期刊
SIAM JOURNAL ON SCIENTIFIC COMPUTING
卷 41, 期 5, 页码 S242-S268出版社
SIAM PUBLICATIONS
DOI: 10.1137/18M1193761
关键词
multigrid; nonsymmetric; hyperbolic; algebraic; AMG; reduction
资金
- U.S. Department of Defense, Air Force Office of Scientific Research, National Defense Science and Engineering Graduate (NDSEG) Fellowship [32 CFR 168a]
- U.S. Department of Energy [DE-FC02-03ER25574, DE-NA0002376]
- Lawrence Livermore National Laboratory [B614452]
Algebraic multigrid (AMG) is often an effective solver for symmetric positive definite (SPD) linear systems resulting from the discretization of general elliptic PDEs or the spatial discretization of parabolic PDEs. However, convergence theory and most variations of AMG rely on A being SPD. Hyperbolic PDEs, which arise often in large-scale scientific simulations, remain a challenge for AMG, as well as other fast linear solvers, in part because the resulting linear systems are often highly nonsymmetric. Here, a novel convergence framework is developed for nonsymmetric, reduction-based AMG, and sufficient conditions derived for l(2)-convergence of error and residual. In particular, classical multigrid approximation properties are connected with reduction-based measures to develop a robust framework for nonsymmetric, reduction-based AMG. Matrices with block-triangular structure are then recognized as being amenable to reduction-type algorithms, and a reduction-based AMG method is developed for upwind discretizations of hyperbolic PDEs, based on the concept of a Neumann approximation to ideal restriction (nAIR). nAIR can be seen as a variation of local AIR (lAIR) introduced in previous work, specifically targeting matrices with triangular structure. Although less versatile than lAIR, setup times for nAIR can be substantially faster for problems with high connectivity. nAIR is shown to be an effective and scalable solver of steady state transport for discontinuous, upwind discretizations, with unstructured meshes, and up to 6th-order finite elements, offering a significant improvement over existing AMG methods. nAIR is also shown to be effective on several classes of nearly triangular matrices resulting from curvilinear finite elements and artificial diffusion.
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