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Communications in Mathematical Sciences
Volume 19 (2021)
Number 1
Normalized Goldstein-type local minimax method for finding multiple unstable solutions of semilinear elliptic PDEs
Pages: 147 – 174
DOI: https://dx.doi.org/10.4310/CMS.2021.v19.n1.a6
Authors
Abstract
In this paper, we propose a normalized Goldstein-type local minimax method (NG-LMM) to seek for multiple minimax-type solutions. Inspired by the classical Goldstein line search rule in the optimization theory in $\mathbb{R}^m$, which is aimed to guarantee the global convergence of some descent algorithms, we introduce a normalized Goldstein-type search rule and combine it with the local minimax method to be suitable for finding multiple unstable solutions of semilinear elliptic PDEs both in numerical implementation and theoretical analysis. Compared with the normalized Armijo-type local minimax method (NA-LMM), which was first introduced in [Y. Li and J. Zhou, SIAM J. Sci. Comput., 24(3):865–885, 2002] and then modified in [Z.Q. Xie, Y.J. Yuan, and J. Zhou, SIAM J. Sci. Comput., 34(1):A395–A420, 2012], our approach can prevent the step-size from being too small automatically and then ensure that the iterations make reasonable progress by taking full advantage of two inequalities. The feasibility of the NG-LMM is verified strictly. Further, the global convergence of the NG-LMM is proven rigorously under a weak assumption that the peak selection is only continuous. Finally, it is implemented to solve several typical semilinear elliptic boundary value problems on square or dumbbell domains for multiple unstable solutions and the numerical results indicate that this approach performs well.
Keywords
semilinear elliptic PDEs, global convergence, multiple solutions, local minimax method, normalized Goldstein-type search rule
2010 Mathematics Subject Classification
35B38, 35J20, 65K15, 65N12
This work was supported by NSFC (91430107, 11771138, 11171104) and the Construct Program of the Key Discipline in Hunan. Yi’s work was also partially supported by the Fundamental Research Funds for the Central Universities 531118010207 and the NSFC Grant 11901185.
Received 16 December 2019
Accepted 12 August 2020
Published 24 March 2021