Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/168983
Title: An efficient spectral trust-region deflation method for multiple solutions
Authors: Li, Lin
Wang, Li-Lian
Li, Huiyuan
Keywords: Science::Mathematics
Issue Date: 2023
Source: Li, L., Wang, L. & Li, H. (2023). An efficient spectral trust-region deflation method for multiple solutions. Journal of Scientific Computing, 95(1). https://dx.doi.org/10.1007/s10915-023-02154-0
Project: RG15/21
Journal: Journal of Scientific Computing
Abstract: It is quite common that a nonlinear partial differential equation (PDE) admits multiple distinct solutions and each solution may carry a unique physical meaning. One typical approach for finding multiple solutions is to use the Newton method with different initial guesses that ideally fall into the basins of attraction confining the solutions. In this paper, we propose a fast and accurate numerical method for multiple solutions comprised of three ingredients: (i) a well-designed spectral-Galerkin discretization of the underlying PDE leading to a nonlinear algebraic system (NLAS) with multiple solutions; (ii) an effective deflation technique to eliminate a known (founded) solution from the other unknown solutions leading to deflated NLAS; and (iii) a viable nonlinear least-squares and trust-region (LSTR) method for solving the NLAS and the deflated NLAS to find the multiple solutions sequentially one by one. We demonstrate through ample examples of differential equations and comparison with relevant existing approaches that the spectral LSTR-Deflation method has the merits: (i) it is quite flexible in choosing initial values, even starting from the same initial guess for finding all multiple solutions; (ii) it guarantees high-order accuracy; and (iii) it is quite fast to locate multiple distinct solutions and explore new solutions which are not reported in literature.
URI: https://hdl.handle.net/10356/168983
ISSN: 0885-7474
DOI: 10.1007/s10915-023-02154-0
Schools: School of Physical and Mathematical Sciences 
Rights: © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SPMS Journal Articles

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