Automated Grammar-based Algebraic Multigrid Design With Evolutionary Algorithms

• URL: http://arxiv.org/abs/2603.17641v1
• Date: Wed, 18 Mar 2026 12:02:26 GMT
• Title: Automated Grammar-based Algebraic Multigrid Design With Evolutionary Algorithms
• Abstract summary: We show how to generate efficient multigrid methods with level-specific smoothing sequences and non-recursive cycling patterns.<n>Experiments with the linear algebra library, emphhypre, demonstrate the potential of these non-standard GP cycles to improve multigrid performance as a solver and a preconditioner.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Although multigrid is asymptotically optimal for solving many important partial differential equations, its efficiency relies heavily on the careful selection of the individual algorithmic components. In contrast to recent approaches that can optimize certain multigrid components using deep learning techniques, we adopt a complementary strategy, employing evolutionary algorithms to construct efficient multigrid cycles from proven algorithmic building blocks. Here, we will present its application to generate efficient algebraic multigrid methods with so-called \emph{flexible cycling}, that is, level-specific smoothing sequences and non-recursive cycling patterns. The search space with such non-standard cycles is intractable to navigate manually, and is generated using genetic programming (GP) guided by context-free grammars. Numerical experiments with the linear algebra library, \emph{hypre}, demonstrate the potential of these non-standard GP cycles to improve multigrid performance both as a solver and a preconditioner.

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