A \tau Matrix Based Approximate Inverse Preconditioning for Tempered Fractional Diffusion Equations
<|reference_start|>A \tau Matrix Based Approximate Inverse Preconditioning for Tempered Fractional Diffusion Equations: Tempered fractional diffusion equations are a crucial class of equations widely applied in many physical fields. In this paper, the Crank-Nicolson method and the tempered weighted and shifts Gr\"unwald formula are firstly applied to discretize the tempered fractional diffusion equations. We then obtain that the coefficient matrix of the discretized system has the structure of the sum of the identity matrix and a diagonal matrix multiplied by a symmetric positive definite(SPD) Toeplitz matrix. Based on the properties of SPD Toeplitz matrices, we use $\tau$ matrix approximate it and then propose a novel approximate inverse preconditioner to approximate the coefficient matrix. The $\tau$ matrix based approximate inverse preconditioner can be efficiently computed using the discrete sine transform(DST). In spectral analysis, the eigenvalues of the preconditioned coefficient matrix are clustered around 1, ensuring fast convergence of Krylov subspace methods with the new preconditioner. Finally, numerical experiments demonstrate the effectiveness of the proposed preconditioner.<|reference_end|>
@article{zhang2024a,
    title={A {\tau} Matrix Based Approximate Inverse Preconditioning for Tempered
  Fractional Diffusion Equations},
    author={Xuan Zhang, Chaojie Wang, Haiyu Liu},
    journal={arXiv preprint arXiv:2407.21603},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.21603},
    primaryClass={math.NA cs.NA}
}