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Convergence Analysis of Schwarz Waveform Relaxation for Nonlocal Diffusion Problems
Author(s) -
Ke Li,
Dali Guo,
Yunxiang Zhao
Publication year - 2021
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2021/5574403
Subject(s) - discretization , mathematics , rate of convergence , convergence (economics) , quadrature (astronomy) , relaxation (psychology) , partial differential equation , waveform , convolution (computer science) , numerical analysis , schwarz alternating method , mathematical analysis , computer science , finite element method , domain decomposition methods , physics , psychology , computer network , channel (broadcasting) , social psychology , radar , telecommunications , machine learning , artificial neural network , economics , thermodynamics , economic growth , optics
Diffusion equations with Riemann–Liouville fractional derivatives are Volterra integro-partial differential equations with weakly singular kernels and present fundamental challenges for numerical computation. In this paper, we make a convergence analysis of the Schwarz waveform relaxation (SWR) algorithms with Robin transmission conditions (TCs) for these problems. We focus on deriving good choice of the parameter involved in the Robin TCs, at the continuous and fully discretized level. Particularly, at the space-time continuous level, we show that the derived Robin parameter is much better than the one predicted by the well-understood equioscillation principle. At the fully discretized level, the problem of determining a good Robin parameter is studied in the convolution quadrature framework, which permits us to precisely capture the effects of different temporal discretization methods on the convergence rate of the SWR algorithms. The results obtained in this paper will be preliminary preparations for our further study of the SWR algorithms for integro-partial differential equations.

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