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Numer. Math. Theor. Meth. Appl., 18 (2025), pp. 226-258.
Published online: 2025-04
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In this paper, a quadratic spline collocation (QSC) method combined with $L1$ time discretization in the framework of alternating direction implicit (ADI) approach, namely ADI-QSC-$L$1 method, is developed to solve the variable-order time-fractional mobile-immobile diffusion equations in multi-dimensional spaces. Discrete $L_2$ norm-based stability and error estimate are carefully discussed, which show that the proposed method is unconditionally stable and convergent with first-order accuracy in time and second-order accuracy in space. Then, based on the exponential-sum-approximation technique for the fast evaluation of the variable-order Caputo fractional derivative, an efficient implementation strategy of the ADI-QSC-$L1$ method, named ADI-QSC-${\rm F}L1$ is presented, which further improves the computational efficiency by reduced memory requirement and computational cost. Finally, numerical examples are provided to support both the theoretical results and efficiency of the developed method.
}, issn = {2079-7338}, doi = {https://doi.org/10.4208/nmtma.OA-2024-0086}, url = {http://global-sci.org/intro/article_detail/nmtma/23948.html} }In this paper, a quadratic spline collocation (QSC) method combined with $L1$ time discretization in the framework of alternating direction implicit (ADI) approach, namely ADI-QSC-$L$1 method, is developed to solve the variable-order time-fractional mobile-immobile diffusion equations in multi-dimensional spaces. Discrete $L_2$ norm-based stability and error estimate are carefully discussed, which show that the proposed method is unconditionally stable and convergent with first-order accuracy in time and second-order accuracy in space. Then, based on the exponential-sum-approximation technique for the fast evaluation of the variable-order Caputo fractional derivative, an efficient implementation strategy of the ADI-QSC-$L1$ method, named ADI-QSC-${\rm F}L1$ is presented, which further improves the computational efficiency by reduced memory requirement and computational cost. Finally, numerical examples are provided to support both the theoretical results and efficiency of the developed method.