PEARL: Preconditioner Enhancement through Actor-critic Reinforcement
Learning
PEARL: Preconditioner Enhancement through Actor-critic Reinforcement
Learning
We present PEARL (Preconditioner Enhancement through Actor-critic Reinforcement Learning), a novel approach to learning matrix preconditioners. Existing preconditioners such as Jacobi, Incomplete LU, and Algebraic Multigrid methods offer problem-specific advantages but rely heavily on hyperparameter tuning. Recent advances have explored using deep neural networks to learn preconditioners, though challenges such …