Automated Grammar-based Algebraic Multigrid Design With Evolutionary Algorithms
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866908897453801472 |
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| author | Parthasarathy, Dinesh Mitchell, Wayne Gambhir, Arjun Köstler, Harald Rüde, Ulrich |
| author_facet | Parthasarathy, Dinesh Mitchell, Wayne Gambhir, Arjun Köstler, Harald Rüde, Ulrich |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_17641 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Automated Grammar-based Algebraic Multigrid Design With Evolutionary Algorithms Parthasarathy, Dinesh Mitchell, Wayne Gambhir, Arjun Köstler, Harald Rüde, Ulrich Computational Engineering, Finance, and Science Artificial Intelligence Numerical Analysis 65M55, 68W50, 35-04 G.1.8; I.2.2; F.4.3 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. |
| title | Automated Grammar-based Algebraic Multigrid Design With Evolutionary Algorithms |
| topic | Computational Engineering, Finance, and Science Artificial Intelligence Numerical Analysis 65M55, 68W50, 35-04 G.1.8; I.2.2; F.4.3 |
| url | https://arxiv.org/abs/2603.17641 |