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Online Autotuning of Algebraic Multigrid via Contextual Bandits and Reinforcement Learning
Algebraic multigrid (AMG) methods are widely used for solving large sparse linear systems, but their efficiency depends strongly on interacting setup and solve parameters. Effective parameter choices typically depend on the problem. We develop a phase-aware online autotuning method for hypre's Boome
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