Expregular functions
Thomas Colcombet, Nathan Lhote, Pierre Ohlmann

TL;DR
This paper introduces expregular functions, a new class of string-to-string functions with exponential growth, and proves their equivalence across three models, extending the theory of polyregular functions.
Contribution
It defines expregular functions, establishes their equivalence across MSO set interpretations, yield-Hennie machines, and Ariadne transducers, and proves their regularity reflecting property.
Findings
MSO set interpretations are regularity reflecting.
Ariadne transducers recognize regular languages.
The three models are proven equivalent.
Abstract
Polyregular functions form a robust class of string-to-string functions with polynomial growth, as evidenced by Bojanczyk (2018). This class admits numerous descriptions and enjoys several closure properties. Most notably, polyregular functions are regularity reflecting (\ie the inverse image of a regular language is regular). In this work, we propose a robust class of string-to-string functions with exponential growth which we call expregular functions. We consider the following three models for describing them: - MSO set interpretations, which extend MSO interpretations (one of the models capturing polyregular functions), by operating on monadic variables instead of tuples of first-order variables; - yield-Hennie machines, which are branching one-tape Turing machines with bounded visit; and - Ariadne transducers, a new model of 2-way pushdown machines with a bounded visit…
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