Skip to main content
Log in

Computational Complexity Study on Krylov Integration Factor WENO Method for High Spatial Dimension Convection–Diffusion Problems

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

Abstract

Integration factor (IF) methods are a class of efficient time discretization methods for solving stiff problems via evaluation of an exponential function of the corresponding matrix for the stiff operator. The computational challenge in applying the methods for partial differential equations (PDEs) on high spatial dimensions (multidimensional PDEs) is how to deal with the matrix exponential for very large matrices. Compact integration factor methods developed in Nie et al. (J Comput Phys 227:5238–5255, 2008) provide an approach to reduce the cost prohibitive large matrix exponentials for linear diffusion operators with constant diffusion coefficients in high spatial dimensions to a series of much smaller one dimensional computations. This approach is further developed in Wang et al. (J Comput Phys 258:585–600, 2014) to deal with more complicated high dimensional reaction–diffusion equations with cross-derivatives in diffusion operators. Another approach is to use Krylov subspace approximations to efficiently calculate large matrix exponentials. In Chen and Zhang (J Comput Phys 230:4336–4352, 2011), Krylov subspace approximation is directly applied to the implicit integration factor (IIF) methods for solving high dimensional reaction–diffusion problems. Recently the method is combined with weighted essentially non-oscillatory (WENO) schemes in Jiang and Zhang (J Comput Phys 253:368–388, 2013) to efficiently solve semilinear and fully nonlinear convection–reaction–diffusion equations. A natural question that arises is how these two approaches may perform differently for various types of problems. In this paper, we study the computational power of Krylov IF-WENO methods for solving high spatial dimension convection–diffusion PDE problems (up to four spatial dimensions). Systematical numerical comparison and complexity analysis are carried out for the computational efficiency of the two different approaches. We show that although the Krylov IF-WENO methods have linear computational complexity, both the compact IF method and the Krylov IF method have their own advantages for different type of problems. This study provides certain guidance for using IF-WENO methods to solve general high spatial dimension convection–diffusion problems.

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
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Ascher, U., Ruuth, S., Wetton, B.: Implicit–explicit methods for time-dependent PDE’s. SIAM J. Numer. Anal. 32, 797–823 (1995)

    Article  MATH  MathSciNet  Google Scholar 

  2. Ashe, H.L., Levine, M.: Local inhibition and long-range enhancement of Dpp signal transduction by Sog. Nature 398, 427–431 (1999)

    Article  Google Scholar 

  3. Beylkin, G., Keiser, J.M., Vozovoi, L.: A new class of time discretization schemes for the solution of nonlinear PDEs. J. Comput. Phys. 147, 362–387 (1998)

    Article  MATH  MathSciNet  Google Scholar 

  4. Bourlioux, A., Layton, A.T., Minion, M.L.: High-order multi-implicit spectral deferred correction methods for problems of reactive flow. J. Comput. Phys. 189, 651–675 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  5. Chen, S., Zhang, Y.-T.: Krylov implicit integration factor methods for spatial discretization on high dimensional unstructured meshes: application to discontinuous Galerkin methods. J. Comput. Phys. 230, 4336–4352 (2011)

    Article  MATH  MathSciNet  Google Scholar 

  6. Christlieb, A., Ong, B., Qiu, J.-M.: Integral deferred correction methods constructed with high order Runge–Kutta integrators. Math. Comput. 79, 761–783 (2010)

    Article  MATH  MathSciNet  Google Scholar 

  7. Cox, S.M., Matthews, P.C.: Exponential time differencing for stiff systems. J. Comput. Phys. 176, 430–455 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  8. Dutt, A., Greengard, L., Rokhlin, V.: Spectral deferred correction methods for ordinary differential equations. BIT 40(2), 241–266 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  9. Fokker, A.D.: Die mittlere energie rotierender elektrischer dipole im strahlungsfeld. Ann. Phys. 348, 810–820 (1914)

    Article  Google Scholar 

  10. Gallopoulos, E., Saad, Y.: Efficient solution of parabolic equations by Krylov approximation methods. SIAM J. Sci. Stat. Comput. 13(5), 1236–1264 (1992)

    Article  MATH  MathSciNet  Google Scholar 

  11. Gottlieb, S., Shu, C.-W.: Total variation diminishing Runge–Kutta schemes. Math. Comput. 67, 73–85 (1998)

    Article  MATH  MathSciNet  Google Scholar 

  12. Gottlieb, S., Shu, C.-W., Tadmor, E.: Strong stability preserving high order time discretization methods. SIAM Rev. 43, 89–112 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  13. Harten, A., Engquist, B., Osher, S., Chakravarthy, S.: Uniformly high order essentially non-oscillatory schemes, III. J. Comput. Phys. 71, 231–303 (1987)

    Article  MATH  MathSciNet  Google Scholar 

  14. Higham, N.J.: The scaling and squaring method for the matrix exponential revisited. SIAM Rev. 51(4), 747–764 (2009)

    Article  MATH  MathSciNet  Google Scholar 

  15. Hu, C., Shu, C.-W.: Weighted essentially non-oscillatory schemes on triangular meshes. J. Comput. Phys. 150, 97–127 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  16. Huang, J., Jia, J., Minion, M.: Arbitrary order Krylov deferred correction methods for differential algebraic equations. J. Comput. Phys. 221(2), 739–760 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  17. Hochbruck, M., Lubich, C.: On Krylov subspace approximations to the matrix exponential operator. SIAM J. Numer. Anal. 34, 1911–1925 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  18. Hundsdorfer, W., Verwer, J.: Numerical Solution of Time-Dependent Advection–Diffusion–Reaction Equations. Springer, Berlin (2003)

    Book  MATH  Google Scholar 

  19. Jiang, G.-S., Shu, C.-W.: Efficient implementation of weighted ENO schemes. J. Comput. Phys. 126, 202–228 (1996)

    Article  MATH  MathSciNet  Google Scholar 

  20. Jiang, T., Zhang, Y.-T.: Krylov implicit integration factor WENO methods for semilinear and fully nonlinear advection–diffusion–reaction equations. J. Comput. Phys. 253, 368–388 (2013)

    Article  MATH  MathSciNet  Google Scholar 

  21. Jiang, T., Zhang, Y.-T.: Krylov single-step implicit integration factor WENO methods for advection–diffusion–reaction equations. J. Comput. Phys. 311, 22–44 (2016)

    Article  MATH  MathSciNet  Google Scholar 

  22. Ju, L., Zhang, J., Zhu, L., Du, Q.: Fast explicit integration factor methods for semilinear parabolic equations. J. Sci. Comput. 62, 431–455 (2015)

    Article  MATH  MathSciNet  Google Scholar 

  23. Kanevsky, A., Carpenter, M.H., Gottlieb, D., Hesthaven, J.S.: Application of implicit–explicit high order Runge–Kutta methods to discontinuous-Galerkin schemes. J. Comput. Phys. 225(2), 1753–1781 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  24. Kassam, A.-K., Trefethen, L.N.: Fourth-order time stepping for stiff PDEs. SIAM J. Sci. Comput. 26(4), 1214–1233 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  25. Kennedy, C.A., Carpenter, M.H.: Additive Runge–Kutta schemes for convection–diffusion–reaction equations. Appl. Numer. Math. 44, 139–181 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  26. Layton, A.T., Minion, M.L.: Conservative multi-implicit spectral deferred correction methods for reacting gas dynamics. J. Comput. Phys. 194(2), 697–715 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  27. Lander, A., Nie, Q., Wan, F., Zhang, Y.-T.: Localized ectopic expression of Dpp receptors in a Drosophila embryo. Stud. Appl. Math. 123, 175–214 (2009)

    Article  MATH  MathSciNet  Google Scholar 

  28. Liu, X.-D., Osher, S., Chan, T.: Weighted essentially non-oscillatory schemes. J. Comput. Phys. 115, 200–212 (1994)

    Article  MATH  MathSciNet  Google Scholar 

  29. Liu, X.F., Nie, Q.: Compact integration factor methods for complex domains and adaptive mesh refinement. J. Comput. Phys. 229(16), 5692–5706 (2010)

    Article  MATH  MathSciNet  Google Scholar 

  30. Liu, Y., Zhang, Y.-T.: A robust reconstruction for unstructured WENO schemes. J. Sci. Comput. 54, 603–621 (2013)

    Article  MATH  MathSciNet  Google Scholar 

  31. Lu, D., Zhang, Y.-T.: Krylov integration factor method on sparse grids for high spatial dimension convection–diffusion equations. J. Sci. Comput. 69, 736–763 (2016)

    Article  MATH  MathSciNet  Google Scholar 

  32. Lu, J., Fang, J., Tan, S., Shu, C.-W., Zhang, M.: Inverse Lax–Wendroff procedure for numerical boundary conditions of convection–diffusion equations. J. Comput. Phys. 317, 276–300 (2016)

    Article  MATH  MathSciNet  Google Scholar 

  33. Maday, Y., Patera, A.T., Ronquist, E.M.: An operator-integration-factor splitting method for time-dependent problems: application to incompressible fluid flow. J. Sci. Comput. 5, 263–292 (1990)

    Article  MATH  MathSciNet  Google Scholar 

  34. Minion, M.L.: Semi-implicit spectral deferred correction methods for ordinary differential equations. Commun. Math. Sci. 1(3), 471–500 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  35. Mizutani, C.M., Nie, Q., Wan, F., Zhang, Y.-T., Vilmos, P., Sousa-Neves, R., Bier, E., Marsh, L., Lander, A.: Formation of the BMP activity gradient in the Drosophila embryo. Dev. Cell 8, 915–924 (2005)

    Article  Google Scholar 

  36. Moler, C., Van Loan, C.: Nineteen dubious ways to compute the exponential of a matrix, twenty-five years later. SIAM Rev. 45, 3–49 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  37. Nie, Q., Zhang, Y.-T., Zhao, R.: Efficient semi-implicit schemes for stiff systems. J. Comput. Phys. 214, 521–537 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  38. Nie, Q., Wan, F., Zhang, Y.-T., Liu, X.-F.: Compact integration factor methods in high spatial dimensions. J. Comput. Phys. 227, 5238–5255 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  39. Planck, M.: Sitzber. Preuss. Akad. Wiss., p. 324 (1917)

  40. Risken, H.: The Fokker–Planck Equation: Methods of Solutions and Applications. Springer, Berlin (1996)

    Book  MATH  Google Scholar 

  41. Shu, C.-W.: TVD time discretizations. SIAM J. Sci. Stat. Comput. 9, 1073–1084 (1988)

    Article  MATH  Google Scholar 

  42. Shu, C.-W., Osher, S.: Efficient implementation of essentially non-oscillatory shock-capturing schemes. J. Comput. Phys. 77, 439–471 (1988)

    Article  MATH  MathSciNet  Google Scholar 

  43. Shu, C.-W.: Essentially non-oscillatory and weighted essentially non-oscillatory schemes for hyperbolic conservation laws. In: Cockburn, B., Johnson, C., Shu, C.-W., Tadmor, E., Quarteroni, A. (eds.) Advanced Numerical Approximation of Nonlinear Hyperbolic Equations. Lecture Notes in Mathematics, vol. 1697. Springer (1998)

  44. Sjoberg, P., Lotstedt, P., Elf, J.: Fokker–Planck approximation of the master equation in molecular biology. Comput. Vis. Sci. 12, 37–50 (2009)

    Article  MathSciNet  Google Scholar 

  45. Strang, G.: On the construction and comparison of difference schemes. SIAM J. Numer. Anal 8(3), 506–517 (1968)

    Article  MATH  MathSciNet  Google Scholar 

  46. Ta, C., Wang, D., Nie, Q.: An integration factor method for stochastic and stiff reaction–diffusion systems. J. Comput. Phys. v295, 505–522 (2015)

    Article  MATH  MathSciNet  Google Scholar 

  47. Trefethen, L.N., Bau, D.: Numerical Linear Algebra. SIAM, Philadelphia (1997)

    Book  MATH  Google Scholar 

  48. Verwer, J.G., Sommeijer, B.P., Hundsdorfer, W.: RKC time-stepping for advection–diffusion–reaction problems. J. Comput. Phys. 201, 61–79 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  49. Wang, D., Zhang, L., Nie, Q.: Array-representation integration factor method for high-dimensional systems. J. Comput. Phys. 258, 585–600 (2014)

    Article  MATH  MathSciNet  Google Scholar 

  50. Wang, D., Chen, W., Nie, Q.: Semi-implicit integration factor methods on sparse grids for high-dimensional systems, J. Comput. Phys. v292, 43–55 (2015)

    Article  MATH  MathSciNet  Google Scholar 

  51. Zhang, Y.-T., Shu, C.-W.: High order WENO schemes for Hamilton–Jacobi equations on triangular meshes. SIAM J. Sci. Comput. 24, 1005–1030 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  52. Zhang, Y.-T., Shu, C.-W.: Third order WENO scheme on three dimensional tetrahedral meshes. Commun. Comput. Phys. 5, 836–848 (2009)

    MATH  MathSciNet  Google Scholar 

  53. Zhong, X.: Additive semi-implicit Runge–Kutta methods for computing high-speed nonequilibrium reactive flows. J. Comput. Phys. 128, 19–31 (1996)

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yong-Tao Zhang.

Additional information

Dedicated to Professor Chi-Wang Shu on the occasion of his 60th birthday.

Research supported by NSF Grant DMS-1620108.

Appendix: Detailed Formulae for AcIIF-WENO Schemes

Appendix: Detailed Formulae for AcIIF-WENO Schemes

(1) For the three dimensional CDR equation, if \({\mathcal {L}}_{12}\), \({\mathcal {L}}_{13}\) and \({\mathcal {L}}_{23}\) commute with each other, then

$$\begin{aligned} {\varvec{\Theta }}_\mathbf{1}= & {} \underset{1\le k_1\le N_1}{\bigotimes }e^{{\mathcal {A}}_{23}\triangle {t_n}}\Bigg (\underset{1\le k_2\le N_2}{\bigotimes }e^{{\mathcal {A}}_{13}\triangle {t_n}}\Bigg (\underset{1\le k_3\le N_3}{\bigotimes }e^{{\mathcal {A}}_{12}\triangle {t_n}}V_1(:,:,k_3)\Bigg )(:,k_2,:)\Bigg )(k_1,:,:),\nonumber \\ {\varvec{\Theta }}_\mathbf{2}= & {} \underset{1\le k_1\le N_1}{\bigotimes }e^{{\mathcal {A}}_{23}(\triangle {t_n}+\triangle {t_{n-1}})}\Bigg (\underset{1\le k_2\le N_2}{\bigotimes }e^{{\mathcal {A}}_{13}(\triangle {t_n}+\triangle {t_{n-1}})}\nonumber \\&\Bigg (\underset{1\le k_3\le N_3}{\bigotimes }e^{{\mathcal {A}}_{12}(\triangle {t_n}+\triangle {t_{n-1}})}V_2(:,:,k_3)\Bigg )(:,k_2,:)\Bigg )(k_1,:,:). \end{aligned}$$
(69)

If \({\mathcal {L}}_{12}\), \({\mathcal {L}}_{13}\) and \({\mathcal {L}}_{23}\) do not commute with each other, then

$$\begin{aligned} \begin{aligned} {\varvec{\Theta }}_\mathbf{1}&=\underset{1\le k_3\le N_3}{\bigotimes }e^{{\mathcal {A}}^{k_3}_{12}\frac{\triangle {t_n}}{2}}\Bigg (\underset{1\le k_2\le N_2}{\bigotimes }e^{{\mathcal {A}}^{k_2}_{13}\frac{\triangle {t_n}}{2}}V_1^*(:,k_2,:)\Bigg )(:,:,k_3),\\ V_1^*&=\underset{1\le k_1\le N_1}{\bigotimes }e^{{\mathcal {A}}^{k_1}_{23}\triangle {t_n}}\Bigg (\underset{1\le k_2\le N_2}{\bigotimes }e^{{\mathcal {A}}^{k_2}_{13}\frac{\triangle {t_n}}{2}}\Bigg (\underset{1\le k_3\le N_3}{\bigotimes }e^{{\mathcal {A}}^{k_3}_{12}\frac{\triangle {t_n}}{2}}V_1(:,:,k_3)\Bigg )(:,k_2,:)\Bigg )(k_1,:,:); \end{aligned} \end{aligned}$$
(70)

and

$$\begin{aligned} \begin{aligned} {\varvec{\Theta }}_\mathbf{2}&=\underset{1\le k_3\le N_3}{\bigotimes }e^{{\mathcal {A}}^{k_3}_{12}\frac{(\triangle {t_n}+\triangle {t_{n-1}})}{2}}\Bigg (\underset{1\le k_2\le N_2}{\bigotimes }e^{{\mathcal {A}}^{k_2}_{13}\frac{(\triangle {t_n}+\triangle {t_{n-1}})}{2}}V_2^*(:,k_2,:)\Bigg )(:,:,k_3),\\ V_2^*&=\underset{1\le k_1\le N_1}{\bigotimes }e^{{\mathcal {A}}^{k_1}_{23}(\triangle {t_n}+\triangle {t_{n-1}})}\Bigg (\underset{1\le k_2\le N_2}{\bigotimes }e^{{\mathcal {A}}^{k_2}_{13}\frac{(\triangle {t_n}+\triangle {t_{n-1}})}{2}}\\&\Bigg (\underset{1\le k_3\le N_3}{\bigotimes }e^{{\mathcal {A}}^{k_3}_{12}\frac{(\triangle {t_n}+\triangle {t_{n-1}})}{2}}V_2(:,:,k_3)\Bigg )(:,k_2,:)\Bigg )(k_1,:,:). \end{aligned} \end{aligned}$$
(71)

(2) For the four dimensional CDR equation, if \({\mathcal {L}}_{12}\), \({\mathcal {L}}_{13}\), \({\mathcal {L}}_{14}\), \({\mathcal {L}}_{23}\), \({\mathcal {L}}_{24}\) and \({\mathcal {L}}_{34}\) commute with each other, then

$$\begin{aligned} \begin{aligned} {\varvec{\Theta }}_\mathbf{1}&= {\mathop {\mathop {\bigotimes }\limits _{1\le k_1\le N_1}} \limits _{1\le k_2 \le N_2}}e^{{\mathcal {A}}_{34}\triangle {t_n}}\Bigg ({\mathop {\mathop {\bigotimes }\limits _{1\le k_1\le N_1}}\limits _{1\le k_3 \le N_3}}e^{{\mathcal {A}}_{24}\triangle {t_n}}\Bigg ({\mathop {\mathop {\bigotimes }\limits _{1\le k_1\le N_1}}\limits _{1\le k_4 \le N_4}}e^{{\mathcal {A}}_{23}\triangle {t_n}}\Bigg ({\mathop {\mathop {\bigotimes }\limits _{1\le k_2\le N_2}}\limits _{1\le k_3 \le N_3}}e^{{\mathcal {A}}_{14}\triangle {t_n}}\Bigg ({\mathop {\mathop {\bigotimes }\limits _{1\le k_2\le N_2}}\limits _{1\le k_4 \le N_4}}e^{{\mathcal {A}}_{13}\triangle {t_n}}\\&\Bigg ({\mathop {\mathop {\bigotimes }\limits _{1\le k_3\le N_3}}\limits _{1\le k_4 \le N_4}}e^{{\mathcal {A}}_{12}\triangle {t_n}}V_1(:,:,k_3,k_4)\Bigg )(:,k_2,:,k_4)\Bigg )(:,k_2,k_3,:)\Bigg )(k_1,:,:,k_4)\Bigg )(k_1,:,k_3,:)\Bigg )\\\&(k_1,k_2,:,:), \end{aligned} \end{aligned}$$
(72)
$$\begin{aligned} \begin{aligned} {\varvec{\Theta }}_\mathbf{2}&={\mathop {\mathop {\bigotimes }\limits _{1\le k_1\le N_1}}\limits _{1\le k_2 \le N_2}}e^{{\mathcal {A}}_{34}(\triangle {t_n}+\triangle {t_{n-1}})}\Bigg ({\mathop {\mathop {\bigotimes }\limits _{1\le k_1\le N_1}} \limits _{1\le k_3 \le N_3}}e^{{\mathcal {A}}_{24}(\triangle {t_n}+\triangle {t_{n-1}})}\\&\Bigg ({\mathop {\mathop {\bigotimes }\limits _{1\le k_1\le N_1}}\limits _{1\le k_4 \le N_4}}e^{{\mathcal {A}}_{23}(\triangle {t_n}+\triangle {t_{n-1}})}\Bigg ({\mathop {\mathop {\bigotimes }\limits _{1\le k_2\le N_2}} \limits _{1\le k_3 \le N_3}}e^{{\mathcal {A}}_{14}(\triangle {t_n}+\triangle {t_{n-1}})}\\&\Bigg ({\mathop {\mathop {\bigotimes }\limits _{1\le k_2\le N_2}}\limits _{1\le k_4 \le N_4}}e^{{\mathcal {A}}_{13}(\triangle {t_n}+\triangle {t_{n-1}})}\Bigg ({\mathop {\mathop {\bigotimes }\limits _{1\le k_3\le N_3}} \limits _{1\le k_4 \le N_4}}e^{{\mathcal {A}}_{12}(\triangle {t_n}+\triangle {t_{n-1}})}V_2(:,:,k_3,k_4)\Bigg )(:,k_2,:,k_4)\Bigg )\\&(:,k_2,k_3,:)\Bigg )(k_1,:,:,k_4)\Bigg )(k_1,:,k_3,:)\Bigg )(k_1,k_2,:,:). \end{aligned} \end{aligned}$$
(73)

If \({\mathcal {L}}_{12}\), \({\mathcal {L}}_{13}\), \({\mathcal {L}}_{14}\), \({\mathcal {L}}_{23}\), \({\mathcal {L}}_{24}\) and \({\mathcal {L}}_{34}\) do not commute with each other, then

$$\begin{aligned} \begin{aligned} {\varvec{\Theta }}_\mathbf{1}&={\mathop {\mathop {\bigotimes }\limits _{1\le k_1\le N_1}} \limits _{1\le k_2 \le N_2}}e^{{\mathcal {A}}^{k_1, k_2}_{34}\frac{\triangle {t_n}}{2}}\Bigg ({\mathop {\mathop {\bigotimes }\limits _{1\le k_1\le N_1}} \limits _{1\le k_3 \le N_3}}e^{{\mathcal {A}}^{k_1, k_3}_{24}\frac{\triangle {t_n}}{2}}\Bigg ({\mathop {\mathop {\bigotimes }\limits _{1\le k_1\le N_1}} \limits _{1\le k_4 \le N_4}}e^{{\mathcal {A}}^{k_1, k_4}_{23}\frac{\triangle {t_n}}{2}}\Bigg ({\mathop {\mathop {\bigotimes }\limits _{1\le k_2\le N_2}} \limits _{1\le k_3 \le N_3}}e^{{\mathcal {A}}^{k_2, k_3}_{14}\frac{\triangle {t_n}}{2}}\\&\Bigg ({\mathop {\mathop {\bigotimes }\limits _{1\le k_2\le N_2}}\limits _{1\le k_4 \le N_4}}e^{{\mathcal {A}}^{k_2, k_4}_{13}\frac{\triangle {t_n}}{2}}V_1^*(:,k_2,:,k_4)\Bigg )(:,k_2,k_3,:)\Bigg )(k_1,:,:,k_4)\Bigg )(k_1,:,k_3,:)\Bigg )(k_1,k_2,:,:), \end{aligned} \end{aligned}$$
(74)
$$\begin{aligned} \begin{aligned} V_1^*&={\mathop {\mathop {\bigotimes }\limits _{1\le k_3\le N_3}}\limits _{1\le k_4 \le N_4}}e^{{\mathcal {A}}^{k_3, k_4}_{12}\triangle {t_n}}\Bigg ({\mathop {\mathop {\bigotimes }\limits _{1\le k_2\le N_2}}\limits _{1\le k_4 \le N_4}}e^{{\mathcal {A}}^{k_2, k_4}_{13}\frac{\triangle {t_n}}{2}}\Bigg ({\mathop {\mathop {\bigotimes }\limits _{1\le k_2\le N_2}} \limits _{1\le k_3 \le N_3}}e^{{\mathcal {A}}^{k_2, k_3}_{14}\frac{\triangle {t_n}}{2}}\Bigg ({\mathop {\mathop {\bigotimes }\limits _{1\le k_1\le N_1}} \limits _{1\le k_4 \le N_4}}e^{{\mathcal {A}}^{k_1, k_4}_{23}\frac{\triangle {t_n}}{2}}\\&\Bigg ({\mathop {\mathop {\bigotimes }\limits _{1\le k_1\le N_1}} \limits _{1\le k_3 \le N_3}}e^{{\mathcal {A}}^{k_1, k_3}_{24}\frac{\triangle {t_n}}{2}}\Bigg ({\mathop {\mathop {\bigotimes }\limits _{1\le k_1\le N_1}}\limits _{1\le k_2 \le N_2}}e^{{\mathcal {A}}^{k_1, k_2}_{34}\frac{\triangle {t_n}}{2}}V_1(k_1,k_2,:,:)\Bigg )\\&(k_1,:,k_3,:)\Bigg )(k_1,:,:,k_4)\Bigg )(:,k_2,k_3,:)\Bigg )(:,k_2,:,k_4)\Bigg )(:,:,k_3,k_4). \end{aligned} \end{aligned}$$
(75)

And

$$\begin{aligned} {\varvec{\Theta }}_\mathbf{2}= & {} {\mathop {\mathop {\bigotimes }\limits _{1\le k_1\le N_1}}\limits _{1\le k_2 \le N_2}}e^{{\mathcal {A}}^{k_1, k_2}_{34}\frac{(\triangle {t_n}+\triangle {t_{n-1}})}{2}}\Bigg ({\mathop {\mathop {\bigotimes }\limits _{1\le k_1\le N_1}}\limits _{1\le k_3 \le N_3}}e^{{\mathcal {A}}^{k_1, k_3}_{24}\frac{(\triangle {t_n}+\triangle {t_{n-1}})}{2}} \Bigg ({\mathop {\mathop {\bigotimes }\limits _{1\le k_1\le N_1}}\limits _{1\le k_4 \le N_4}}e^{{\mathcal {A}}^{k_1, k_4}_{23}\frac{(\triangle {t_n}+\triangle {t_{n-1}})}{2}}\nonumber \\&\Bigg ({\mathop {\mathop {\bigotimes }\limits _{1\le k_2\le N_2}}\limits _{1\le k_3 \le N_3}}e^{{\mathcal {A}}^{k_2, k_3}_{14}\frac{(\triangle {t_n}+\triangle {t_{n-1}})}{2}}\Bigg ( {\mathop {\mathop {\bigotimes }\limits _{1\le k_2\le N_2}}\limits _{1\le k_4 \le N_4}}e^{{\mathcal {A}}^{k_2, k_4}_{13}\frac{(\triangle {t_n}+\triangle {t_{n-1}})}{2}}V_2^*(:,k_2,:,k_4)\Bigg )\\&(:,k_2,k_3,:)\Bigg )(k_1,:,:,k_4)\Bigg )(k_1,:,k_3,:)\Bigg )(k_1,k_2,:,:),\nonumber \end{aligned}$$
(76)
$$\begin{aligned} V_2^*&={\mathop {\mathop {\bigotimes }\limits _{1\le k_3\le N_3}}\limits _{1\le k_4 \le N_4}}e^{{\mathcal {A}}^{k_3, k_4}_{12}(\triangle {t_n}+\triangle {t_{n-1}})}\Bigg ({\mathop {\mathop {\bigotimes }\limits _{1\le k_2\le N_2}}\limits _{1\le k_4 \le N_4}}e^{{\mathcal {A}}^{k_2, k_4}_{13}\frac{(\triangle {t_n}+\triangle {t_{n-1}})}{2}}\Bigg ({\mathop {\mathop {\bigotimes }\limits _{1\le k_2\le N_2}}\limits _{1\le k_3 \le N_3}}e^{{\mathcal {A}}^{k_2, k_3}_{14}\frac{(\triangle {t_n}+\triangle {t_{n-1}})}{2}}\nonumber \\&\Bigg ({\mathop {\mathop {\bigotimes }\limits _{1\le k_1\le N_1}}\limits _{1\le k_4 \le N_4}}e^{{\mathcal {A}}^{k_1, k_4}_{23}\frac{(\triangle {t_n}+\triangle {t_{n-1}})}{2}}\Bigg ( {\mathop {\mathop {\bigotimes }\limits _{1\le k_1\le N_1}}\limits _{1\le k_3 \le N_3}}e^{{\mathcal {A}}^{k_1, k_3}_{24}\frac{(\triangle {t_n}+\triangle {t_{n-1}})}{2}}\Bigg ({\mathop {\mathop {\bigotimes }\limits _{1\le k_1\le N_1}}\limits _{1\le k_2 \le N_2}}e^{{\mathcal {A}}^{k_1, k_2}_{34}\frac{(\triangle {t_n}+\triangle {t_{n-1}})}{2}}\nonumber \\&V_2(k_1,k_2,:,:) \Bigg )(k_1,:,k_3,:)\Bigg )(k_1,:,:,k_4)\Bigg )(:,k_2,k_3,:)\Bigg )(:,k_2,:,k_4)\Bigg )(:,:,k_3,k_4). \end{aligned}$$
(77)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lu, D., Zhang, YT. Computational Complexity Study on Krylov Integration Factor WENO Method for High Spatial Dimension Convection–Diffusion Problems. J Sci Comput 73, 980–1027 (2017). https://doi.org/10.1007/s10915-017-0398-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10915-017-0398-7

Keywords

Navigation