Gaussian process regression trained on small linear systems predicts near-optimal AMG strong-threshold parameters that match grid-search iteration counts at reduced cost for several PDEs.
FP-AMG: FPGA-based acceleration frame- work for algebraic multigrid solvers
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Kernel-learning parameter prediction and evaluation in algebraic multigrid method for several PDEs
Gaussian process regression trained on small linear systems predicts near-optimal AMG strong-threshold parameters that match grid-search iteration counts at reduced cost for several PDEs.