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Incomplete iterative solution of subdiffusion

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Abstract

In this work, we develop an efficient incomplete iterative scheme for the numerical solution of the subdiffusion model involving a Caputo derivative of order \(\alpha \in (0,1)\) in time. It is based on piecewise linear Galerkin finite element method in space and backward Euler convolution quadrature in time and solves one linear algebraic system inexactly by an iterative algorithm at each time step. We present theoretical results for both smooth and nonsmooth solutions, using novel weighted estimates of the time-stepping scheme. The analysis indicates that with the number of iterations at each time level chosen properly, the error estimates are nearly identical with that for the exact linear solver, and the theoretical findings provide guidelines on the choice. Illustrative numerical results are presented to complement the theoretical analysis.

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References

  1. Alikhanov, A.A.: A new difference scheme for the time fractional diffusion equation. J. Comput. Phys. 280, 424–438 (2015). https://doi.org/10.1016/j.jcp.2014.09.031

    Article  MathSciNet  MATH  Google Scholar 

  2. Alpert, B., Greengard, L., Hagstrom, T.: Rapid evaluation of nonreflecting boundary kernels for time-domain wave propagation. SIAM J. Numer. Anal. 37(4), 1138–1164 (2000). https://doi.org/10.1137/S0036142998336916

    Article  MathSciNet  MATH  Google Scholar 

  3. Bramble, J.H., Pasciak, J.E., Sammon, P.H., Thomée, V.: Incomplete iterations in multistep backward difference methods for parabolic problems with smooth and nonsmooth data. Math. Comput. 52(186), 339–367 (1989)

    Article  MathSciNet  Google Scholar 

  4. Bramble, J.H., Sammon, P.H.: Efficient higher order single step methods for parabolic problems. I. Math. Comput. 35(151), 655–677 (1980). https://doi.org/10.2307/2006186

    Article  MathSciNet  MATH  Google Scholar 

  5. Cuesta, E., Lubich, C., Palencia, C.: Convolution quadrature time discretization of fractional diffusion-wave equations. Math. Comput. 75(254), 673–696 (2006). https://doi.org/10.1090/S0025-5718-06-01788-1

    Article  MathSciNet  MATH  Google Scholar 

  6. Douglas Jr., J., Dupont, T., Ewing, R.E.: Incomplete iteration for time-stepping a Galerkin method for a quasilinear parabolic problem. SIAM J. Numer. Anal. 16(3), 503–522 (1979). https://doi.org/10.1137/0716039

    Article  MathSciNet  MATH  Google Scholar 

  7. Du, Q., Ming, P.: Cascadic multigrid methods for parabolic problems. Sci. China Ser. A 51(8), 1415–1439 (2008). https://doi.org/10.1007/s11425-008-0112-1

    Article  MathSciNet  MATH  Google Scholar 

  8. Gaspar, F.J., Rodrigo, C.: Multigrid waveform relaxation for the time-fractional heat equation. SIAM J. Sci. Comput. 39(4), A1201–A1224 (2017). https://doi.org/10.1137/16M1090193

    Article  MathSciNet  MATH  Google Scholar 

  9. Hackbusch, W.: Multigrid Methods and Applications. Springer, Berlin (1985). https://doi.org/10.1007/978-3-662-02427-0

    Book  MATH  Google Scholar 

  10. Jiang, S., Zhang, J., Zhang, Q., Zhang, Z.: Fast evaluation of the Caputo fractional derivative and its applications to fractional diffusion equations. Commun. Comput. Phys. 21(3), 650–678 (2017). https://doi.org/10.4208/cicp.OA-2016-0136

    Article  MathSciNet  Google Scholar 

  11. Jin, B., Lazarov, R., Zhou, Z.: Two fully discrete schemes for fractional diffusion and diffusion-wave equations with nonsmooth data. SIAM J. Sci. Comput. 38(1), A146–A170 (2016). https://doi.org/10.1137/140979563

    Article  MathSciNet  MATH  Google Scholar 

  12. Jin, B., Lazarov, R., Zhou, Z.: Numerical methods for time-fractional evolution equations with nonsmooth data: a concise overview. Comput. Methods Appl. Mech. Eng. 346, 332–358 (2019). https://doi.org/10.1016/j.cma.2018.12.011

    Article  MathSciNet  MATH  Google Scholar 

  13. Jin, B., Li, B., Zhou, Z.: Correction of high-order BDF convolution quadrature for fractional evolution equations. SIAM J. Sci. Comput. 39(6), A3129–A3152 (2017). https://doi.org/10.1137/17M1118816

    Article  MathSciNet  MATH  Google Scholar 

  14. Jin, B., Li, B., Zhou, Z.: Discrete maximal regularity of time-stepping schemes for fractional evolution equations. Numer. Math. 138(1), 101–131 (2018). https://doi.org/10.1007/s00211-017-0904-8

    Article  MathSciNet  MATH  Google Scholar 

  15. Jin, B., Li, B., Zhou, Z.: Subdiffusion with a time-dependent coefficient: analysis and numerical solution. Math. Comput. 88(319), 2157–2186 (2019). https://doi.org/10.1090/mcom/3413

    Article  MathSciNet  MATH  Google Scholar 

  16. Karaa, S.: Semidiscrete finite element analysis of time fractional parabolic problems: a unified approach. SIAM J. Numer. Anal. 56(3), 1673–1692 (2018). https://doi.org/10.1137/17M1134160

    Article  MathSciNet  MATH  Google Scholar 

  17. Keeling, S.L.: Galerkin/Runge-Kutta discretizations for parabolic equations with time-dependent coefficients. Math. Comput. 52(186), 561–586 (1989). https://doi.org/10.2307/2008483

    Article  MathSciNet  MATH  Google Scholar 

  18. Kilbas, A.A., Srivastava, H.M., Trujillo, J.J.: Theory and Applications of Fractional Differential Equations. Elsevier, Amsterdam (2006)

    MATH  Google Scholar 

  19. Lin, X.L., Lu, X., Ng, M.K., Sun, H.W.: A fast accurate approximation method with multigrid solver for two-dimensional fractional sub-diffusion equation. J. Comput. Phys. 323, 204–218 (2016). https://doi.org/10.1016/j.jcp.2016.07.031

    Article  MathSciNet  MATH  Google Scholar 

  20. Lin, Y., Xu, C.: Finite difference/spectral approximations for the time-fractional diffusion equation. J. Comput. Phys. 225(2), 1533–1552 (2007). https://doi.org/10.1016/j.jcp.2007.02.001

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  22. Lubich, C., Schädle, A.: Fast convolution for nonreflecting boundary conditions. SIAM J. Sci. Comput. 24(1), 161–182 (2002). https://doi.org/10.1137/S1064827501388741

    Article  MathSciNet  MATH  Google Scholar 

  23. Lubich, C., Sloan, I.H., Thomée, V.: Nonsmooth data error estimates for approximations of an evolution equation with a positive-type memory term. Math. Comput. 65(213), 1–17 (1996). https://doi.org/10.1090/S0025-5718-96-00677-1

    Article  MathSciNet  MATH  Google Scholar 

  24. McLean, W., Mustapha, K.: Convergence analysis of a discontinuous Galerkin method for a sub-diffusion equation. Numer. Algorithms 52(1), 69–88 (2009). https://doi.org/10.1007/s11075-008-9258-8

    Article  MathSciNet  MATH  Google Scholar 

  25. Metzler, R., Jeon, J.H., Cherstvy, A.G., Barkai, E.: Anomalous diffusion models and their properties: non-stationarity, non-ergodicity, and ageing at the centenary of single particle tracking. Phys. Chem. Chem. Phys. 16(44), 24128–24164 (2014)

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  27. Mustapha, K., Abdallah, B., Furati, K.M.: A discontinuous Petrov-Galerkin method for time-fractional diffusion equations. SIAM J. Numer. Anal. 52(5), 2512–2529 (2014)

    Article  MathSciNet  Google Scholar 

  28. Saad, Y.: Iterative Methods for Sparse Linear Systems, 2nd edn. SIAM, Philadelphia (2003). https://doi.org/10.1137/1.9780898718003

    Book  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(2), 1057–1079 (2017). https://doi.org/10.1137/16M1082329

    Article  MathSciNet  MATH  Google Scholar 

  30. Sun, Z.Z., Wu, X.: A fully discrete difference scheme for a diffusion-wave system. Appl. Numer. Math. 56(2), 193–209 (2006). https://doi.org/10.1016/j.apnum.2005.03.003

    Article  MathSciNet  MATH  Google Scholar 

  31. Thomée, V.: Galerkin Finite Element Methods for Parabolic Problems, 2nd edn. Springer, Berlin (2006)

    MATH  Google Scholar 

  32. Toselli, A., Widlund, O.: Domain Decomposition Methods-Algorithms and Theory. Springer, Berlin (2005). https://doi.org/10.1007/b137868

    Book  MATH  Google Scholar 

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Correspondence to Zhi Zhou.

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The work of B. Jin is partially supported by UK EPSRC EP/T000864/1, and that of Z. Zhou by Hong Kong RGC Grant No. 25300818.

Appendices

Appendix A: Basic estimates

Lemma 11

For \(\beta ,\gamma \ge 0\), there holds

$$\begin{aligned} \sum _{i=1}^n(n+1-i)^{-\beta }i^{-\gamma } \le \left\{ \begin{array}{ll} cn^{\max (1-\gamma ,0)-\beta }, &{}\quad 0\le \beta <1,\gamma \ne 1,\\ cn^{-\beta }\ln (1+n), &{}\quad 0\le \beta \le 1,\gamma =1,\\ c{n^{-\min (\beta ,\gamma )}}, &{}\quad \beta>1, \gamma >1. \end{array}\right. \end{aligned}$$

Proof

We denote by \([\cdot ]\) the integral part of a real number. Then

$$\begin{aligned} \sum _{i=1}^n(n+1-i)^{-\beta }i^{-\gamma } = \sum _{i=1}^{[{n/2}]}(n+1-i)^{-\beta }i^{-\gamma } + \sum _{i=[{n/2}]+1}^n(n+1-i)^{-\beta }i^{-\gamma }:=\mathrm{I}+\mathrm{II}. \end{aligned}$$

Then, by the trivial inequalities: for \(1\le i\le [{n/2}] \), there holds \((n+1-i)^{-\beta }\le cn^{-\beta }\) and for \([{n/2}]+1\le i\le n\), there holds \(i^{-\gamma }\le cn^{-\gamma }\), we deduce

$$\begin{aligned} \mathrm{I} \le cn^{-\beta } \sum _{i=1}^{[{n/2}]}i^{-\gamma }\quad \text{ and }\quad \mathrm{II}\le cn^{-\gamma } \sum _{i=[{n/2}]+1}^n(n+1-i)^{-\beta }. \end{aligned}$$

Simple computation gives \(\sum _{i=1}^ji^{-\gamma }\le cj^{\max (1-\gamma ,0)}\) if \(\gamma \ne 1\) and \(\sum _{i=1}^ji^{-1}\le c\ln (j+1)\). Combining these estimates yields the desired assertion.\(\square \)

Next we give an upper bound on the CQ weights \(b_j^{(\alpha )}\).

Lemma 12

For the weights \(b_j^{(\alpha )}\), \(|b_j^{(\alpha )}| \le e^{2\alpha }(j+1)^{-\alpha -1}\).

Proof

The weight \(b_j^{(\alpha )}\) is given by \(b_0^{(\alpha )}=1\) and \(b_j^{(\alpha )}= -\varPi _{\ell =1}^j(1-\frac{1+\alpha }{\ell })\) for any \(j\ge 1\). Note the elementary inequality \(\ln (1-x)\le -x\) for any \(x\in (0,1)\), and the estimate \(\sum _{\ell =1}^j\ell ^{-1}\ge \int _1^{j+1}s^{-1}\mathrm{d}s = \ln (j+1).\) Since \(\ln \alpha = \ln (1-(1-\alpha ))\le \alpha -1\), for any \(j\ge 1\),

$$\begin{aligned} \ln |b_j^{(\alpha )}|&= \ln \alpha +\sum _{\ell =2}^j \ln \left( 1-\frac{1+\alpha }{\ell }\right) \le \ln \alpha - \sum _{\ell =2}^j\frac{1+\alpha }{\ell } \\&= \ln \alpha + (1+\alpha ) - \sum _{\ell =1}^j\frac{1+\alpha }{\ell } \le 2\alpha -(1+\alpha ) \ln (j+1). \end{aligned}$$

This completes the proof of the lemma. \(\square \)

Appendix B: Proof of Lemmas 4 and 5

In this part, we provide the proof of Lemmas 4 and 5. The proof of Corollary 1 is identical with that for Lemma 5 and thus it is omitted. The proof relies on the discrete Laplace transform, and the following two well-known estimates

$$\begin{aligned} \quad c_1 |z|&\le |\delta _\tau (e^{-z\tau })| \le c_2|z| \quad \forall \; z\in \varGamma _{\theta ,\delta }^\tau , \end{aligned}$$
(B.1)
$$\begin{aligned} |\delta _\tau (e^{-z\tau })|&\le |z| \sum _{k=1}^\infty \frac{|z\tau |^{k-1}}{k!} \le |z|e^{|z|\tau }, \quad \forall \; z\in \varSigma _\theta , \end{aligned}$$
(B.2)

and the resolvent estimate: for any \(\theta \in (\pi /2,\pi )\),

$$\begin{aligned} \Vert (z+A_h)^{-1}\Vert \le c|z|^{-1},\quad \forall \; z\in \varSigma _\theta . \end{aligned}$$
(B.3)

Now we can give the proof of Lemma 4.

Proof of Lemma 4

By Laplace transform, \(w_h(t_n)={\bar{\partial }}_\tau ^2 u_h(t_n)\) is given by

$$\begin{aligned} w_h(t_n) = \frac{1}{2\pi i} \int _{\varGamma _{\theta ,\delta }}\delta _\tau (e^{-z\tau })^2e^{zt_n}K(z)v_h \mathrm{d}z,\quad \text{ with } K(z) = z^{\alpha -1}(z^\alpha +A_h)^{-1}. \end{aligned}$$

We split the contour \(\varGamma _{\theta ,\delta }\) into \(\varGamma _{\theta ,\delta }^\tau \) and \(\varGamma _{\theta ,\delta } {\setminus }\varGamma _{\theta ,\delta }^\tau \), and denote the corresponding integral by \(\mathrm{I}\) and \(\mathrm{II}\), respectively. We discuss the cases \(v\in L^2(\varOmega )\) and \(v\in D(A)\), separately.

Case (i): \(v\in L^2(\varOmega )\). By (B.1) and (B.3), \(\Vert K(z)\Vert \le c\) for \(z\in \varGamma _{\theta ,\delta }^\tau \). Then choosing \(\delta =c/t_n\) in \(\varGamma _{\theta ,\delta }^\tau \) gives

$$\begin{aligned} \Vert \mathrm{I}\Vert _{L^2(\varOmega )} \le c \Vert v_h \Vert _{L^2(\varOmega )} \Big (\int _{\frac{c}{t_n}}^{\frac{\pi \sin \theta }{\tau }} \rho e^{t_n\rho \cos \theta } \,\mathrm{d}\rho + \int _{-\theta }^\theta t_n^{-2} \,\mathrm{d}\varphi \Big ) \le c t_n^{-2} \Vert v_h \Vert _{L^2(\varOmega )}. \end{aligned}$$

For any \(z=\rho e^{\pm \mathrm {i}\theta }\in \varGamma _{\theta ,\delta }{\setminus }\varGamma _{\theta ,\delta }^\tau \), by the estimates (B.2) and (B.3), \( \Vert K(z)\Vert \le ce^{2\rho \tau }.\) By choosing \(\theta \in (\pi /2,\pi )\) sufficiently close to \(\pi \), we deduce

$$\begin{aligned} \Vert \mathrm{II}\Vert _{L^2(\varOmega )} \le c \Vert v_h\Vert _{L^2(\varOmega )} \int _{\frac{\pi \sin \theta }{\tau }}^\infty e^{\rho (\cos \theta t_n+2\tau )} \rho \,\mathrm{d}\rho \le ct_n^{-2} \Vert v_h\Vert _{L^2(\varOmega )}. \end{aligned}$$

Thus, \(\Vert {\bar{\partial }}_\tau ^2u_h(t_n)\Vert \le c t_n^{-2}\Vert v_h\Vert _{L^2(\varOmega )}.\) Next, by the identity \(A_h(z^\alpha +A_h)^{-1}=I-z^\alpha (z^\alpha +A_h)\) and (B.3), \(\Vert A_hK(z)\Vert \le |z|^{\alpha -1}\) for \(z\in \varSigma _\theta \). Then repeating the argument gives

$$\begin{aligned} \tau ^\alpha \Vert A_h {\bar{\partial }}_\tau ^2 u_h(t_n)\Vert \le c \tau ^\alpha t_n^{-2-\alpha } \Vert v_h\Vert _{L^2(\varOmega )} \le ct_n^{-2} \Vert v_h \Vert _{L^2(\varOmega )}. \end{aligned}$$

Then the assertion for the case \(v\in L^2(\varOmega )\) follows from the triangle inequality.

Case (ii): \(v\in D(A)\). Simple computation gives the identity \(K(z)v_h=z^{\alpha -1}(z^\alpha +A_h)^{-1}v_h = z^{-1}v_h - z^{-\alpha }(z^\alpha +A_h)^{-1}A_hv_h\). Thus, we have

$$\begin{aligned} w_h(t_n) = - \frac{1}{2\pi i} \int _{\varGamma _{\theta ,\delta }}e^{zt_n} \delta _\tau (e^{-z\tau })^2 z^{-\alpha }K(z)A_h v_h \mathrm{d}z, \end{aligned}$$

in which we split the contour \(\varGamma _{\theta ,\delta }\) into \(\varGamma _{\theta ,\delta }^\tau \) and \(\varGamma _{\theta ,\delta }{\setminus } \varGamma _{\theta ,\delta }^\tau \), and accordingly the integral. Then the rest of the proof follows from the estimates (B.1), (B.2) and (B.3) as before.\(\square \)

Last, we prove Lemma 5.

Proof of Lemma 5

By Laplace transform and its discrete analogue, we have

$$\begin{aligned} \partial _t^\alpha y_h(t_n)-{\bar{\partial }}_\tau ^\alpha y_h(t_n)&= \frac{1}{2\pi \mathrm {i}} \int _{\varGamma _{\theta ,\delta }^\tau } e^{zt_n} K(z) A_h v_h\,\mathrm{d}z + \frac{1}{2\pi \mathrm {i}} \int _{\varGamma _{\theta ,\delta }{\setminus }\varGamma _{\theta ,\delta }^\tau } e^{zt_n} K(z) A_h v_h\,\mathrm{d}z\\&:=\mathrm{I}+\mathrm{II}, \end{aligned}$$

with \(K(z)=(\delta _\tau (e^{-z\tau })^{\alpha }-z^\alpha )z^{ -1} (z^\alpha +A_h)^{-1}\). Recall the following estimate:

$$\begin{aligned} | \delta _\tau (e^{-z\tau })^{\alpha }-z^\alpha | \le c \tau z^{1+\alpha },\quad \forall \; z\in \varGamma _{\theta ,\delta }^\tau . \end{aligned}$$
(B.4)

Then by choosing \(\delta =c/t_n\) in the contour \(\varGamma _{\theta , \delta }^\tau \) and the resolvent estimate (B.3), we obtain

$$\begin{aligned} \Vert \mathrm{I}\Vert _{L^2(\varOmega )}&\le c \tau \Vert A_h v_h \Vert _{L^2(\varOmega )} \Big (\int _{\frac{c}{t_n}}^{\frac{\pi \sin \theta }{\tau }} e^{-c\rho t_n} \,\mathrm{d}\rho + \int _{-\theta }^\theta ct_n^{-1} \,\mathrm{d}\varphi \Big ) \le c\tau t_n^{-1} \Vert A v \Vert _{L^2(\varOmega )}. \end{aligned}$$

Further, by (B.2), for any \(z=\rho e^{\pm \mathrm {i}\theta }\in \varGamma _{\theta ,\delta }{\setminus }\varGamma _{\theta ,\delta }^\tau \) and choosing \(\theta \in (\pi /2,\pi )\) close to \(\pi \),

$$\begin{aligned} |e^{zt_n} (\delta _\tau (e^{-z\tau })^{\alpha }-z^\alpha )z^{ -1}|&\le e^{t_n\rho \cos \theta }(c|z|^\alpha e^{\alpha \rho \tau }+|z|^\alpha )|z|^{-1}\le c|z|^{\alpha -1}e^{-c\rho t_n}. \end{aligned}$$

Then we deduce

$$\begin{aligned} \Vert \mathrm{II}\Vert _{L^2(\varOmega )}&\le c \Vert A_hv_h\Vert _{L^2(\varOmega )} \int _{\frac{\pi \sin \theta }{\tau }}^\infty e^{-c\rho t_n} \rho ^{-1}\,\mathrm{d}\rho \le c\tau t_n^{-1} \Vert Av\Vert _{L^2(\varOmega )}. \end{aligned}$$

Thus, we show the assertion for \(\beta =0\). For the case \(\beta =1\), the identity \(A_h(z^\alpha +A_h)^{-1}=I- z^\alpha (z^\alpha +A_h)\), (B.3) and (B.4) give

$$\begin{aligned} \Vert A_h\mathrm{I}\Vert _{L^2(\varOmega )}&\le c \tau \Vert A_h v_h\Vert _{L^2(\varOmega )} \Big (\int _{\frac{c}{t_n}}^{\frac{\pi \sin \theta }{\tau }} e^{-c\rho t_n}\rho ^\alpha \,\mathrm{d}\rho + \int _{-\theta }^\theta ct_n^{-1-\alpha } \,\mathrm{d}\varphi \Big )\\&\le c\tau t_n^{-1-\alpha } \Vert A v\Vert _{L^2(\varOmega )}, \end{aligned}$$

and the bound on \(\Vert A_h\mathrm{II}\Vert _{L^2(\varOmega )}\) follows analogously, completing the proof for \(\beta =1\). Then the case \(\beta \in (0,1)\) follows by interpolation. This shows part (i). The proof of part (ii) is similar and applies the \(L^2(\varOmega )\) stability of \(P_h\), and hence the detail is omitted. \(\square \)

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Jin, B., Zhou, Z. Incomplete iterative solution of subdiffusion. Numer. Math. 145, 693–725 (2020). https://doi.org/10.1007/s00211-020-01128-w

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