1. CS 302: Discrete Math II A Review

2. An alphabet Σ is a finite set (e.g., Σ = {0,1}) A string over Σ is a finite-length sequence of elements of Σ For x a string, |x| isthe length of x The unique string of length 0 will be denoted by ε and will be called the empty or null string NOTATION A language over Σ is a set of strings over Σ

3. Q = {q 0, q 1, q 2, q 3 } are states Σ = {0,1} is the alphabet  : Q  Σ → Q transition function * q 0  Q is start state F = {q 1, q 2 }  Q accept states M = (Q, Σ, , q 0, F) where  01 q0q0 q0q0 q1q1 q1q1 q2q2 q2q2 q2q2 q3q3 q2q2 q3q3 q0q0 q2q2 * q2q2 0 0,1 0 0 1 1 1 q0q0 q1q1 q3q3 M NFAs have “choices” --  : Q  Σ  P (Q), and accept on input w if there is a path from q 0 to a q a in F.

4. DFA NFA Regular Language Regular Expression DEF

5. THE REGULAR PUMPING LEMMA Let L be a regular language Then there exists P such that For every w  L with |w| ≥ P 1. |y| > 0 there exist xyz=w, where: 3. xy i z  L for any i ≥ 0 2. |xy| ≤ P { ww : w 2 Σ* } is not regular!

6. SOME LANGUAGES ARE NOT REGULAR! B = {0 n 1 n | n ≥ 0} is NOT regular! Suppose B is regular and let P be the pumping length. The string 0 P 1 P  B and has length at least P. So by the pumping lemma, there should be xyz = 0 P 1 P so that |xy| ≤ P, |y| > 0, and xyyz  B. But y must be only 0s (otherwise |xy| > P), so xyyz has at least P+1 “0”s (because |y| > 0) but only P “1”s. So xyyz  B, contradicting the pumping lemma. Therefore B is not regular.

7. ε,ε → $ 0,ε → 0 1,0 → ε ε,$ → ε stringpoppush A PDA accepts the string x if there is a path on input x and empty stack from q 0 to some q a 2 F. Following transition “a,b  c” reads “a” from the input, pops “b” off the stack, and pushes “c” onto it.

8. A → 0A1 A → B B → # CONTEXT-FREE GRAMMARS A  0A1  00A11  00B11  00#11 A derives 00#11 in 4 steps. uVw yields uvw if (V → v) in G. variables terminals production rules start variable

9. THE CHOMSKY NORMAL FORM A context-free grammar is in Chomsky normal form if every rule is of the form: A → BC A → a S → ε B and C are not start variable a is a terminal S is the start variable Any variable A that is not the start variable can only generate strings of length > 0 Theorem: All grammars can be converted to CNF

10. THE CONTEXT-FREE PUMPING LEMMA Let L be a context-free language Then there exists P such that For every w  L with |w| ≥ P 1. |vy| > 0 there exist uvxyz=w, where: 3. uv i xy i z  L for any i ≥ 0 2. |vxy| ≤ P { ww : w in Σ* } is not context-free!

11. 0 → 0, R readwritemove  → , R q accept q reject 0 → 0, R  → , R 0 → 0, R  → , L TURING MACHINES UNBOUNDED TAPE 0 q0q0 q0q0 q1q1 q2q2 q1q1 qaqa 0

12. Definition: A Turing Machine is a 7-tuple T = (Q, Σ, Γ, , q 0, q accept, q reject ), where: Q is a finite set of states; q 0 is the start state Σ is the input alphabet, Γ is the tape alphabet  : Q  Γ → Q  Γ  {L,R} is the transition function q accept  q reject are the accept and reject states We can encode a TM as a string of 0s and 1s:

13. Terminology Every TM recognizes a language. The language of M is the set L(M) = { w | M(w) eventually accepts } A TM decides L if it accepts all strings in L and rejects all strings not in L A language is decidable if some TM decides it A language is recursively enumerable if some TM recognizes it. THE CHURCH-TURING THESIS: There is a program for L iff there is a TM for L

14. UNDECIDABILITY A language is a set of strings. It is a mathematical way of expressing a problem: given an input, is it in the set L? If a language is decidable, there is a computer program (TM) that can always solve the problem correctly – it terminates and has the right answer. If a language is undecidable, then no matter how smart you are, and no matter how long you give it, you cannot program a computer to always solve the problem correctly.

15. Let S be any set and P(S) be the power set of S Theorem: There is no onto map from S to P(S) Proof: Assume, for a contradiction, that there is an onto map f : S  P(S) Let D f = { d  S | d  f(d) } If D f = f(y) then y  D f if and only if y  D f Turing Machines Strings of 0s and 1s Sets of strings of 0s and 1s Languages over {0,1} S P(S)

16. UNDECIDABLE PROBLEMS D TM = { (M) : M is a TM that does not accept (M) } Theorem. D TM is undecidable. Proof. Suppose machine N decides D TM. Then N accepts →  D TM → N does not accept A TM = { (M,w) : M is a TM that accepts on input w } Theorem. If A TM is decidable, so is D TM. Proof. If ¬A TM is decided by the program nAccept we can decide if  D TM by calling nAccept(M,M). Here we have reduced deciding D TM to deciding A TM. Since we know D TM is undecidable, A TM must also be undecidable.

17. MAPPING REDUCTIONS f : Σ*  Σ* is a computable function if there is a TM that on input w, halts with f(w) on its tape A  m B if there is a computable f, such that w  A  f(w)  B f is called a reduction from A to B

18. RICE’S THEOREM Let P be a set of Turing machines. If P satisfies the following two properties: For any TMs M 1 and M 2, where L(M 1 ) = L(M 2 ), M 1  P if and only if M 2  P There exist TMs M IN  P and M OUT  P Then P is undecidable EXTREMELY POWERFUL (i.e. P is a “nontrivial property of the r.e. languages.”)

19. CONTEXT-FREE LANGUAGES 100 200 300 REGULAR LANGUAGES 100 200 300 NON-REGULAR NON-CFL 100 200 300 JEOPARDY

20. A DFA for the language (ab + ba)*

21. A regular expression for the set of strings accepted by the NFA: 0,1 1

22. A proof that the language { w  Σ*: w ≠ w R } is not regular.

23. The language accepted by the following PDA: ε,ε → $ 0,ε → 0 1,0 → ε ε, $ → ε

24. A CFG for the language { w # b n : |w| = n }.

25. Regular operations that the Context-Free Languages are closed under. DAILY DOUBLE

26. A string in the language L = { 1 n #1 n : n ≥ 0} that contradicts the regular pumping lemma.

27. A string in the language {a n #a n #a n : n ≥ 0 } that contradicts the context-free pumping lemma.

28. A proof that the language { a n #b m : m = n 2, n ≥ 0 } is not context-free.

29. Do there exist non-regular languages that satisfy the Regular Pumping Lemma?

30. UNDECIDABLE LANGUAGES 100 200 300 DECIDABLE LANGUAGES 100 200 300 NP-COMPLETE REDUCTIONS 100 200 300 DOUBLE JEOPARDY

31. Show that if L 1 and L 2 are decidable, then so is L 1 – L 2.

32. A proof that the set {0,1,2}* is countable.

33. A proof that ODD ≤ m EVEN, where Σ = {0,1} ODD = { 1 i : i is odd} EVEN = { 1 i : i is even}

34. A proof, using Rice’s Theorem, that P 3 = { (M) : M accepts all strings of length 3 } is undecidable.

35. What operations are recursively enumerable languages closed under?

36. A proof that A TM ≤ m A JAVA, where A JAVA = { (J,w) : J is a Java program with method “public boolean test(string w)” that returns true on string w. }

37. What’s the definition of NP? NP-Complete? NP-hard?

38. Is Addition an NP-Complete problem? Why or why not?

39. Show that 4-SAT is NP-Complete by reducing it to 3-SAT.