Skip to main content

Efficient Preconditioner Updates for Semilinear Space–Time Fractional Reaction–Diffusion Equations

  • Chapter
  • First Online:
  • 1241 Accesses

Part of the book series: Springer INdAM Series ((SINDAMS,volume 30))

Abstract

The numerical solution of fractional partial differential equations poses significant computational challenges in regard to efficiency as a result of the nonlocality of the fractional differential operators. In this work we consider the numerical solution of nonlinear space–time fractional reaction–diffusion equations integrated in time by fractional linear multistep formulas. The Newton step needed to advance in (fractional) time requires the solution of sequences of large and dense linear systems because of the fractional operators in space. A preconditioning updating strategy devised recently is adapted and the spectrum of the underlying operators is briefly analyzed. Because of the quasilinearity of the problem, each Jacobian matrix of the Newton equations can be written as the sum of a multilevel Toeplitz plus a diagonal matrix and produced exactly in the code. Numerical tests with a population dynamics problem show that the proposed approach is fast and reliable with respect to standard direct, unpreconditioned, multilevel circulant/Toeplitz and ILU preconditioned iterative solvers.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   44.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   59.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Bell, N., Garland, M.: Cusp: Generic Parallel Algorithms for Sparse Matrix and Graph Computations. Version 0.5.1 (2015). http://cusplibrary.github.io/

  2. Bellavia, S., Bertaccini, D., Morini, B.: Nonsymmetric preconditioner updates in Newton-Krylov methods for nonlinear systems. SIAM J. Sci. Comput. 33(5), 2595–2619 (2011)

    Article  MathSciNet  Google Scholar 

  3. Benzi, M.: Preconditioning techniques for large linear systems: a survey. J. Comput. Phys. 182, 418–477 (2002)

    Article  MathSciNet  Google Scholar 

  4. Benzi, M., Tüma, M.: A sparse approximate inverse preconditioner for nonsymmetric linear systems. SIAM J. Sci. Comput. 19(3), 968–994 (1998)

    Article  MathSciNet  Google Scholar 

  5. Benzi, M., Meyer, C.D., Tüma, M.: A sparse approximate inverse preconditioner for the conjugate gradient method. SIAM J. Sci. Comput. 17(5), 1135–1149 (1996)

    Article  MathSciNet  Google Scholar 

  6. Benzi, M., Cullum, J.K., Tüma, M.: Robust approximate inverse preconditioning for the conjugate gradient method. SIAM J. Sci. Comput. 22(4), 1318–1332 (2000)

    Article  MathSciNet  Google Scholar 

  7. Bertaccini, D.: Efficient preconditioning for sequences of parametric complex symmetric linear systems. ETNA 18, 49–64 (2004)

    MathSciNet  MATH  Google Scholar 

  8. Bertaccini, D., Durastante, F.: Interpolating preconditioners for the solution of sequence of linear systems. Comput. Math. Appl. 72(4), 1118–1130 (2016)

    Article  MathSciNet  Google Scholar 

  9. Bertaccini, D., Durastante, F.: Solving mixed classical and fractional partial differential equations using short–memory principle and approximate inverses. Numer. Algorithms 74(4), 1061–1082 (2017)

    Article  MathSciNet  Google Scholar 

  10. Bertaccini, D., Durastante, F.: Iterative Methods and Preconditioning for Large and Sparse Linear Systems with Applications. Chapman and Hall/CRC, New York (2018)

    Book  Google Scholar 

  11. Bertaccini, D., Filippone, S.: Sparse approximate inverse preconditioners on high performance GPU platforms. Comput. Math. Appl. 71, 693–711 (2016)

    Article  MathSciNet  Google Scholar 

  12. Cipolla, S., Durastante, F.: Fractional PDE constrained optimization: An optimize-then-discretize approach with L-BFGS and approximate inverse preconditioning. Appl. Numer. Math. 123, 43–57 (2018)

    Article  MathSciNet  Google Scholar 

  13. Diethelm, K., Ford, J.M., Ford, N.J., Weilbeer, M.: Pitfalls in fast numerical solvers for fractional differential equations. J. Comput. Appl. Math. 186(2), 482–503 (2006)

    Article  MathSciNet  Google Scholar 

  14. Garrappa, R.: Trapezoidal methods for fractional differential equations: theoretical and computational aspects. Math. Comput. Simul. 110, 96–112 (2015)

    Article  MathSciNet  Google Scholar 

  15. Golub, G.H., Van Loan, C.F.: Matrix computations, 4 edn. Johns Hopkins Studies in the Mathematical Sciences. Johns Hopkins University Press, Baltimore (2012)

    MATH  Google Scholar 

  16. Hairer, E., Lubich, C., Schlichte, M.: Fast numerical solution of nonlinear Volterra convolution equations. SIAM J. Sci. Stat. Comput. 6(3), 532–541 (1985)

    Article  MathSciNet  Google Scholar 

  17. Jaffard, S.: Propriétés des matrices <<bien localisées>> près de leur diagonale et quelques applications. In: Ann. Henri Poincaré Analyse non linéaire, vol. 7.5, pp. 461–476. Gauthier-Villars (1990)

    Google Scholar 

  18. Lambert, J.D.: Numerical Methods for Ordinary Differential Systems: The Initial Value Problem. Wiley, New York, (1991)

    MATH  Google Scholar 

  19. Lubich, C.: Discretized fractional calculus. SIAM J. Math. Anal. 17(3), 704–719 (1986)

    Article  MathSciNet  Google Scholar 

  20. Moroney, T., Yang, Q.: A banded preconditioner for the two–sided, nonlinear space–fractional diffusion equation. Comput. Math. Appl. 66(5), 659–667 (2013). Fractional Differentiation and its Applications. https://doi.org/10.1016/j.camwa.2013.01.048. http://www.sciencedirect.com/science/article/pii/S0898122113000783

    Article  MathSciNet  Google Scholar 

  21. Ng, M.K.: Iterative Methods for Toeplitz Systems. Oxford University Press, Oxford (2004)

    MATH  Google Scholar 

  22. Ng, M.K., Pan, J.: Approximate inverse circulant–plus–diagonal preconditioners for Toeplitz-plus-diagonal matrices. SIAM J. Sci. Comput. 32(3), 1442–1464 (2010)

    Article  MathSciNet  Google Scholar 

  23. Ortigueira, M.D.: Riesz potential operators and inverses via fractional centred derivatives. Int. J. Math. Math. Sci. 2006, 48391 (2006)

    Google Scholar 

  24. Prakash, A., Kumar, M.: Numerical solution of two dimensional time fractional-order biological population model. Open Phys. 14(1), 177–186 (2016)

    Article  Google Scholar 

  25. Saad, Y.: Iterative Methods for Sparse Linear Systems, 2nd edn. SIAM, Philadelphia (2003)

    Book  Google Scholar 

  26. Simmons, A., Yang, Q., Moroney, T.: A preconditioned numerical solver for stiff nonlinear reaction–diffusion equations with fractional Laplacians that avoids dense matrices. J. Comput. Phys. 287, 254–268 (2015). https://doi.org/10.1016/j.jcp.2015.02.012. http://www.sciencedirect.com/science/article/pii/S0021999115000741

    Article  MathSciNet  Google Scholar 

  27. Strang, G.: A proposal for Toeplitz matrix calculations. Stud. Appl. Math. 74(2), 171–176 (1986)

    Article  Google Scholar 

  28. Van Duin, A.C.: Scalable parallel preconditioning with the sparse approximate inverse of triangular matrices. SIAM J. Matrix Anal. Appl. 20(4), 987–1006 (1999)

    Article  MathSciNet  Google Scholar 

  29. Wolkenfelt, P.H.M.: Linear Multistep Methods and the Construction of Quadrature Formulae for Volterra Integral and Integro–Differential Equations. Tech. Rep. NW 76/79, Mathematisch Centrum, Amsterdam (1979)

    Google Scholar 

Download references

Acknowledgements

We wish to thank two anonymous referees for their constructive comments which have improved the readability of the paper.

This work was supported in part by the 2018 GNCS–INDAM project “Tecniche innovative per problemi di algebra lineare.” The first author acknowledges the MIUR Excellence Department Project awarded to the Department of Mathematics, University of Rome Tor Vergata, CUP E83C18000100006.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Daniele Bertaccini .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Bertaccini, D., Durastante, F. (2019). Efficient Preconditioner Updates for Semilinear Space–Time Fractional Reaction–Diffusion Equations. In: Bini, D., Di Benedetto, F., Tyrtyshnikov, E., Van Barel, M. (eds) Structured Matrices in Numerical Linear Algebra. Springer INdAM Series, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-04088-8_15

Download citation

Publish with us

Policies and ethics