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Convergence analysis of a minimax method for finding multiple solutions of hemivariational inequality in Hilbert space

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Abstract

In 2013, a minimax method for finding saddle points of locally Lipschitz continuous functional was designed (Yao Math. Comp. 82 2087–2136 2013). The method can be applied to numerically solve hemivariational inequality for multiple solutions. Its subsequence and sequence convergence results in functional analysis were established in the same paper. But, since these convergence results do not consider discretization, they are not convergence results in numerical analysis. In this paper, we point out what approximation problem is, when this minimax method is used to solve hemivariational inequality and the finite element method is used in discretization. Computation of the approximation problem is discussed, numerical experiment is carried out and its global convergence is verified. Finally, as element size goes to zero, convergence of solutions of the approximation problem to solutions of hemivariational inequality is proved.

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Correspondence to Xudong Yao.

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Communicated by: Goong Chen

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Yao, X. Convergence analysis of a minimax method for finding multiple solutions of hemivariational inequality in Hilbert space. Adv Comput Math 42, 1331–1362 (2016). https://doi.org/10.1007/s10444-016-9465-0

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  • DOI: https://doi.org/10.1007/s10444-016-9465-0

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