State complexity

State complexity is an area of theoretical computer science dealing with the size of abstract automata, such as different kinds of finite automata. The classical result in the area is that simulating an -state nondeterministic finite automaton by a deterministic finite automaton requires exactly states in the worst case.

Transformation between variants of finite automata

Finite automata can be deterministic and nondeterministic, one-way (DFA, NFA) and two-way (2DFA, 2NFA). Other related classes are unambiguous (UFA), self-verifying (SVFA) and alternating (AFA) finite automata. These automata can also be two-way (2UFA, 2SVFA, 2AFA).

All these machines can accept exactly the regular languages. However, the size of different types of automata necessary to accept the same language (measured in the number of their states) may be different. For any two types of finite automata, the state complexity tradeoff between them is an integer function where is the least number of states in automata of the second type sufficient to recognize every language recognized by an -state automaton of the first type. The following results are known.

  • UFA to DFA: states, see Leung,[3] An earlier lower bound by Schmidt[4] was smaller.
  • NFA to UFA: states, see Leung.[3] There was an earlier smaller lower bound by Schmidt.[4]
  • SVFA to DFA: states, see Jirásková and Pighizzini[5]
  • 2DFA to DFA: states, see Kapoutsis.[6] Earlier construction by Shepherdson[7] used more states, and an earlier lower bound by Moore[8] was smaller.
  • 2DFA to NFA: , see Kapoutsis.[6] Earlier construction by Birget [9] used more states.
  • 2NFA to NFA: , see Kapoutsis.[6]
    • 2NFA to NFA accepting the complement: states, see Vardi.[10]
  • AFA to DFA: states, see Chandra, Kozen and Stockmeyer.[11]
  • AFA to NFA: states, see Fellah, Jürgensen and Yu.[12]
  • 2AFA to DFA: , see Ladner, Lipton and Stockmeyer.[13]
  • 2AFA to NFA: , see Geffert and Okhotin.[14]

The 2DFA vs. 2NFA problem and logarithmic space

Unsolved problem in computer science:
Does every -state 2NFA have an equivalent -state 2DFA?
(more unsolved problems in computer science)

It is an open problem whether all 2NFAs can be converted to 2DFAs with polynomially many states, i.e. whether there is a polynomial such that for every -state 2NFA there exists a -state 2DFA. The problem was raised by Sakoda and Sipser,[15] who compared it to the P vs. NP problem in the computational complexity theory. Berman and Lingas[16] discovered a formal relation between this problem and the L vs. NL open problem. This relation was further elaborated by Kapoutsis.[17]

State complexity of operations for finite automata

Given a binary regularity-preserving operation on languages and a family of automata X (DFA, NFA, etc.), the state complexity of is an integer function such that

  • for each m-state X-automaton A and n-state X-automaton B there is an -state X-automaton for , and
  • for all integers m, n there is an m-state X-automaton A and an n-state X-automaton B such that every X-automaton for must have at least states.

Analogous definition applies for operations with any number of arguments.

The first results on state complexity of operations for DFAs were published by Maslov [18] and by Yu, Zhuang and Salomaa. [19] Holzer and Kutrib [20] pioneered the state complexity of operations on NFA. The known results for basic operations are listed below.

Union

If language requires m states and language requires n states, how many states requires?

  • DFA: states, see Maslov[18] and Yu, Zhuang and Salomaa.[19]
  • NFA: states, see Holzer and Kutrib.[20]
  • UFA: between and states, see Jirásek, Jirásková and Šebej.[21]
  • SVFA: states, see Jirásek, Jirásková and Szabari.[22]
  • 2DFA: between and states, see Kunc and Okhotin.[23]
  • 2NFA: states, see Kunc and Okhotin.[24]

Intersection

How many states requires?

  • DFA: states, see Maslov[18] and Yu, Zhuang and Salomaa.[19]
  • NFA: states, see Holzer and Kutrib.[20]
  • UFA: states, see Jirásek, Jirásková and Šebej.[21]
  • SVFA: states, see Jirásek, Jirásková and Szabari.[22]
  • 2DFA: between and states, see Kunc and Okhotin.[23]
  • 2NFA: between and states, see Kunc and Okhotin.[24]

Complementation

If language L requires n states then how many states its complement requires?

  • DFA: states, by exchanging accepting and rejecting states.
  • NFA: states, see Birget.[25]
  • UFA: at least and at most states, see Okhotin[26] for the lower bound and Jirásek, Jirásková and Šebej[21] for the upper bound. Raskin[27] recently proved a superpolynomial lower bound.
  • SVFA: states, by exchanging accepting and rejecting states.
  • 2DFA: at least and at most states, see Geffert, Mereghetti and Pighizzini.[28]

Concatenation

How many states requires?

  • DFA: states, see Maslov [18] and Yu, Zhuang and Salomaa.[19]
  • NFA: states, see Holzer and Kutrib.[20]
  • UFA: states, see Jirásek, Jirásková and Šebej.[21]
  • SVFA: states, see Jirásek, Jirásková and Szabari.[22]
  • 2DFA: at least and at most states, see Jirásková and Okhotin.[29]

Kleene star

  • DFA: states, see Maslov[18] and Yu, Zhuang and Salomaa.[19]
  • NFA: states, see Holzer and Kutrib.[20]
  • UFA: states, see Jirásek, Jirásková and Šebej.[21]
  • SVFA: states, see Jirásek, Jirásková and Szabari.[22]
  • 2DFA: at least and at most states, see Jirásková and Okhotin.[29]

Reversal

  • DFA: states, see Mirkin,[30] Leiss,[31] and Yu, Zhuang and Salomaa.[19]
  • NFA: states, see Holzer and Kutrib.[20]
  • UFA: states.
  • SVFA: states, see Jirásek, Jirásková and Szabari.[22]
  • 2DFA: between and states, see Jirásková and Okhotin.[29]

Finite automata over a unary alphabet

State complexity of finite automata with a one-letter (unary) alphabet, pioneered by Chrobak,[32] is different from the multi-letter case.

Let be Landau's function.

Transformation between models

For a one-letter alphabet, transformations between different types of finite automata are sometimes more efficient than in the general case.

  • NFA to DFA: states, see Chrobak.[32]
  • 2DFA to DFA: states, see Chrobak[32] and Kunc and Okhotin.[33]
  • 2NFA to DFA: states, see Mereghetti and Pighizzini.[34] and Geffert, Mereghetti and Pighizzini.[35]
  • NFA to 2DFA: at most states, see Chrobak.[32]
  • 2NFA to 2DFA: at most states, proved by implementing the method of Savitch's theorem, see Geffert, Mereghetti and Pighizzini.[35]
  • UFA to DFA: , see Okhotin.[26]
  • NFA to UFA: , see Okhotin.[26]

Union

  • DFA: states, see Yu, Zhuang and Salomaa.[19]
  • NFA: states, see Holzer and Kutrib.[20]
  • 2DFA: between and states, see Kunc and Okhotin.[23]
  • 2NFA: states, see Kunc and Okhotin.[24]

Intersection

  • DFA: states, see Yu, Zhuang and Salomaa.[19]
  • NFA: states, see Holzer and Kutrib.[20]
  • 2DFA: between and states, see Kunc and Okhotin.[23]
  • 2NFA: between and states, see Kunc and Okhotin.[24]

Complementation

  • DFA: states.
  • NFA: states, Holzer and Kutrib.[20]
  • UFA: at least and at most states, see Okhotin.[26]
  • 2DFA: at least and at most states, see Kunc and Okhotin.[23]
  • 2NFA: at least and at most states. The upper bound is by implementing the method of the Immerman–Szelepcsényi theorem, see Geffert, Mereghetti and Pighizzini.[28]

Concatenation

  • DFA: states, see Yu, Zhuang and Salomaa.[19]
  • NFA: between and states, see Holzer and Kutrib.[20]
  • 2DFA: states, see Kunc and Okhotin.[23]
  • 2NFA: states, see Kunc and Okhotin.[23]

Kleene star

  • DFA: states, see Yu, Zhuang and Salomaa.[19]
  • NFA: states, see Holzer and Kutrib.[20]
  • UFA: states, see Okhotin.[26]
  • 2DFA: states, see Kunc and Okhotin.[23]
  • 2NFA: states, see Kunc and Okhotin.[23]

Further reading

Surveys of state complexity were written by Holzer and Kutrib[36][37] and by Gao et al.[38]

New research on state complexity is commonly presented at the annual workshops on Descriptional Complexity of Formal Systems (DCFS), at the Conference on Implementation and Application of Automata (CIAA), and at various conferences on theoretical computer science in general.

References

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  2. Lupanov, Oleg B. (1963). "A comparison of two types of finite sources". Problemy Kibernetiki. 9: 321–326.
  3. Leung, Hing (2005). "Descriptional complexity of NFA of different ambiguity". International Journal of Foundations of Computer Science. 16 (5): 975–984. doi:10.1142/S0129054105003418. ISSN 0129-0541.
  4. Schmidt, Erik M. (1978). Succinctness of Description of Context-Free, Regular and Unambiguous Languages (Ph.D.). Cornell University.
  5. Jirásková, Galina; Pighizzini, Giovanni (2011). "Optimal simulation of self-verifying automata by deterministic automata". Information and Computation. 209 (3): 528–535. doi:10.1016/j.ic.2010.11.017. ISSN 0890-5401.
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  8. Moore, F.R. (1971). "On the Bounds for State-Set Size in the Proofs of Equivalence Between Deterministic, Nondeterministic, and Two-Way Finite Automata". IEEE Transactions on Computers. C-20 (10): 1211–1214. doi:10.1109/T-C.1971.223108. ISSN 0018-9340.
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  10. Vardi, Moshe Y. (1989). "A note on the reduction of two-way automata to one-way automata". Information Processing Letters. 30 (5): 261–264. CiteSeerX 10.1.1.60.464. doi:10.1016/0020-0190(89)90205-6. ISSN 0020-0190.
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  14. Geffert, Viliam; Okhotin, Alexander (2014). Transforming Two-Way Alternating Finite Automata to One-Way Nondeterministic Automata. Lecture Notes in Computer Science. 8634. pp. 291–302. doi:10.1007/978-3-662-44522-8_25. ISBN 978-3-662-44521-1. ISSN 0302-9743.
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  16. Berman, Piotr; Lingas, Andrzej (1977). On the complexity of regular languages in terms of finite automata. Report 304. Polish Academy of Sciences.
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