Develops two-level convergence theory for LS-AMG-DD showing coarse-space weak approximation property bounded by spectral cutoff threshold, yielding factored bounds for multiplicative cycles with block-Jacobi and overlapping Schwarz smoothers on Gram-representable SPD matrices.
Computing56(3), 179–196 (1996)
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
RAPNet uses a GNN with level-wise training to learn sparse robust coarse operators that accelerate algebraic multigrid on large PDE and graph problems.
Matching-based AMG preconditioners deliver robust and scalable performance for solving large ill-conditioned systems from IgA discretizations in parallel HPC settings.
citing papers explorer
-
Two-level convergence of Algebraic Multigrid with Overlapping Smoothers and Spectral Coarse Grids
Develops two-level convergence theory for LS-AMG-DD showing coarse-space weak approximation property bounded by spectral cutoff threshold, yielding factored bounds for multiplicative cycles with block-Jacobi and overlapping Schwarz smoothers on Gram-representable SPD matrices.
-
Parallel matching-based AMG preconditioners for elliptic equations discretized by IgA
Matching-based AMG preconditioners deliver robust and scalable performance for solving large ill-conditioned systems from IgA discretizations in parallel HPC settings.