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On the Average State Complexity of Partial Derivative Transducers

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 12011)

Abstract

2D regular expressions represent rational relations over two alphabets. In this paper we study the average state complexity of partial derivative standard transducers (\(\mathcal {T}_{\text {PD}}\)) that can be defined for (general) 2D expressions where basic terms are pairs of ordinary regular expressions (1D). While in the worst case the number of states of \(\mathcal {T}_{\text {PD}}\) can be \(O(n^2)\), where n is the size of the expression, asymptotically and on average that value is bounded from above by \(O(n^{\frac{3}{2}})\). Moreover, asymptotically and on average the alphabetic size of a 2D expression is half of the size of that expression. All results are obtained in the framework of analytic combinatorics considering generating functions of parametrised combinatorial classes defined implicitly by algebraic curves. In particular, we generalise the methods developed in previous work to a broad class of analytic functions.

This work was partially supported by NSERC, Canada and CMUP (UID/MAT/00144/2019), which is funded by FCT, FEDER, and PT2020.

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Fig. 1.
Fig. 2.

Notes

  1. 1.

    Extending partial derivatives w.r.t. words, one could also define \({\mathsf {PD}}(r)=\bigcup _{w\in \varSigma ^\star }\partial _w(\mathbf{r} )\).

  2. 2.

    This follows from the definition above.

  3. 3.

    I.e. \([z^n]G_k(z)\) gives the number of expressions \(\mathbf{g} \) of size n.

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Konstantinidis, S., Machiavelo, A., Moreira, N., Reis, R. (2020). On the Average State Complexity of Partial Derivative Transducers. In: , et al. SOFSEM 2020: Theory and Practice of Computer Science. SOFSEM 2020. Lecture Notes in Computer Science(), vol 12011. Springer, Cham. https://doi.org/10.1007/978-3-030-38919-2_15

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  • DOI: https://doi.org/10.1007/978-3-030-38919-2_15

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