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CSE 311 Foundations of Computing I

CSE 311 Foundations of Computing I. Lecture 23 NFAs, Regular Expressions, and Equivalence with DFAs Spring 2013. Announcements. Reading assignments 7 th Edition, Sections 13.3 and 13.4 6 th Edition, Section 12.3 and 12.4. 0. 2. S0 [1]. S1 [0]. 0. 1. 2. 3. 3. 0. 1. 1. 0.

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CSE 311 Foundations of Computing I

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  1. CSE 311 Foundations of Computing I Lecture 23 NFAs, Regular Expressions, and Equivalence with DFAs Spring 2013

  2. Announcements • Reading assignments • 7thEdition, Sections 13.3 and 13.4 • 6th Edition, Section 12.3 and 12.4

  3. 0 2 S0[1] S1 [0] 0 1 2 3 3 0 1 1 0 S2[1] S3 [0] 1 2 2 3 3 1 2 2 3 S4[1] S5 [0] 0 0 1 3 Last lecture highlights Finite State Machines with output at states State minimization 0 2 S0[1] S1 [0] 0 1 2 3 0 1,3 3 1,2 0 S2[1] S3 [0] 1 2 3 ⇒

  4. Last lecture highlights Lemma: The language recognized by a DFA is the set of strings x that label some path from its start state to one of its final states 0 0 1 1 1 s0 s1 s2 s3 0 0,1

  5. Nondeterministic Finite Automaton (NFA) • Graph with start state, final states, edges labeled by symbols (like DFA) but • Not required to have exactly 1 edge out of each state labeled by each symbol - can have 0 or >1 • Also can have edges labeled by empty string  • Definition: The language recognized by an NFA is the set of strings x that label some path from its start state to one of its final states 1 1 1 s0 s1 s2 s3 0,1 0,1

  6. Three ways of thinking about NFAs • Outside observer: Is there a path labeled by x from the start state to some final state? • Perfect guesser: The NFA has input x and whenever there is a choice of what to do it magically guesses a good one (if one exists) • Parallel exploration: The NFA computation runs all possible computations on x step-by-step at the same time in parallel

  7. Design an NFA with 4 states to recognize the set of binary strings whose 3rd from last character is a 1

  8. Design an NFA to recognize the set of binary strings that contain 111 or have an even # of 1’s

  9. NFAs and Regular Expressions Theorem: For any set of strings (language) Adescribed by a regular expression, there is an NFA that recognizes A. Proof idea: Structural induction based on the recursive definition of regular expressions... Note: One can also find a regular expression to describe the language recognized by any NFA but we won’t prove that fact

  10. Regular expressions over  • Basis: • ,  are regular expressions • a is a regular expression for any a • Recursive step: • If A and B are regular expressions then so are: • (A  B) • (AB) • A*

  11. Basis • Case : • Case : • Case a:

  12. Basis • Case : • Case : • Case a: a

  13. Inductive Hypothesis • Suppose that for some regular expressionsA and B there exist NFAs NA and NB such that NA recognizes the language given by A and NB recognizes the language given by B NA NB

  14. Inductive Step • Case (A  B): NA NB

  15. Inductive Step • Case (A  B): λ NA λ NB

  16. Inductive Step • Case(AB): NB NA

  17. Inductive Step • Case(AB): λ λ NB NA

  18. Inductive Step • Case A* NA

  19. Inductive Step • Case A* λ λ NA λ

  20. Build a NFA for (01 1)*0

  21. Solution (01 1)*0 0  1     0     1

  22. NFAs and DFAs Every DFA is an NFA • DFAs have requirements that NFAs don’t have Can NFAs recognize more languages? No! Theorem: For every NFA there is a DFA that recognizes exactly the same language

  23. Conversion of NFAs to a DFAs • Proof Idea: • The DFA keeps track of ALL the states that the part of the input string read so far can reach in the NFA • There will be one state in the DFA for each subset of states of the NFA that can be reached by some string

  24. Conversion of NFAs to a DFAs • New start state for DFA • The set of all states reachable from the start state of the NFA using only edges labeled  f   a,b,e,f a b  e DFA NFA

  25. Conversion of NFAs to a DFAs • For each state of the DFA corresponding to a set S of states of the NFA and each symbol s • Add an edge labeled s to state corresponding to T, the set of states of the NFA reached by • starting from some state in S, then • following one edge labeled by s, and • then following some number of edges labeled by  • T will be  if no edges from S labeled s exist f d 1   g 1 b c 1  b,e,f c,d,e,g e 1 1

  26. Conversion of NFAs to a DFAs • Final states for the DFA • All states whose set contain some final state of the NFA c e a,b,c,e a b DFA NFA

  27. Example: NFA to DFA a  1 0 b c 0 0,1 NFA DFA

  28. Example: NFA to DFA a,b a  1 0 b c 0 0,1 NFA DFA

  29. Example: NFA to DFA 0 a,b a 1  1 c 0 b c 0 0,1 NFA DFA

  30. Example: NFA to DFA 0 a,b a 1  1 1 b c 0 b c 0 0 0,1 b,c NFA DFA

  31. Example: NFA to DFA 0  a,b a 1 0 1  1 1 b c 0 b c 0 0 0,1 b,c NFA DFA

  32. Example: NFA to DFA 0,1 0  a,b a 1 0 1  1 1 b c 0 b c 0 0 0,1 b,c NFA DFA

  33. Example: NFA to DFA 0,1 0  a,b a 1 0 1  1 1 b c 0 1 b c 0 0 0,1 b,c a,b,c 0 NFA DFA

  34. Example: NFA to DFA 0,1 0  a,b a 1 0 1  1 1 b c 0 1 b c 0 0 0 0,1 1 b,c a,b,c 0 NFA DFA

  35. Exponential blow-up in simulating nondeterminism • In general the DFA might need a state for every subset of states of the NFA • Power set of the set of states of the NFA • n-state NFA yields DFA with at most 2n states • We saw an example where roughly 2n is necessary • Is the nth char from the end a 1? • The famous “P=NP?” question asks whether a similar blow-up is always necessary to get rid of nondeterminism for polynomial-time algorithms

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