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arXiv · 2410.23681

Convergent analysis of algebraic multigrid method with data-driven parameter learning for non-selfadjoint elliptic problems

Juan Zhang·Junyue Luo
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Abstract

In this paper, we apply the practical GADI-HS iteration as a smoother in algebraic multigrid (AMG) method for solving second-order non-selfadjoint elliptic problem. Additionally, we prove the convergence of the derived algorithm and introduce a data-driven parameter learing method called Gaussian process regression (GPR) to predict optimal parameters. Numerical experimental results show that using GPR to predict parameters can save a significant amount of time cost and approach the optimal parameters accurately.

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Juan Zhang, Junyue Luo. 2024-10-31. Convergent analysis of algebraic multigrid method with data-driven parameter learning for non-selfadjoint elliptic problems. https://arxiv.org/abs/2410.23681

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