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arXiv · 2606.26678

Selective Memoization for Efficient Backtracking Regular Expression Matching

Martin Berglund·Brink van der Merwe·Iain le Roux
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Abstract

Backtracking regular expression matchers are widely used due to their expressive power but may exhibit exponential worst-case matching time. Memoization provides a principled method for eliminating redundant computation and ensuring linear matching time, but full memoization is memory-intensive and impractical. We introduce the Minimum Feedback Node (MFN) memoization scheme, a selective memoization strategy based on computing a minimum feedback vertex set of an automaton. We establish relationships with existing memoization schemes and analyze their behaviour under both Thompson and Glushkov automaton constructions.

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Martin Berglund, Brink van der Merwe, Iain le Roux. 2026-06-25. Selective Memoization for Efficient Backtracking Regular Expression Matching. https://doi.org/10.4204/eptcs.446.1

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