AlgMortar: a fully algebraic multiscale mortar preconditioner
Abstract
The solution of large-scale symmetric positive definite (SPD) linear systems arising from discretizations of second-order elliptic equations is challenging, especially in applications with highly heterogeneous coefficients, such as flow in porous media, which can lead to severely ill-conditioned systems. In this context, multiscale methods have recently been used to accelerate Krylov subspace methods, owing to their favorable parallel scalability. In this work, we present AlgMortar, a fully algebraic realization of the Multiscale Mortar Mixed Finite Element Method (MMMFEM). The method uses only information extracted from the fine-grid system matrix, which facilitates its implementation in existing solvers. AlgMortar uses graph partitioning to define a domain decomposition directly from the matrix graph. On each subdomain, it builds local linear systems that mimic Dirichlet problems, and couples the resulting local solutions through an algebraic interface condition that recovers the weak flux-continuity mechanism of MMMFEM. We prove that the method is well posed when the matrix is SPD. Numerical experiments on fine-grid linear systems arising from finite-volume discretizations of Darcy flow problems show that, when used as a preconditioner for the conjugate gradient method, the proposed approach exhibits good scalability and is competitive with state-of-the-art algebraic multigrid (AMG) methods for challenging heterogeneous, high-contrast test cases, including highly irregular corner-point grids. Additional tests with SPD matrices arising from problems other than finite-volume discretizations of Darcy flow showed competitive performance with AMG.
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BibTeX
@misc{santos2026algmortar,
title = {{AlgMortar: a fully algebraic multiscale mortar preconditioner}},
author = {Luan F. Santos and Fabricio S. Sousa and Roberto F. Ausas and Rafael T. Guiraldello and Felipe Pereira},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2607.22905},
eprint = {2607.22905},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2607.22905},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - AlgMortar: a fully algebraic multiscale mortar preconditioner
AU - Luan F. Santos
AU - Fabricio S. Sousa
AU - Roberto F. Ausas
AU - Rafael T. Guiraldello
AU - Felipe Pereira
PY - 2026
DA - 2026/10/09
PB - arXiv
DO - 10.48550/arxiv.2607.22905
UR - https://arxiv.org/abs/2607.22905
AB - The solution of large-scale symmetric positive definite (SPD) linear systems arising from discretizations of second-order elliptic equations is challenging, especially in applications with highly heterogeneous coefficients, such as flow in porous media, which can lead to severely ill-conditioned systems. In this context, multiscale methods have recently been used to accelerate Krylov subspace methods, owing to their favorable parallel scalability. In this work, we present AlgMortar, a fully algebraic realization of the Multiscale Mortar Mixed Finite Element Method (MMMFEM). The method uses only information extracted from the fine-grid system matrix, which facilitates its implementation in existing solvers. AlgMortar uses graph partitioning to define a domain decomposition directly from the matrix graph. On each subdomain, it builds local linear systems that mimic Dirichlet problems, and couples the resulting local solutions through an algebraic interface condition that recovers the weak flux-continuity mechanism of MMMFEM. We prove that the method is well posed when the matrix is SPD. Numerical experiments on fine-grid linear systems arising from finite-volume discretizations of Darcy flow problems show that, when used as a preconditioner for the conjugate gradient method, the proposed approach exhibits good scalability and is competitive with state-of-the-art algebraic multigrid (AMG) methods for challenging heterogeneous, high-contrast test cases, including highly irregular corner-point grids. Additional tests with SPD matrices arising from problems other than finite-volume discretizations of Darcy flow showed competitive performance with AMG.
ER -