Skip to main content
Log in

A Preconditioning Technique for an All-at-once System from Volterra Subdiffusion Equations with Graded Time Steps

  • Published:
Journal of Scientific Computing Aims and scope Submit manuscript

Abstract

Volterra subdiffusion problems with weakly singular kernel describe the dynamics of subdiffusion processes well. The graded L1 scheme is often chosen to discretize such problems since it can handle the singularity of the solution near \(t = 0\). In this paper, we propose a modification. We first split the time interval [0, T] into \([0, T_0]\) and \([T_0, T]\), where \(T_0\) (\(0< T_0 < T\)) is reasonably small. Then, the graded L1 scheme is applied in \([0, T_0]\), while the uniform one is used in \([T_0, T]\). Our all-at-once system is derived based on this strategy. In order to solve the arising system efficiently, we split it into two subproblems and design two preconditioners. Some properties of these two preconditioners are also investigated. Moreover, we extend our method to solve semilinear subdiffusion problems. Numerical results are reported to show the efficiency of our method.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. A parallel version of FFT is available at http://www.fftw.org/parallel/parallel-fftw.html.

References

  1. Sokolov, I.M., Klafter, J., Blumen, A.: Fractional kinetics. Phys. Today 55, 48–54 (2002)

    Google Scholar 

  2. Metzler, R., Schick, W., Kilian, H.-G., Nonnenmacher, T.F.: Relaxation in filled polymers: a fractional calculus approach. J. Chem. Phys. 103, 7180–7186 (1995)

    Google Scholar 

  3. He, J.-H.: Approximate analytical solution for seepage flow with fractional derivatives in porous media. Comput. Meth. Appl. Mech. Eng. 167, 57–68 (1998)

    MathSciNet  MATH  Google Scholar 

  4. del-Castillo-Negrete, D., Carreras, B., Lynch, V.: Fractional diffusion in plasma turbulence. Phys. Plasmas 11, 3854–3864 (2004)

  5. Metzler, R., Klafter, J.: Boundary value problems for fractional diffusion equations. Physica A 278, 107–125 (2000)

    MathSciNet  MATH  Google Scholar 

  6. Gorenflo, R., Mainardi, F., Moretti, D., Paradisi, P.: Time fractional diffusion: a discrete random walk approach. Nonlinear Dyn. 29, 129–143 (2002)

    MathSciNet  MATH  Google Scholar 

  7. Podlubny, I., Chechkin, A., Skovranek, T., Chen, Y., Jara, B.M.V.: Matrix approach to discrete fractional calculus II: Partial fractional differential equations. J. Comput. Phys. 228, 3137–3153 (2009)

    MathSciNet  MATH  Google Scholar 

  8. Pagnini, G., Paradisi, P.: A stochastic solution with Gaussian stationary increments of the symmetric space-time fractional diffusion equation. Fract. Calc. Appl. Anal. 19, 408–440 (2016)

    MathSciNet  MATH  Google Scholar 

  9. Podlubny, I.: Fractional Differential Equations, vol. 198. Academic Press, San Diego, CA (1998)

    MATH  Google Scholar 

  10. Lin, X.-L., Ng, M.K., Sun, H.-W.: Crank-Nicolson alternative direction implicit method for space-fractional diffusion equations with nonseparable coefficients. SIAM J. Numer. Anal. 57, 997–1019 (2019)

    MathSciNet  MATH  Google Scholar 

  11. Lei, S.-L., Wang, W., Chen, X., Ding, D.: A fast preconditioned penalty method for American options pricing under regime-switching tempered fractional diffusion models. J. Sci. Comput. 75, 1633–1655 (2018)

    MathSciNet  MATH  Google Scholar 

  12. Shen, J., Li, C., Sun, Z.-Z.: An H2N2 interpolation for Caputo derivative with order in (1,2) and its application to time-fractional wave equations in more than one space dimension. J. Sci. Comput. 83, 38 (2020). https://doi.org/10.1007/s10915-020-01219-8

    Article  MathSciNet  MATH  Google Scholar 

  13. Liao, H.-L., McLean, W., Zhang, J.: A discrete Grönwall inequality with applications to numerical schemes for subdiffusion problems. SIAM J. Numer. Anal. 57, 218–237 (2019)

    MathSciNet  MATH  Google Scholar 

  14. Cao, J., Song, G., Wang, J., Shi, Q., Sun, S.: Blow-up and global solutions for a class of time fractional nonlinear reaction-diffusion equation with weakly spatial source. Appl. Math. Lett. 91, 201–206 (2019)

    MathSciNet  MATH  Google Scholar 

  15. Gu, X.-M., Wu, S.-L.: A parallel-in-time iterative algorithm for Volterra partial integral-differential problems with weakly singular kernel. J. Comput. Phys. 417, 109576 (2020). https://doi.org/10.1016/j.jcp.2020.109576

    Article  MathSciNet  MATH  Google Scholar 

  16. Zhao, Y.-L., Zhu, P.-Y., Gu, X.-M., Zhao, X.-L., Jian, H.-Y.: A preconditioning technique for all-at-once system from the nonlinear tempered fractional diffusion equation. J. Sci. Comput. 83, 10 (2020). https://doi.org/10.1007/s10915-020-01193-1

    Article  MathSciNet  MATH  Google Scholar 

  17. Li, M., Zhao, Y.-L.: A fast energy conserving finite element method for the nonlinear fractional Schrödinger equation with wave operator. Appl. Math. Comput. 338, 758–773 (2018)

    MathSciNet  MATH  Google Scholar 

  18. Bouchaud, J.-P., Georges, A.: Anomalous diffusion in disordered media: statistical mechanisms, models and physical applications. Phys. Rep. 195, 127–293 (1990)

    MathSciNet  Google Scholar 

  19. Lin, Y., Xu, C.: Finite difference/spectral approximations for the time-fractional diffusion equation. J. Comput. Phys. 225, 1533–1552 (2007)

    MathSciNet  MATH  Google Scholar 

  20. Li, X., Xu, C.: A space-time spectral method for the time fractional diffusion equation. SIAM J. Numer. Anal. 47, 2108–2131 (2009)

    MathSciNet  MATH  Google Scholar 

  21. Gao, G.-H., Sun, Z.-Z., Zhang, H.-W.: A new fractional numerical differentiation formula to approximate the Caputo fractional derivative and its applications. J. Comput. Phys. 259, 33–50 (2014)

    MathSciNet  MATH  Google Scholar 

  22. Zhang, Y.-N., Sun, Z.-Z., Liao, H.-L.: Finite difference methods for the time fractional diffusion equation on non-uniform meshes. J. Comput. Phys. 265, 195–210 (2014)

    MathSciNet  MATH  Google Scholar 

  23. Alikhanov, A.A.: A new difference scheme for the time fractional diffusion equation. J. Comput. Phy. 280, 424–438 (2015)

    MathSciNet  MATH  Google Scholar 

  24. Jin, B., Lazarov, R., Zhou, Z.: An analysis of the L1 scheme for the subdiffusion equation with nonsmooth data. IMA J. Numer. Anal. 36, 197–221 (2016)

    MathSciNet  MATH  Google Scholar 

  25. Zeng, F., Li, C., Liu, F., Turner, I.: Numerical algorithms for time-fractional subdiffusion equation with second-order accuracy. SIAM J. Sci. Comput. 37, A55–A78 (2015)

    MathSciNet  MATH  Google Scholar 

  26. Hu, X., Rodrigo, C., Gaspar, F.J.: Using hierarchical matrices in the solution of the time-fractional heat equation by multigrid waveform relaxation. J. Comput. Phys. 416, 109540 (2020). https://doi.org/10.1016/j.jcp.2020.109540

  27. Mustapha, K., AlMutawa, J.: A finite difference method for an anomalous sub-diffusion equation, theory and applications. Numer. Algorithms 61, 525–543 (2012)

    MathSciNet  MATH  Google Scholar 

  28. Mustapha, K.: An implicit finite-difference time-stepping method for a sub-diffusion equation, with spatial discretization by finite elements. IMA J. Numer. Anal. 31, 719–739 (2011)

    MathSciNet  MATH  Google Scholar 

  29. Stynes, M., O’Riordan, E., Gracia, J.L.: Error analysis of a finite difference method on graded meshes for a time-fractional diffusion equation. SIAM J. Numer. Anal. 55, 1057–1079 (2017)

    MathSciNet  MATH  Google Scholar 

  30. Liao, H.-L., Li, D., Zhang, J.: Sharp error estimate of the nonuniform L1 formula for linear reaction-subdiffusion equations. SIAM J. Numer. Anal. 56, 1112–1133 (2018)

    MathSciNet  MATH  Google Scholar 

  31. Lubich, C., Sloan, I., Thomée, V.: Nonsmooth data error estimates for approximations of an evolution equation with a positive-type memory term. Math. Comput. 65, 1–17 (1996)

    MathSciNet  MATH  Google Scholar 

  32. Zeng, F., Zhang, Z., Karniadakis, G.E.: Second-order numerical methods for multi-term fractional differential equations: smooth and non-smooth solutions. Comput. Meth. Appl. Mech. Eng. 327, 478–502 (2017)

    MathSciNet  MATH  Google Scholar 

  33. Yan, Y., Khan, M., Ford, N.J.: An analysis of the modified L1 scheme for time-fractional partial differential equations with nonsmooth data. SIAM J. Numer. Anal. 56, 210–227 (2018)

    MathSciNet  MATH  Google Scholar 

  34. Jin, B., Lazarov, R., Zhou, Z.: Numerical methods for time-fractional evolution equations with nonsmooth data: a concise overview. Comput. Meth. Appl. Mech. Eng. 346, 332–358 (2019)

    MathSciNet  MATH  Google Scholar 

  35. Wang, Y., Yan, Y., Yan, Y., Pani, A.K.: Higher order time stepping methods for subdiffusion problems based on weighted and shifted Grünwald-Letnikov formulae with nonsmooth data. J. Sci. Comput. 83, 40 (2020). https://doi.org/10.1007/s10915-020-01223-y

    Article  MATH  Google Scholar 

  36. Kwon, K., Sheen, D.: A parallel method for the numerical solution of integro-differential equation with positive memory. Comput. Meth. Appl. Mech. Eng. 192, 4641–4658 (2003)

    MathSciNet  MATH  Google Scholar 

  37. McLean, W., Thomée, V.: Maximum-norm error analysis of a numerical solution via Laplace transformation and quadrature of a fractional-order evolution equation. IMA J. Numer. Anal. 30, 208–230 (2010)

    MathSciNet  MATH  Google Scholar 

  38. Li, X., Tang, T., Xu, C.: Parallel in time algorithm with spectral-subdomain enhancement for Volterra integral equations. SIAM J. Numer. Anal. 51, 1735–1756 (2013)

    MathSciNet  MATH  Google Scholar 

  39. Wu, S.-L., Zhou, T.: Parareal algorithms with local time-integrators for time fractional differential equations. J. Comput. Phys. 358, 135–149 (2018)

    MathSciNet  MATH  Google Scholar 

  40. Fu, H., Wang, H.: A preconditioned fast parareal finite difference method for space-time fractional partial differential equation. J. Sci. Comput. 78, 1724–1743 (2019)

    MathSciNet  MATH  Google Scholar 

  41. Ke, R., Ng, M.K., Sun, H.-W.: A fast direct method for block triangular Toeplitz-like with tri-diagonal block systems from time-fractional partial differential equations. J. Comput. Phys. 303, 203–211 (2015)

    MathSciNet  MATH  Google Scholar 

  42. Huang, Y.-C., Lei, S.-L.: A fast numerical method for block lower triangular Toeplitz with dense Toeplitz blocks system with applications to time-space fractional diffusion equations. Numer. Algorithms 76, 605–616 (2017)

    MathSciNet  MATH  Google Scholar 

  43. Lu, X., Pang, H.-K., Sun, H.-W.: Fast approximate inversion of a block triangular Toeplitz matrix with applications to fractional sub-diffusion equations. Numer. Linear Algebr. Appl. 22, 866–882 (2015)

    MathSciNet  MATH  Google Scholar 

  44. Lu, X., Pang, H.-K., Sun, H.-W., Vong, S.-W.: Approximate inversion method for time-fractional subdiffusion equations. Numer. Linear Algebr. Appl. 25, e2132 (2018). https://doi.org/10.1002/nla.2132

    Article  MathSciNet  MATH  Google Scholar 

  45. Bertaccini, D., Durastante, F.: Limited memory block preconditioners for fast solution of fractional partial differential equations. J. Sci. Comput. 77, 950–970 (2018)

    MathSciNet  MATH  Google Scholar 

  46. Bertaccini, D., Durastante, F.: Block structured preconditioners in tensor form for the all-at-once solution of a finite volume fractional diffusion equation. Appl. Math. Lett. 95, 92–97 (2019)

    MathSciNet  MATH  Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  48. Van der Vorst, H.A.: Bi-CGSTAB: a fast and smoothly converging variant of Bi-CG for the solution of nonsymmetric linear systems. SIAM J. Sci. Statist. Comput. 13, 631–644 (1992)

    MathSciNet  MATH  Google Scholar 

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

    MATH  Google Scholar 

  50. Murphy, M.F., Golub, G.H., Wathen, A.J.: A note on preconditioning for indefinite linear systems. SIAM J. Sci. Comput. 21, 1969–1972 (2000)

    MathSciNet  MATH  Google Scholar 

  51. Moroney, T., Yang, Q.: Efficient solution of two-sided nonlinear space-fractional diffusion equations using fast Poisson preconditioners. J. Comput. Phy. 246, 304–317 (2013)

    MathSciNet  MATH  Google Scholar 

  52. Gu, X.-M., Zhao, Y.-L., Zhao, X.-L., Carpentieri, B., Huang, Y.-Y.: A note on parallel preconditioning for the all-at-once solution of Riesz fractional diffusion equations. Numer. Math. Theor. Meth. Appl. (2021). https://doi.org/10.4208/nmtma.OA-2020-0020

  53. Liao, H.-L., Yan, Y., Zhang, J.: Unconditional convergence of a fast two-level linearized algorithm for semilinear subdiffusion equations. J. Sci. Comput. 80, 1–25 (2019)

    MathSciNet  MATH  Google Scholar 

  54. Greenbaum, A.: Iterative Methods for Solving Linear Systems. SIAM, Philadelphia, PA (1997)

    MATH  Google Scholar 

Download references

Acknowledgements

This research is supported by the National Natural Science Foundation of China (No. 11801463) and the Applied Basic Research Program of Sichuan Province (No. 2020YJ0007). The first author is also supported by the China Scholarship Council. We would like to express our sincere thanks to the referees for insightful comments and invaluable suggestions that greatly improved the representation of this paper.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Xian-Ming Gu or Alexander Ostermann.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, YL., Gu, XM. & Ostermann, A. A Preconditioning Technique for an All-at-once System from Volterra Subdiffusion Equations with Graded Time Steps. J Sci Comput 88, 11 (2021). https://doi.org/10.1007/s10915-021-01527-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10915-021-01527-7

Keywords

Navigation