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Compressed Structures for Partial Derivative Automata Constructions

    https://doi.org/10.1142/S0129054124420061Cited by:0 (Source: Crossref)

    The partial derivative automaton (𝒜PD) is an elegant simulation of a regular expression. Although it is, in general, smaller than the position automaton (𝒜POS), the algorithms that build 𝒜PD in quadratic worst-case time first compute 𝒜POS. Asymptotically, and on average for the uniform distribution, the size of 𝒜PD is half the size of 𝒜POS, being both linear on the size of the expression. We address the construction of 𝒜PD efficiently, on average, avoiding the computation of 𝒜POS. The expression and the set of its partial derivatives are represented by a directed acyclic graph with shared common subexpressions. We develop an algorithm for building 𝒜PDs from expressions in strong star normal form of size n that runs, on average, in time that asymptotically behaves as n3/2log(n)4, and space as n3/2/(logn)3/4. Empirical results corroborate its good practical performance.

    Research supported by NSERC (Canada) and by CMUP through FCT project UIDB/00144/2021.

    Communicated by Sang-Ki Ko

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