Double fast algorithm for solving time-space fractional diffusion problems with spectral fractional Laplacian

TL;DR

This paper introduces a fast, efficient algorithm for high-dimensional time-space fractional diffusion problems with spectral fractional Laplacian, reducing computational cost and memory usage through innovative discretization and transform techniques.

Contribution

The paper develops a novel double fast algorithm combining spectral methods and discrete sine transforms for efficient solution of fractional diffusion equations.

Findings

Exact evaluation of fractional matrix powers using discrete sine transform.

Achieves optimal temporal convergence rate of O(N^{-(2-α)}) with graded mesh.

Significant reduction in computation time and memory requirements.

Abstract

This paper presents an efficient and concise double fast algorithm to solve high dimensional time-space fractional diffusion problems with spectral fractional Laplacian. We first establish semi-discrete scheme of time-space fractional diffusion equation, which uses linear finite element or fourth-order compact difference method combining with matrix transfer technique to approximate spectral fractional Laplacian. Then we introduce a fast time-stepping L1 scheme for time discretization. The proposed scheme can exactly evaluate fractional power of matrix and perform matrix-vector multiplication at per time level by using discrete sine transform, which doesn't need to resort to any iteration method and can significantly reduce computation cost and memory requirement. Further, we address stability and convergence analyses of full discrete scheme based on fast time-stepping L1 scheme on graded time mesh. Our analysis shows that the choice of graded mesh factor $\omega=(2-\alpha)/\alpha$ shall give an optimal temporal convergence $\mathcal{O}(N^{-(2-\alpha)})$ with $N$ denoting the number of time mesh. Finally, ample numerical examples are delivered to illustrate our theoretical analysis and the efficiency of the suggested scheme.