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A Spectral Analysis of Subspace Enhanced Preconditioners

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Abstract

It is well-known that the convergence of Krylov subspace methods for solving linear system of equations depends on the spectrum of the matrix, moreover, it is widely accepted that for both symmetric and unsymmetric systems Krylov subspace methods converge faster if the spectrum of the matrix is clustered. In this paper we investigate the spectrum of the system preconditioned by deflation, coarse correction and adapted deflation preconditioners. Our analysis shows that the spectrum of the preconditioned system is highly impacted by the angle between the coarse space of the three preconditioners and the subspace spanned by the eigenvectors associated with the small eigenvalues of the matrix. Furthermore, we prove that the accuracy of the inverse of the projection matrix also impacts the spectrum of the preconditioned system. Numerical experiments confirm the theoretical analysis.

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References

  1. Brown, P.N., Walker, H.F.: GMRES on singular systems. SIAM J. Matrix Anal. Appl. 18, 37–51 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  2. Cai, X.C., Sarkis, M.: A restricted additive Schwarz preconditioner for general sparse linear systems. SIAM J. Sci. Comput. 21, 792–797 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  3. Efstathiou, E., Gander, M.J.: Why restricted additive Schwarz converges faster than additive Schwarz. BIT 43, 945–959 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  4. Erlangga, Y.A., Nabben, R.: Deflation and balancing preconditioners for Krylov subspace methods applied to nonsymmetric matrices. SIAM J. Matrix Anal. Appl. 30, 684–699 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  5. Frank, J., Vuik, C.: On the construction of deflation-based preconditioners. SIAM J. Sci. Comput. 23, 442–462 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  6. Golub, G.H., van Loan, C.F.: Matrix Comput., 3rd edn. John Hopkins University Press, Baltimore (1996)

    Google Scholar 

  7. Giraud, L., Gratton, S.: On the sensitivity of some spectral preconditioners. SIAM J. Matrix Anal. Appl. 27, 1089–1105 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  8. Gosselet, P., Rey, C.: On a selective reuse of Krylov subspaces in Newton–Krylov approaches for nonlinear elasticity. In: Domain Decomposition Methods in Science and Engineering, National Autonomous University of Mexico, México, pp. 419–426 (2003)

  9. Havé, P., Masson, R., Nataf, F., Szydlarski, M., Xiang, H., Zhao, T.: Algebraic domain decomposition methods for highly heterogeneous problems. SIAM J. Sci. Comput. 35, C284–C302 (2013)

    Article  MATH  Google Scholar 

  10. Hecht, F.: FreeFem++. http://www.freefem.org/ff++/ (2014)

  11. Kaasschieter, E.F.: Preconditioned conjugate gradients for solving singular systems. J. Comput. Appl. Math. 24, 265–275 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  12. Karypis, G., Kumar, V.: A fast and highly qualty multilevel scheme for partitioning irregular graphs. SIAM J. Sci. Comput. 20, 359–392 (1998)

    Article  MathSciNet  Google Scholar 

  13. Klawonn, A., Rheinbach, O.: Deflation, projector preconditioning, and balancing in iterative substructuring methods: connections and new results. SIAM J. Sci. Comput. 34, A459–A484 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  14. Klawonn, A., Lanser, M., Rheinbach, O.: Nonlinear FETI-DP and BDDC methods. SIAM J. Sci. Comput. 36, A737–A765 (2014)

    Article  MathSciNet  Google Scholar 

  15. Morgan, R.B.: GMRES with deflated restarting. SIAM J. Sci. Comput. 24, 20–37 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  16. Nabben, R., Vuik, C.: a comparison of abstract versions of deflation, balancing and additive coarse grid correction preconditioners. Numer. Linear Algeb. Appl. 15, 355–372 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  17. Nataf, F., Xiang, H., Dolean, V., Spillane, N.: A coarse grid space construction based on local Dirichlet to Neumann maps. SIAM J. Sci. Comput. 33, 1623–1642 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  18. Nicolaides, R.A.: Deflation of conjugate gradient with application to boundary value problem. SIAM J. Numer. Anal. 24, 355–365 (1987)

    Article  MathSciNet  MATH  Google Scholar 

  19. Notay, Y., Napov, A.: Further comparison of additive and multiplicative coarse grid correction. Appl. Numer. Math. 65, 53–62 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  20. Padiy, A., Axelsson, O., Polman, B.: Generalized augmented matrix preconditioning approach and its application to iterative solution of ill-conditioned algebraic systems. SIAM J. Matrix Anal. Appl. 22, 793–818 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  21. Parks, M.L., de Sturler, E., Mackey, G., Johnson, D.D., Maiti, S.: Recycling Krylov subspaces for sequences of linear systems. SIAM J. Sci. Comput. 28, 1651–1674 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  22. Saad, Y.: Iterative Methods for Sparse Linear Systems. SIAM, Philadelphia (2003)

    Book  MATH  Google Scholar 

  23. St-Cyr, A., Gander, M.J., Thomas, S.J.: Optimized multiplicative, additive, and restricted additive Schwarz preconditioning. SIAM J. Sci. Comput. 29, 2402–2425 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  24. Stewart, G.W.: Matrix Algorithms Volume II: Eigensystems. SIAM, Philadelphia (2001)

    Book  Google Scholar 

  25. Tang, J.M., Maclachlan, S.P., Nabben, R., Vuik, C.: A comparison of two-level preconditioners based on multigrid and deflation. SIAM J. Matrix Anal. Appl. 31, 1715–1739 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  26. Tang, J.M., Nabben, R., Vuik, C., Erlangga, Y.A.: Comparison of two-level preconditioners derived from deflation, domain decomposition and multigrid methods. J. Sci. Comput. 39, 340–370 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  27. Toselli, A., Widlund, O.: Domain Decomposition Methods: Algorithms and Theory. Springer, Berlin (2005)

    Google Scholar 

  28. van der Sluis, A., van der Vorst, H.A.: The rate of convergence of conjugate gradients. Numer. Math. 48, 543–560 (1986)

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgments

The author would like to thank Prof. Frédéric Nataf and Prof. Xiao-Chuan Cai for their supervision and valuable suggestions. The author also thanks the referees for the helpful comments that improve the paper.

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Correspondence to Tao Zhao.

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Zhao, T. A Spectral Analysis of Subspace Enhanced Preconditioners. J Sci Comput 66, 435–457 (2016). https://doi.org/10.1007/s10915-015-0029-0

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  • DOI: https://doi.org/10.1007/s10915-015-0029-0

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