arXiv · 2611.06012
Geometric Multigrid Preconditioners for conforming Finite Element Methods on Arbitrarily Agglomerated Meshes
Abstract
Geometric multigrid methods for conforming finite element discretisations classically require a nested mesh hierarchy, forcing coarse meshes to be conforming, which is unsuitable for general domains. In order to eliminate this geometric constraint, we introduce and analyse a family of multilevel auxiliary space preconditioners. At the first level, a conforming Lagrangian discretisation on a fine simplicial mesh is preconditioned by a non-conforming polytopal discretisation posed on an arbitrarily agglomerated coarse mesh. At the subsequent levels, the coarse problem is solved by a geometric multigrid method for that polytopal discretisation on further agglomerated meshes, so that the whole hierarchy is generated from the fine mesh by agglomeration alone. We consider two polytopal discretisations, a symmetric interior penalty discontinuous Galerkin method and a hybrid high-order method. The prolongation operator at the first level is an element-wise interpolation onto the fine mesh followed by nodal averaging, preceded in the hybrid high-order case by a local potential reconstruction. For point-Jacobi and vertex-star patch additive Schwarz smoothers, we prove that the condition number of the preconditioned system is bounded by $O((H/h)^3)$, where $H/h$ is the coarsening ratio, and by $O((H/h)^2)$ for the DG variant with penalty proportional to $H/h$. Numerical experiments in two and three dimensions, on structured and unstructured meshes with agglomerates generated by automatic clustering algorithms, confirm the robustness of the preconditioners.
Explore related subjects
Keep this discovery
Explore connections, maps & timelines
Santiago Badia, Jordi Manyer. 2026-11-04. Geometric Multigrid Preconditioners for conforming Finite Element Methods on Arbitrarily Agglomerated Meshes. https://arxiv.org/abs/2611.06012
Cite the original work for its findings. Save a collection to share your selection of sources.