Neural networks predict the weights of momentum-accelerated long-step Richardson iterations, reducing iteration counts on anisotropic diffusion and Helmholtz problems versus Chebyshev-based iterations in numerical experiments.
Nesterov, Introductory lectures on convex optimization: A basic course, Vol
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Momentum-Accelerated Richardson(m) and Their Multilevel Neural Solvers
Neural networks predict the weights of momentum-accelerated long-step Richardson iterations, reducing iteration counts on anisotropic diffusion and Helmholtz problems versus Chebyshev-based iterations in numerical experiments.