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arXiv · 1104.3007

Optimal Hyper-Minimization

Andreas Maletti·Daniel Quernheim
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Abstract

Minimal deterministic finite automata (DFAs) can be reduced further at the expense of a finite number of errors. Recently, such minimization algorithms have been improved to run in time O(n log n), where n is the number of states of the input DFA, by [Gawrychowski and Jeż: Hyper-minimisation made efficient. Proc. MFCS, LNCS 5734, 2009] and [Holzer and Maletti: An n log n algorithm for hyper-minimizing a (minimized) deterministic automaton. Theor. Comput. Sci. 411, 2010]. Both algorithms return a DFA that is as small as possible, while only committing a finite number of errors. These algorithms are further improved to return a DFA that commits the least number of errors at the expense of an increased (quadratic) run-time. This solves an open problem of [Badr, Geffert, and Shipman: Hyper-minimizing minimized deterministic finite state automata. RAIRO Theor. Inf. Appl. 43, 2009]. In addition, an experimental study on random automata is performed and the effects of the existing algorithms and the new algorithm are reported.

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Andreas Maletti, Daniel Quernheim. 2011-04-15. Optimal Hyper-Minimization. https://doi.org/10.1007/978-3-642-18098-9_22

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