An algebraic multiscale preconditioner for large sparse SPD matrices
For computational scientists solving large-scale elliptic PDEs with high heterogeneity, this method offers a robust, parallelizable preconditioner without geometric information.
The paper presents a two-grid algebraic multiscale preconditioner for large sparse SPD systems from elliptic problems with highly heterogeneous coefficients, achieving robustness to coefficient contrast and problem size, outperforming standard algebraic multigrid on challenging large-scale cases, and demonstrating good parallel scalability.
We present a two-grid algebraic multiscale preconditioner for large sparse symmetric positive definite systems arising from elliptic problems with highly heterogeneous coefficients. The coarse space is constructed directly from the system matrix by graph partitioning and local generalized eigenvalue solvers, yielding basis functions that capture the low-energy modes responsible for slow convergence. The method requires no geometric information, making it suitable for unstructured and matrix-only settings, and its construction is naturally parallelizable. Numerical results for heterogeneous Darcy flow problems show robustness with respect to coefficient contrast and problem size, better performance than standard algebraic multigrid on challenging large-scale cases, and good parallel scalability.