Skip to main content
Log in

On banded M-splitting iteration methods for solving discretized spatial fractional diffusion equations

  • Published:
BIT Numerical Mathematics Aims and scope Submit manuscript

Abstract

For solving time-dependent one-dimensional spatial-fractional diffusion equations of variable coefficients, we establish a banded M-splitting iteration method applicable to compute approximate solutions for the corresponding discrete linear systems resulting from certain finite difference schemes at every temporal level, and demonstrate its asymptotic convergence without imposing any extra condition. Also, we provide a multistep variant for the banded M-splitting iteration method, and prove that the computed solutions of the discrete linear systems by employing this iteration method converge to the exact solutions of the spatial fractional diffusion equations. Numerical experiments show the accuracy and efficiency of the multistep banded M-splitting iteration 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

Similar content being viewed by others

References

  1. Bai, Z.-Z.: Motivations and realizations of Krylov subspace methods for large sparse linear systems. J. Comput. Appl. Math. 283, 71–78 (2015)

    Article  MATH  MathSciNet  Google Scholar 

  2. Bai, Z.-Z., Lu, K.-Y., Pan, J.-Y.: Diagonal and Toeplitz splitting iteration methods for diagonal-plus-Toeplitz linear systems from spatial fractional diffusion equations. Numer. Linear Algebra Appl. 24(e2093), 1–15 (2017)

    MATH  MathSciNet  Google Scholar 

  3. Bai, Z.-Z., Wang, D.-R.: Generalized matrix multisplitting relaxation methods and their convergence. Numer. Math. J. Chin. Univ. (Engl. Ser.) 2, 87–100 (1993)

    MATH  MathSciNet  Google Scholar 

  4. Berman, A., Plemmons, R.J.: Nonnegative Matrices in the Mathematical Sciences (Revised Reprint of the 1979 Original). SIAM, Philadelphia (1994)

    Google Scholar 

  5. Breiten, T., Simoncini, V., Stoll, M.: Low-rank solvers for fractional differential equations. Electron. Trans. Numer. Anal. 45, 107–132 (2016)

    MATH  MathSciNet  Google Scholar 

  6. Carreras, B.A., Lynch, V.E., Zaslavsky, G.M.: Anomalous diffusion and exit time distribution of particle tracers in plasma turbulence model. Phys. Plasmas 8, 5096–5103 (2001)

    Article  Google Scholar 

  7. Deng, W.-H.: Finite element method for the space and time fractional Fokker–Planck equation. SIAM J. Numer. Anal. 47, 204–226 (2008/2009)

  8. Donatelli, M., Mazza, M., Serra-Capizzano, S.: Spectral analysis and structure preserving preconditioners for fractional diffusion equations. J. Comput. Phys. 307, 262–279 (2016)

    Article  MATH  MathSciNet  Google Scholar 

  9. Ervin, V.J., Roop, J.P.: Variational formulation for the stationary fractional advection dispersion equation. Numer. Methods Partial Differ. Equ. 22, 558–576 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  10. Hu, J.-G.: The estimate of \(\Vert M^{-1}N\Vert _{\infty }\) and the optimally scaled matrix. J. Comput. Math. 2, 122–129 (1984)

    MathSciNet  Google Scholar 

  11. Kirchner, J.W., Feng, X.-H., Neal, C.: Fractal stream chemistry and its implications for contaminant transport in catchments. Nature 403, 524–527 (2000)

    Article  Google Scholar 

  12. Kreer, M., Kızılersü, A., Thomas, A.W.: Fractional Poisson processes and their representation by infinite systems of ordinary differential equations. Stat. Probab. Lett. 84, 27–32 (2014)

    Article  MATH  MathSciNet  Google Scholar 

  13. Lei, S.-L., Sun, H.-W.: A circulant preconditioner for fractional diffusion equations. J. Comput. Phys. 242, 715–725 (2013)

    Article  MATH  MathSciNet  Google Scholar 

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

    Article  MATH  MathSciNet  Google Scholar 

  15. Li, X.-J., Xu, C.-J.: Existence and uniqueness of the weak solution of the space-time fractional diffusion equation and a spectral method approximation. Commun. Comput. Phys. 8, 1016–1051 (2010)

    MATH  MathSciNet  Google Scholar 

  16. Lin, F.-R., Yang, S.-W., Jin, X.-Q.: Preconditioned iterative methods for fractional diffusion equation. J. Comput. Phys. 256, 109–117 (2014)

    Article  MATH  MathSciNet  Google Scholar 

  17. Magin, R.L.: Fractional Calculus in Bioengineering. Begell House Publishers, Danbury (2006)

    Google Scholar 

  18. Meerschaert, M.M., Scheffler, H.-P., Tadjeran, C.: Finite difference methods for two-dimensional fractional dispersion equation. J. Comput. Phys. 211, 249–261 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  19. Meerschaert, M.M., Tadjeran, C.: Finite difference approximations for fractional advection-dispersion flow equations. J. Comput. Appl. Math. 172, 65–77 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  20. Meerschaert, M.M., Tadjeran, C.: Finite difference approximations for two-sided space-fractional partial differential equations. Appl. Numer. Math. 56, 80–90 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  21. Metzler, R., Klafter, J.: The random walk’s guide to anomalous diffusion: a fractional dynamics approach. Phys. Rep. 339, 1–77 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  22. Ortega, J.M., Rheinboldt, W.C.: Iterative Solution of Nonlinear Equations in Several Variables. SIAM, Philadelphia (2000)

    Book  MATH  Google Scholar 

  23. Pan, J.-Y., Ke, R.-H., Ng, M.K., Sun, H.-W.: Preconditioning techniques for diagonal-times-Toeplitz matrices in fractional diffusion equations. SIAM J. Sci. Comput. 36, A2698–A2719 (2014)

    Article  MATH  MathSciNet  Google Scholar 

  24. Podlubny, I.: Fractional Differential Equations, Mathematics in Science and Engineering, vol. 198. Academic Press, San Diego (1999)

    MATH  Google Scholar 

  25. Raberto, M., Scalas, E., Mainardi, F.: Waiting-times and returns in high-frequency financial data: an empirical study. Phys. A Stat. Mech. Appl. 314, 749–755 (2002)

    Article  MATH  Google Scholar 

  26. Razminia, K., Razminia, A., Baleanu, D.: Investigation of the fractional diffusion equation based on generalized integral quadrature technique. Appl. Math. Model. 39, 86–98 (2015)

    Article  MATH  MathSciNet  Google Scholar 

  27. Sabatelli, L., Keating, S., Dudley, J., Richmond, P.: Waiting time distributions in financial markets. Eur. Phys. J. B Condens. Matter Phys. 27, 273–275 (2002)

    MathSciNet  Google Scholar 

  28. Samko, S.G., Kilbas, A.A., Marichev, O.I.: Fractional Integrals and Derivatives: Theory and Applications. Gordon and Breach Science, Yverdon (1993)

    MATH  Google Scholar 

  29. Stewart, G.W.: Matrix Algorithms. SIAM, Philadelphia (1998)

    Book  MATH  Google Scholar 

  30. Upadhyay, R.K., Mondal, A.: Dynamics of fractional order modified Morris–Lecar neural model. Netw. Biol. 5, 113–136 (2015)

    Google Scholar 

  31. Varga, R.S.: Matrix Iterative Analysis. Prentice Hall, Englewood Cliffs (1962)

    MATH  Google Scholar 

  32. Varga, R.S.: Geršgorin and His Circles. Springer, Berlin (2004)

    Book  MATH  Google Scholar 

  33. Wang, H., Wang, K.-X., Sircar, T.: A direct \({{\cal{O}}}(N\log ^2N)\) finite difference method for fractional diffusion equations. J. Comput. Phys. 229, 8095–8104 (2010)

    Article  MATH  MathSciNet  Google Scholar 

  34. Zaslavsky, G.M.: Chaos, fractional kinetics, and anomalous transport. Phys. Rep. 371, 461–580 (2002)

    Article  MATH  MathSciNet  Google Scholar 

Download references

Acknowledgements

The authors are very much indebted to Prof. Michiel E. Hochstenbach for constructive suggestions and valuable discussions. The referees provided very useful comments and suggestions, which greatly improved the original manuscript of this paper.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zhong-Zhi Bai.

Additional information

Communicated by Michiel E. Hochstenbach.

This study was supported by the National Natural Science Foundation (No. 11671393), P.R. China.

7 Appendix: Proof of Theorem 4.1

7 Appendix: Proof of Theorem 4.1

In fact, it holds that

$$\begin{aligned} \Vert u^{\tau ,k_o}-u_{\star }^{\tau }\Vert _{\infty } \le \Vert u^{\tau ,k_o}-u^{\tau }\Vert _{\infty }+\Vert u^{\tau }-u_{\star }^{\tau }\Vert _{\infty }. \end{aligned}$$
(7.1)

As \(u_{\star }^{\tau }\) is the exact solution of the fractional diffusion equation (1.1), from the approximation property of the finite-difference formulas it satisfies

$$\begin{aligned} A^{\tau }u_{\star }^{\tau }&=\triangle {t} \, f^{\tau }+u_{\star }^{\tau -1} \\&\quad -\frac{\triangle {t}^2}{2} \, \frac{\partial ^2 u_{\star }^{\tau }}{\partial {t^2}} -\left( 1-\frac{\alpha }{2}\right) h^{\alpha +1} \left( D^{\tau } \, \frac{\partial ^{\alpha +1} u_{\star }^{\tau }}{\partial _{+}{x^{\alpha +1}}} +W^{\tau } \, \frac{\partial ^{\alpha +1} u_{\star }^{\tau }}{\partial _{-}{x^{\alpha +1}}} \right) \\&\quad +\mathscr {O}(\triangle {t}^3)+\mathscr {O}(h^2 \, \triangle {t}). \end{aligned}$$

Hence, by making use of the discrete linear system (2.3) we can obtain the truncated error equation

$$\begin{aligned} A^{\tau }(u^{\tau }-u_{\star }^{\tau }) =u^{\tau -1}-u_{\star }^{\tau -1} +r^{\tau }(x,t) +\mathscr {O}(\triangle {t}^3)+\mathscr {O}(h^2 \, \triangle {t}), \end{aligned}$$
(7.2)

where

$$\begin{aligned} r^{\tau }(x,t) :=\frac{\triangle {t}^2}{2} \, \frac{\partial ^2 u_{\star }^{\tau }}{\partial {t^2}} +\left( 1-\frac{\alpha }{2}\right) h^{\alpha +1} \left( D^{\tau } \, \frac{\partial ^{\alpha +1} u_{\star }^{\tau }}{\partial _{+}{x^{\alpha +1}}} +W^{\tau } \, \frac{\partial ^{\alpha +1} u_{\star }^{\tau }}{\partial _{-}{x^{\alpha +1}}} \right) \end{aligned}$$

is the corresponding error term. With applications of the Lipschitz conditions in (4.2) and the definitions of the diagonal matrices \(D^{\tau }\) and \(W^{\tau }\) in (2.4), it can be verified straightforwardly that this error term satisfies

$$\begin{aligned} \Vert r^{\tau }(x,t)\Vert _{\infty } \le \varepsilon \, \triangle {t}. \end{aligned}$$

The error equation (7.2), together with the upper bound about \(\Vert (A^{\tau })^{-1}\Vert _{\infty }\) in Lemma 2.1, straightforwardly lead to the estimate

$$\begin{aligned} \Vert u_{\star }^{\tau }-u^{\tau }\Vert _{\infty }&\le \Vert (A^{\tau })^{-1}\Vert _{\infty } \left( \Vert u^{\tau -1}-u_{\star }^{\tau -1}\Vert _{\infty } +\varepsilon \, \triangle {t} +\mathscr {O}(\triangle {t}^3)+\mathscr {O}(h^2 \, \triangle {t})\right) \\&\le \frac{1}{1+\eta _{\min } \, \triangle {t}} \left( \Vert u^{\tau -1}-u_{\star }^{\tau -1}\Vert _{\infty } +\varepsilon \, \triangle {t} +\mathscr {O}(\triangle {t}^3)+\mathscr {O}(h^2 \, \triangle {t})\right) \\&\le \frac{1}{(1+\eta _{\min } \, \triangle {t})^{\tau }} \Vert u^{0}-u_{\star }^{0}\Vert _{\infty } +\frac{1}{\eta _{\min } \, \triangle {t}} \left( \varepsilon \, \triangle {t} +\mathscr {O}(\triangle {t}^3)+\mathscr {O}(h^2 \, \triangle {t})\right) \\&=\frac{1}{(1+\eta _{\min } \, \triangle {t})^{\tau }} \Vert u^{0}-u_{\star }^{0}\Vert _{\infty } +\frac{1}{\eta _{\min }} \left( \varepsilon +\mathscr {O}(\triangle {t^2})+\mathscr {O}(h^2) \right) . \end{aligned}$$

Note that \(u^{0}=u_{\star }^{0}\). Then it holds that

$$\begin{aligned} \Vert u^{\tau }-u_{\star }^{\tau }\Vert _{\infty } \le \frac{\varepsilon }{\eta _{\min }} +\mathscr {O}(\triangle {t^2})+\mathscr {O}(h^2). \end{aligned}$$
(7.3)

Moreover, by the Lipschitz continuity of the solution \(u_{\star }(x,t)\) of the fractional diffusion equation (1.1), we can obtain

$$\begin{aligned} \Vert u^{\tau }-u^{\tau -1}\Vert _{\infty }&\le \Vert u_{\star }^{\tau }-u_{\star }^{\tau -1}\Vert _{\infty } +\Vert u^{\tau }-u_{\star }^{\tau }\Vert _{\infty } +\Vert u^{\tau -1}-u_{\star }^{\tau -1}\Vert _{\infty } \nonumber \\&\le \left( \gamma _0 \, \triangle {t} +\frac{2\varepsilon }{\eta _{\min }} \right) +\mathscr {O}(\triangle {t^2})+\mathscr {O}(h^2). \end{aligned}$$
(7.4)

On the other hand, based on the discrete linear system (2.3) and the MBMS iteration method (4.1), we can obtain the iterative error equation

$$\begin{aligned} u^{\tau ,k}-u^{\tau }&=\left[ (M^{\tau })^{-1}N^{\tau } u^{\tau ,k-1} +(M^{\tau })^{-1} \left( \triangle {t} \, f^{\tau }+u^{\tau -1,k_o} \right) \right] \\&\quad -\left[ (M^{\tau })^{-1}N^{\tau } u^{\tau } +(M^{\tau })^{-1} \left( \triangle {t} \, f^{\tau }+u^{\tau -1} \right) \right] \\&=(M^{\tau })^{-1}N^{\tau } \left( u^{\tau ,k-1}-u^{\tau } \right) +(M^{\tau })^{-1} \left( u^{\tau -1,k_o}-u^{\tau -1} \right) \\&= \cdots \\&=\left( (M^{\tau })^{-1}N^{\tau }\right) ^k (u^{\tau ,0}-u^{\tau }) +\sum \limits _{i=0}^{k-1}\left( (M^{\tau })^{-1}N^{\tau }\right) ^i (M^{\tau })^{-1} (u^{\tau -1,k_o}-u^{\tau -1}), \end{aligned}$$

which implies

$$\begin{aligned} \left| u^{\tau ,k_o}-u^{\tau }\right|\le & {} \left( (M^{\tau })^{-1}N^{\tau }\right) ^{k_o} \left| u^{\tau ,0}-u^{\tau }\right| +\sum \limits _{i=0}^{k_o-1}\left( (M^{\tau })^{-1}N^{\tau }\right) ^i (M^{\tau })^{-1} \left| u^{\tau -1,k_o}-u^{\tau -1}\right| \\\le & {} \left( (M^{\tau })^{-1}N^{\tau }\right) ^{k_o} \left| u^{\tau ,0}-u^{\tau }\right| +\sum \limits _{i=0}^{\infty }\left( (M^{\tau })^{-1}N^{\tau }\right) ^i (M^{\tau })^{-1} \left| u^{\tau -1,k_o}-u^{\tau -1}\right| \\= & {} \left( (M^{\tau })^{-1}N^{\tau }\right) ^{k_o} \left| u^{\tau ,0}-u^{\tau }\right| \\&+\sum \limits _{i=0}^{\infty }\left( (M^{\tau })^{-1}N^{\tau }\right) ^i \left[ (M^{\tau })^{-1}A^{\tau }\right] (A^{\tau })^{-1} \left| u^{\tau -1,k_o}-u^{\tau -1}\right| \\= & {} \left( (M^{\tau })^{-1}N^{\tau }\right) ^{k_o} \left| u^{\tau ,0}-u^{\tau }\right| +(A^{\tau })^{-1} \left| u^{\tau -1,k_o}-u^{\tau -1}\right| . \end{aligned}$$

Therefore, it follows from Lemma 2.1 and Theorem 3.1 that

$$\begin{aligned} \Vert u^{\tau ,k_o}-u^{\tau }\Vert _{\infty }\le & {} \Vert (M^{\tau })^{-1}N^{\tau }\Vert _{\infty }^{k_o} \, \Vert u^{\tau ,0}-u^{\tau }\Vert _{\infty } +\Vert (A^{\tau })^{-1}\Vert _{\infty } \, \Vert u^{\tau -1,k_o}-u^{\tau -1}\Vert _{\infty } \nonumber \\\le & {} \sigma ^{k_o} \Vert u^{\tau ,0}-u^{\tau }\Vert _{\infty } +\frac{1}{1+\eta _{\min } \, \triangle {t}} \Vert u^{\tau -1,k_o}-u^{\tau -1}\Vert _{\infty }. \end{aligned}$$
(7.5)

Note that \(u^{\tau ,0}=2u^{\tau -1,k_o}-u^{\tau -2,k_o}\) if \(\tau \ge 2\), and \(u^{\tau ,0}=u^0\) if \(\tau =1\). Based on the equivalent expressions

$$\begin{aligned} u^{\tau ,0}-u^{\tau } =2(u^{\tau -1,k_o}-u^{\tau -1}) -(u^{\tau -2,k_o}-u^{\tau -2}) -(u^{\tau }-u^{\tau -1}) +(u^{\tau -1}-u^{\tau -2}) \end{aligned}$$

for \(\tau \ge 2\), and

$$\begin{aligned} u^{\tau ,0}-u^{\tau } =u^0-u^1 \end{aligned}$$

for \(\tau =1\), by utilizing (7.4) we have the following inequalities:

$$\begin{aligned} \Vert u^{\tau ,0}-u^{\tau }\Vert _{\infty }\le & {} 2\Vert u^{\tau -1,k_o}-u^{\tau -1}\Vert _{\infty } +\Vert u^{\tau -2,k_o}-u^{\tau -2}\Vert _{\infty } \nonumber \\&+\,2 \left( \gamma _0 \, \triangle {t} +\frac{2\varepsilon }{\eta _{\min }} \right) +\mathscr {O}(\triangle {t^2})+\mathscr {O}(h^2) \end{aligned}$$
(7.6)

if \(\tau \ge 2\), and

$$\begin{aligned} \Vert u^{\tau ,0}-u^{\tau }\Vert _{\infty } \le \left( \gamma _0 \, \triangle {t} +\frac{2\varepsilon }{\eta _{\min }} \right) +\mathscr {O}(\triangle {t^2})+\mathscr {O}(h^2) \end{aligned}$$
(7.7)

if \(\tau =1\).

The substitution of (7.6) into (7.5) directly results in the recurrence formula

$$\begin{aligned} \Vert u^{\tau ,k_o}-u^{\tau }\Vert _{\infty }\le & {} \left( 2\sigma ^{k_o}+\frac{1}{1+\eta _{\min } \, \triangle {t}} \right) \Vert u^{\tau -1,k_o}-u^{\tau -1}\Vert _{\infty } +\sigma ^{k_o} \Vert u^{\tau -2,k_o}-u^{\tau -2}\Vert _{\infty } \nonumber \\&+\,\sigma ^{k_o} \left[ 2 \left( \gamma _0 \, \triangle {t} +\frac{2\varepsilon }{\eta _{\min }} \right) +\mathscr {O}(\triangle {t^2})+\mathscr {O}(h^2) \right] \nonumber \\\le & {} \left( 2\sigma ^{k_o}+\frac{1}{1+\eta _{\min } \, \triangle {t}} \right) \Vert u^{\tau -1,k_o}-u^{\tau -1}\Vert _{\infty } +\sigma ^{k_o} \Vert u^{\tau -2,k_o}-u^{\tau -2}\Vert _{\infty } \nonumber \\&+\,2 \left( \gamma _0 \, \triangle {t} +\frac{2\varepsilon }{\eta _{\min }} \right) +\mathscr {O}(\triangle {t^2})+\mathscr {O}(h^2) \end{aligned}$$
(7.8)

for \(\tau \ge 2\). As for \(\tau =1\), it follows from (7.5) and (7.4) that

$$\begin{aligned} \Vert u^{1,k_o}-u^{1}\Vert _{\infty }\le & {} \sigma ^{k_o} \Vert u^{1,0}-u^{1}\Vert _{\infty } +\frac{1}{1+\eta _{\min } \, \triangle {t}} \Vert u^{0,k_o}-u^{0}\Vert _{\infty } \nonumber \\= & {} \sigma ^{k_o} \Vert u^{0}-u^{1}\Vert _{\infty } \nonumber \\\le & {} \sigma ^{k_o} \left[ \left( \gamma _0 \, \triangle {t} +\frac{2\varepsilon }{\eta _{\min }} \right) +\mathscr {O}(\triangle {t^2})+\mathscr {O}(h^2) \right] \nonumber \\= & {} \sigma ^{k_o} \left( \gamma _0 \, \triangle {t} +\frac{2\varepsilon }{\eta _{\min }} \right) +\mathscr {O}(\triangle {t^2})+\mathscr {O}(h^2), \end{aligned}$$
(7.9)

for \(\tau =2\) the recurrence formula (7.8) particularly reads as

$$\begin{aligned} \Vert u^{2,k_o}-u^{2}\Vert _{\infty }\le & {} \left( 2\sigma ^{k_o}+\frac{1}{1+\eta _{\min } \, \triangle {t}} \right) \Vert u^{1,k_o}-u^{1}\Vert _{\infty } \nonumber \\&+\,2 \left( \gamma _0 \, \triangle {t} +\frac{2\varepsilon }{\eta _{\min }} \right) +\mathscr {O}(\triangle {t^2})+\mathscr {O}(h^2) \nonumber \\\le & {} \left( 2\sigma ^{k_o}+\frac{1}{1+\eta _{\min } \, \triangle {t}} \right) \left[ \sigma ^{k_o} \left( \gamma _0 \, \triangle {t} +\frac{2\varepsilon }{\eta _{\min }} \right) +\mathscr {O}(\triangle {t^2})+\mathscr {O}(h^2) \right] \nonumber \\&+\,2 \left( \gamma _0 \, \triangle {t} +\frac{2\varepsilon }{\eta _{\min }} \right) +\mathscr {O}(\triangle {t^2})+\mathscr {O}(h^2) \nonumber \\= & {} \left[ 2+\sigma ^{k_o} \left( 2\sigma ^{k_o}+\frac{1}{1+\eta _{\min } \, \triangle {t}} \right) \right] \left( \gamma _0 \, \triangle {t} +\frac{2\varepsilon }{\eta _{\min }} \right) \nonumber \\&+\,\mathscr {O}(\triangle {t^2})+\mathscr {O}(h^2) \nonumber \\\le & {} \left( 2+\sigma ^{k_o}\right) \left( \gamma _0 \, \triangle {t} +\frac{2\varepsilon }{\eta _{\min }} \right) +\mathscr {O}(\triangle {t^2})+\mathscr {O}(h^2) \nonumber \\\le & {} 3 \left( \gamma _0 \, \triangle {t} +\frac{2\varepsilon }{\eta _{\min }} \right) +\mathscr {O}(\triangle {t^2})+\mathscr {O}(h^2). \end{aligned}$$
(7.10)

Here we have used the fact

$$\begin{aligned} 2\sigma ^{k_o}+\frac{1}{1+\eta _{\min } \, \triangle {t}} \le 1, \end{aligned}$$

which is readily implied by the restriction imposed on the positive integer \(k_o\). We further remark that the restriction on \(k_o\) is equivalent to the condition

$$\begin{aligned} 3\sigma ^{k_o}+\frac{1}{1+\eta _{\min } \, \triangle {t}}< 1 \quad \text{ or }\quad \sigma ^{k_o} < \frac{\eta _{\min } \, \triangle {t}}{3 \left( 1+\eta _{\min } \, \triangle {t}\right) }, \end{aligned}$$
(7.11)

which is equivalent to \(\theta <1\), too.

In addition, denote by

$$\begin{aligned} \tilde{\theta }=\frac{1}{2} \left( 2\sigma ^{k_o}+\frac{1}{1+\eta _{\min } \, \triangle {t}} -\sqrt{ \left( 2\sigma ^{k_o}+\frac{1}{1+\eta _{\min } \, \triangle {t}}\right) ^2 +4\sigma ^{k_o} } \right) . \end{aligned}$$

Then, for \(\tau \ge 3\), we can rewrite the second-order difference inequality (7.8) as

$$\begin{aligned} \Vert u^{\tau ,k_o}-u^{\tau }\Vert _{\infty } -\tilde{\theta } \Vert u^{\tau -1,k_o}-u^{\tau -1}\Vert _{\infty }\le & {} \theta \left( \Vert u^{\tau -1,k_o}-u^{\tau -1}\Vert _{\infty } -\tilde{\theta } \Vert u^{\tau -2,k_o}-u^{\tau -2}\Vert _{\infty } \right) \\&+\,2 \left( \gamma _0 \, \triangle {t} +\frac{2\varepsilon }{\eta _{\min }} \right) +\mathscr {O}(\triangle {t^2})+\mathscr {O}(h^2), \end{aligned}$$

which, by induction, leads to

$$\begin{aligned} \Vert u^{\tau ,k_o}-u^{\tau }\Vert _{\infty }\le & {} \tilde{\theta } \Vert u^{\tau -1,k_o}-u^{\tau -1}\Vert _{\infty } +\theta ^{\tau -2} \left( \Vert u^{2,k_o}-u^{2}\Vert _{\infty } -\tilde{\theta } \Vert u^{1,k_o}-u^{1}\Vert _{\infty } \right) \nonumber \\&+\sum \limits _{i=0}^{\tau -3} \theta ^i \left[ 2 \left( \gamma _0 \, \triangle {t} +\frac{2\varepsilon }{\eta _{\min }} \right) +\mathscr {O}(\triangle {t^2})+\mathscr {O}(h^2) \right] . \end{aligned}$$
(7.12)

Since \(\tilde{\theta }<0\) and \(\sqrt{1+s} \le 1+\frac{1}{2} s\) (for \(\forall s \ge 0\)), it holds that

$$\begin{aligned} |\tilde{\theta }|= & {} \frac{1}{2} \left[ \sqrt{ \left( 2\sigma ^{k_o}+\frac{1}{1+\eta _{\min } \, \triangle {t}} \right) ^2 +4\sigma ^{k_o} } -\left( 2\sigma ^{k_o}+\frac{1}{1+\eta _{\min } \, \triangle {t}} \right) \right] \\= & {} \frac{1}{2} \left( 2\sigma ^{k_o}+\frac{1}{1+\eta _{\min } \, \triangle {t}} \right) \left( \sqrt{1+ \frac{4\sigma ^{k_o}}{\left( 2\sigma ^{k_o}+\frac{1}{1+\eta _{\min } \, \triangle {t}}\right) ^2} }-1 \right) \\\le & {} \frac{1}{2} \left( 2\sigma ^{k_o}+\frac{1}{1+\eta _{\min } \, \triangle {t}} \right) \left[ \left( 1+ \frac{1}{2} \, \frac{4\sigma ^{k_o}}{\left( 2\sigma ^{k_o}+\frac{1}{1+\eta _{\min } \, \triangle {t}}\right) ^2} \right) -1 \right] \\= & {} \frac{\sigma ^{k_o}}{2\sigma ^{k_o}+\frac{1}{1+\eta _{\min } \, \triangle {t}}} \\\le & {} (1+\eta _{\min } \, \triangle {t}) \sigma ^{k_o}. \end{aligned}$$

Thereby, by making use of (7.9) and (7.10), from (7.12) we can obtain the estimate

$$\begin{aligned} \Vert u^{\tau ,k_o}-u^{\tau }\Vert _{\infty }\le & {} \theta ^{\tau -2} \left( \Vert u^{2,k_o}-u^{2}\Vert _{\infty } -\tilde{\theta } \Vert u^{1,k_o}-u^{1}\Vert _{\infty } \right) \nonumber \\&+\,\frac{1-\theta ^{\tau -2}}{1-\theta } \left[ 2 \left( \gamma _0 \, \triangle {t} +\frac{2\varepsilon }{\eta _{\min }} \right) +\mathscr {O}(\triangle {t^2})+\mathscr {O}(h^2) \right] \nonumber \\\le & {} \theta ^{\tau -2} \left\{ 3 \left( \gamma _0 \, \triangle {t} +\frac{2\varepsilon }{\eta _{\min }} \right) +\mathscr {O}(\triangle {t^2})+\mathscr {O}(h^2) \right. \nonumber \\&+\,(1+\eta _{\min } \, \triangle {t}) \sigma ^{k_o} \left. \left[ \left( \gamma _0 \, \triangle {t} +\frac{2\varepsilon }{\eta _{\min }} \right) \sigma ^{k_o} +\mathscr {O}(\triangle {t^2})+\mathscr {O}(h^2) \right] \right\} \nonumber \\&+\,\frac{1-\theta ^{\tau -2}}{1-\theta } \left[ 2 \left( \gamma _0 \, \triangle {t} +\frac{2\varepsilon }{\eta _{\min }} \right) +\mathscr {O}(\triangle {t^2})+\mathscr {O}(h^2) \right] \nonumber \\= & {} \left[ \frac{2-3\theta ^{\tau -1}+\theta ^{\tau -2}}{1-\theta } +\left( 1+\eta _{\min } \, \triangle {t}\right) \sigma ^{2k_o} \right] \left( \gamma _0 \, \triangle {t} +\frac{2\varepsilon }{\eta _{\min }} \right) \nonumber \\&+\,\mathscr {O}(\triangle {t^2})+\mathscr {O}(h^2) \nonumber \\\le & {} \left( \frac{3}{1-\theta } +\eta _{\min }^2 \, \triangle {t^2} \right) \left( \gamma _0 \, \triangle {t} +\frac{2\varepsilon }{\eta _{\min }} \right) +\mathscr {O}(\triangle {t^2})+\mathscr {O}(h^2) \nonumber \\= & {} \frac{3}{1-\theta } \left( \gamma _0 \, \triangle {t} +\frac{2\varepsilon }{\eta _{\min }} \right) +\mathscr {O}(\triangle {t^2})+\mathscr {O}(h^2) \end{aligned}$$
(7.13)

for \(\tau \ge 3\). Here the last inequality has been derived by employing the bounds

$$\begin{aligned} 2-3\theta ^{\tau -1}+\theta ^{\tau -2} \le 3 \left( 1-\theta ^{\tau -1}\right) \le 3 \end{aligned}$$

and

$$\begin{aligned} \left( 1+\eta _{\min } \, \triangle {t}\right) \sigma ^{2k_o}< & {} \left( 1+\eta _{\min } \, \triangle {t}\right) \left[ \frac{\eta _{\min } \, \triangle {t}}{3\left( 1+\eta _{\min } \, \triangle {t}\right) } \right] ^2 \\= & {} \frac{\eta _{\min }^2 \, \triangle {t^2}}{9\left( 1+\eta _{\min } \, \triangle {t}\right) } \\< & {} \eta _{\min }^2 \, \triangle {t^2}; \end{aligned}$$

see (7.11).

Recalling (7.9) and (7.10), we see that (7.13) holds true for all \(\tau \ge 1\), too. Now, the error bound (4.3) follows straightforwardly by substitutions of both (7.3) and (7.13) into (7.1). \(\Box \)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bai, ZZ., Lu, KY. On banded M-splitting iteration methods for solving discretized spatial fractional diffusion equations. Bit Numer Math 59, 1–33 (2019). https://doi.org/10.1007/s10543-018-0727-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10543-018-0727-8

Keywords

Mathematics Subject Classification

Navigation