). https://doi.org/10.1007/s11075-023-01585-y

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