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Better Hyper-minimization

Not as Fast, But Fewer Errors
  • Andreas Maletti
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6482)

Abstract

Hyper-minimization aims to compute a minimal deterministic finite automaton (dfa) that recognizes the same language as a given dfa up to a finite number of errors. Algorithms for hyper-minimization that run in time O(n logn), where n is the number of states of the given dfa, have been reported recently in [Gawrychowski and Jeż: Hyper-minimisation made efficient. Proc. Mfcs, Lncs 5734, 2009] and [Holzer and Maletti: An n logn algorithm for hyper-minimizing a (minimized) deterministic automaton. Theor. Comput. Sci. 411, 2010]. These algorithms are improved to return a hyper-minimal dfa that commits the least number of errors. This closes another open problem of [Badr, Geffert, and Shipman: Hyper-minimizing minimized deterministic finite state automata. Rairo Theor. Inf. Appl. 43, 2009]. Unfortunately, the time complexity for the obtained algorithm increases to O(n 2).

Keywords

Recursive Call Lossy Compression Error Count State Automaton Access Path 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Andreas Maletti
    • 1
  1. 1.Departament de Filologies RomàniquesUniversitat Rovira i VirgiliTarragonaSpain

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