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An economic implementation of the optimal rotated block-diagonal preconditioning method

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Abstract

The numerical discretization of the optimal control problems constrained with certain kind of time-dependent fractional diffusion equations leads to a class of highly structured block two-by-two linear systems. We present a different and economic implementation of the approximated rotated block diagonal (ARBD) preconditioner, denoted briefly as the ARBDe preconditioner, for solving this class of linear systems effectively by making use of the correspondingly preconditioned Krylov subspace iteration methods such as the ARBDe-preconditioned flexible GMRES (FGMRES) method, or the ARBDe-FGMRES method. Compared with the ARBD-GMRES method constructed and analyzed by Bai and Lu in 2021 (Appl. Numer. Math. 163:126–146), the ARBDe-FGMRES method requires a lower computational complexity and can achieve much higher computational efficiency in practical applications. With numerical experiments, we have examined and confirmed the robustness, accuracy, and effectiveness of the ARBDe-FGMRES method in solving this class of discrete optimal control problems.

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References

  1. Antil, H., Otárola, E.: A FEM for an optimal control problem of fractional powers of elliptic operators. SIAM J. Control Optim. 53, 3432–3456 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  2. Bai, Z.-Z.: Sharp error bounds of some Krylov subspace methods for non-Hermitian linear systems. Appl. Math. Comput. 109, 273–285 (2000)

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  4. Bai, Z.-Z., Lu, K.-Y.: Optimal rotated block-diagonal preconditioning for discretized optimal control problems constrained with fractional time-dependent diffusive equations. Appl. Numer. Math. 163, 126–146 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  5. Bai, Z.-Z., Benzi, M., Chen, F., Wang, Z.-Q.: Preconditioned MHSS iteration methods for a class of block two-by-two linear systems with applications to distributed control problems. IMA J. Numer. Anal. 33, 343–369 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  6. Bai, Z.-Z., Pan, J.-Y.: Matrix Analysis and Computations. SIAM, Philadelphia, PA (2021)

    Book  MATH  Google Scholar 

  7. Benson, D.A., Wheatcraft, S.W., Meerschaert, M.M.: Application of a fractional advection-dispersion equation. Water Resour. Res. 36, 1403–1412 (2000)

    Article  Google Scholar 

  8. Braess, D.: Finite Elements. Cambridge University Press, Cambridge (1997)

    Book  MATH  Google Scholar 

  9. Chan, R.H., **, X.-Q.: An Introduction to Iterative Toeplitz Solvers. SIAM, Philadelphia, PA (2007)

    Book  MATH  Google Scholar 

  10. Du, N., Wang, H., Liu, W.-B.: A fast gradient projection method for a constrained fractional optimal control. J. Sci. Comput. 68, 1–20 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  11. Elman, H.C., Silvester, D.J., Wathen, A.J.: Finite Elements and Fast Iterative Solvers: With Applications in Incompressible Fluid Dynamics. Oxford University Press, New York (2005)

    MATH  Google Scholar 

  12. 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 

  13. Li, C.-P., Cai, M.: Theory and Numerical Approximations of Fractional Integrals and Derivatives. SIAM, Philadelphia, PA (2019)

    Book  Google Scholar 

  14. Mathieu, B., Melchior, P., Oustaloup, A., Ceyral, Ch.: Fractional differentiation for edge detection. Signal Process. 83, 2421–2432 (2003)

    Article  MATH  Google Scholar 

  15. Podlubny, I.: Fractional Differential Equations, Math. Sci. Engrg 198. Academic Press, San Diego (1999)

    Google Scholar 

  16. 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  MathSciNet  MATH  Google Scholar 

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

    Book  MATH  Google Scholar 

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

    MATH  Google Scholar 

  19. van der Vorst, H.A.: Iterative Krylov Methods for Large Linear Systems. Cambridge University Press, Cambridge (2003)

    Book  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

The authors are very much indebted to the referees for their constructive suggestions and insightful comments, which greatly improved the original manuscript of this paper.

Funding

This work is supported by The National Natural Science Foundation of China (Nos. 12071472, 12001048, and 11671393), and The Science and Technology Planning Projects of Bei**g Municipal Education Commission (No. KM202011232019), P.R. China.

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Correspondence to Zhong-Zhi Bai.

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Bai, ZZ., Lu, KY. An economic implementation of the optimal rotated block-diagonal preconditioning method. Numer Algor 93, 85–101 (2023). https://doi.org/10.1007/s11075-022-01404-w

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