Journal article

Multigrid for two-sided fractional differential equations discretized by finite volume elements on graded meshes

  • Donatelli, Marco ORCID Department of Science and High Technology, Insubria University, Como, Italy
  • Krause, Rolf ORCID Facoltà di scienze informatiche, Università della Svizzera italiana, Svizzera
  • Mazza, Mariarosa ORCID Department of Mathematics, University of Rome Tor Vergata, Italy
  • Trotti, Ken ORCID Department of Science and High Technology, Insubria University, Como, Italy - Facoltà di scienze informatiche, Università della Svizzera italiana, Svizzera
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  • 2024
Published in:
  • Journal of computational and applied mathematics. - 2024, vol. 444, no. 115787
English It is known that the solution of a conservative steady-state two-sided fractional diffusion problem can exhibit singularities near the boundaries. As a consequence of this, and due to the conservative nature of the problem, we adopt a finite volume elements discretization approach over a generic non-uniform mesh. We focus on grids mapped by a smooth function which consists in a combination of a graded mesh near the singularity and a uniform mesh where the solution is smooth. Such a choice gives rise to Toeplitz-like discretization matrices and thus allows a low computational cost of the matrix–vector product and detailed spectral analysis. The obtained spectral information is used to develop an ad-hoc parameter-free multigrid preconditioner for GMRES, which is numerically shown to yield good convergence results in presence of graded meshes mapped by power functions that accumulate points near the singularity. The approximation order of the considered graded meshes is numerically compared with the one of a certain composite mesh given in literature that still leads to Toeplitz-like linear systems and is then still well-suited for our multigrid method. Several numerical tests confirm that power-graded meshes result in lower approximation errors than composite ones and that our solver has a wide range of applicability.
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Language
  • English
Classification
Mathematics
License
CC BY
Open access status
hybrid
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Persistent URL
https://n2t.net/ark:/12658/srd1331132
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