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Linear multi-step methods and their numerical stability for solving gradient flow equations

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Abstract

In this paper, linear multi-step methods are used to numerically solve gradient flow models, and the relations between different numerical stabilities (e.g., unconditional energy stability, A-stability and G-stability) of linear multi-step methods are discussed. First, we introduce the definition of the absolutely unconditional energy stability (AUES), in which the meaning of absoluteness is borrowed from the absolute stability of numerical methods, and we rigorously prove that the AUES is equivalent to the A-/G-stability of the scheme. Then, we obtain a new gradient flow system by using scalar auxiliary variable (SAV) approach with Lagrange multiplier. The linear multi-step method and the Fourier pseudo-spectral method are respectively used to discretize the temporal and spatial variables of the new gradient flow system, and its AUES is guaranteed by simple checking its A-/G-stability of the numerical scheme. Especially, we show that these schemes not only include the commonly-used backward Euler, Crank–Nicolson, second-order backward differentiation formula (BDF2) schemes, but also include some generalized Adams and Nyström schemes. Finally, we apply these numerical schemes for solving gradient flow models, and ample numerical results are provided to demonstrate the high performance of the proposed schemes.

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Acknowledgements

The numerical calculations in this paper have been done on the supercomputing system in the Supercomputing Center of Wuhan University

Funding

This work was partially supported by the National Natural Science Foundation of China (Nos. 12001210, 12131010, 12271414, 12125103, 12261103), by the National Key Research and Development Program of China (No. 2020YFA0714201), and the Yunnan Fundamental Research Projects (No. 202301AT070117)

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Correspondence to Wei Jiang.

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Huang, QA., Jiang, W., Yang, J.Z. et al. Linear multi-step methods and their numerical stability for solving gradient flow equations. Adv Comput Math 49, 39 (2023). https://doi.org/10.1007/s10444-023-10043-1

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