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Parallel Algorithms for Finite Automata Problems

  • B. Ravikumar
Part of the Combinatorial Optimization book series (COOP, volume 5)

Abstract

Finite automata are among the most extensively studied and understood models of computation. They have wide ranging applications — for example, in image compression, protocol validation, game theory and computational biology — just to mention only some recent ones. Here we will survey efficient parallel algorithms for many fundamental computational problems on finite automata. It is well known that problems involving deterministic finite automata (DFA) have polynomial time algorithms, but the problems become hard when the input automata are nondeterministic (NFA or regular expressions). A similar difference is observed in the case of parallel algorithms: most problems involving DFA as input have NC algorithms, while NC algorithms are unlikely with NFA (or regular expression) as input. In addition to DFA and NFA, we will also consider other inputs such as unambiguous finite automata, regular expressions and prefix grammars.

The problems surveyed here include the following: (1) The classical decion problems — membership, disjointness, inclusion and equivalence problems. (2) Counting (the number of strings of a given length), ranking a string, lexicographic successor of a given string, lexicographically first string of a given length, etc. all with respect to a DFA (3) Coarsest partition problems for functions and relations. (4) Finding sequences for automata identification or testing (such as homing sequence, synchronizing sequence etc.) (5) Conversions between different representations (such as regular expression to NFA or -free NFA) and (6) Problems arising from applications such as data compression, string editing etc.

NC algorithms for these problems (when they exist) use transitive closure computation and hence are not practical. In some cases, efficient algorithms (on average) exist that avoid transitive closure computations. We also discuss issues involved in practical implementations of these algorithms and present results on the performance of some algorithms in practical experiments. We conclude with a list of open problems.

Keywords

Parallel Algorithm Regular Expression Total Work Finite Automaton Input Symbol 
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

© Kluwer Academic Publishers 1999

Authors and Affiliations

  • B. Ravikumar
    • 1
  1. 1.Department of Computer ScienceUniversity of Rhode IslandKingstonUSA

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