Multilevel Spectral Domain Decomposition

Abstract

Highly heterogeneous, anisotropic coefficients, e.g. in the simulation of carbon-fibre composite components, can lead to extremely challenging finite element systems. Direct solvers for the resulting large and sparse linear systems suffer from severe memory requirements and limited parallel scalability, while iterative solvers in general lack robustness. Two-level spectral domain decomposition methods can provide such robustness for symmetric positive definite linear systems, by using coarse spaces based on independent generalized eigenproblems in the subdomains. Rigorous condition number bounds are independent of mesh size, number of subdomains, as well as coefficient contrast. However, their parallel scalability is still limited by the fact that (in order to guarantee robustness) the coarse problem is solved via a direct method. In this paper, we introduce a multilevel variant in the context of subspace correction methods and provide a general convergence theory for its robust convergence for abstract, elliptic variational problems. Assumptions of the theory are verified for conforming, as well as for discontinuous Galerkin methods applied to a scalar diffusion problem. Numerical results illustrate the performance of the method for two-and three-dimensional problems and for various discretization schemes, in the context of scalar diffusion and linear elasticity.

Spectral Coarse Spaces for the Substructured Parallel Schwarz Method

“…The literature about two-level DD methods is very rich. See, e.g., [7,[12][13][14][15][16][17][18][19], for references considering two-level Schwarz stationary methods, and, e.g., [20][21][22][23][24][25][26][27][28][29][30][31][32], for references considering two-level Schwarz preconditioners, and, e.g., for references considering two-level substructuring preconditioners [31,[33][34][35]. See also general classical references as [4][5][6] and [36,37].…”