CP405 Theory of Computation

Transcription

1 CP405 Theory of Computation

2 BB(3) q 0 q 1 q 2 0 q 1 1R q 2 0R q 2 1L 1 H1R q 1 1R q 0 1L

3 Growing Fast BB(3) = 6 BB(4) = 13 BB(5) = 4098 BB(6) = x (known) (known) (possible) (possible)

4 Language: a set of strings Computation as a test if a string is in a language DFA: limited memory automaton Regular Language: a language accepted by a DFA Regular Operations (closed): A B, AB, A *, A, A B NFA: non-deterministic finite automaton DFA == NFA == regex (computational power) GNFA: NFA with regex transitions Prove lang. is non-regular with pumping lemma POSIX extended regexes are not formally regular (no corr. DFA) CFG: context-free grammar generates CFL Some languages are CF and not regular CFL are used for programming languages All regular languages are CF Every CFL can be converted to Chomsky Normal Form (CNF) - Rules are two vars, one terminal, or start->epsilon CNF string expansions require 2n-1 steps L-Systems: formal grammars for modeling morphogenesis PDA == CFG (computational power) Non-deterministic PDA > Deterministic PDA (comp. power) Context-sensitive grammar > Context-free grammar (comp power) Prove lang. is not context-free with pumping lemma TM > PDA (comp. power) TM can accept, reject, or loop forever TM match intuitive definition of algorithm Java, Python, etc. are == TM (comp power) Linear Bounded Automata (LBA) == TM with finite tape LBA == context-sensitive grammars (comp power) Post Tag System chops and concatenates strings (== TM) Queue Machine like PDA with queue in place of stack (== TM) PDA with two stacks == TM Counter Machines (pebbles in holes) with two registers (== TM) Random Access Machines provide indirect addressing (== TM) TMs that always halt correctly are called deciders. If TM M is a decider, then L(M) is a decidable language A DFA, A NFA, A CFG, A LBA are decidable A TM is undecidable (the acceptance problem) HALT is undecidable (the halting problem) Rice's Theorem: any non-trivial lang. property is undecidable TMs that accept correctly (may loop) are called recognizers Enumerators == recognizers that print If a TM M is a recognizer, then L(M) is an enumerable lang A TM, HALT are enumerable A TM, HALT are not enumerable EQ TM, EQ TM, are both not enumerable countable number of enumerable languages uncountable number of langs that are not enumerable Post Correspondence Problem (dominos) is undecidable Never play dominos with Matthew A Quine is a program that print out itself TMs can obtain their own descriptions Busy beaver function grows faster than any computable func.

5 Computability Theory Automata Theory Complexity Theory

6 Complexity Theory Algorithmic Complexity Reductions P and NP NP-Completeness

7 Algorithmic Complexity Time Complexity: How long does the algorithm take to run? Space Complexity: How much memory does the algorithm require?

8 What happens when the input size gets big?

9 Big-O Notation (Algorithm Order) When input sizes get big, the largest term dominates the expression

10 Big-O Notation (Algorithm Order) When input sizes get big, the largest term dominates the expression We can describe the order of the growth function by O(largest term) 2n 2 + 3n + 7 O(n 2 )

11 Big-O Notation (Algorithm Order) When input sizes get big, the largest term dominates the expression We can describe the order of the growth function by O(largest term) 2n 2 + 3n + 7 O(n 2 ) 77n + 1 O(n)

12 Big-O Notation (Algorithm Order) When input sizes get big, the largest term dominates the expression We can describe the order of the growth function by O(largest term) 2n 2 + 3n + 7 O(n 2 ) 77n + 1 O(n) 122 O(1)

13 Big-O Notation (Algorithm Order) When input sizes get big, the largest term dominates the expression We can describe the order of the growth function by O(largest term) 2n 2 + 3n + 7 O(n 2 ) 77n + 1 O(n) 122 O(1) 3 log n + 60 O(log n)

14 Big-O Notation Formal Definition The function f(n) is a member of the set O(g(n)) if there exist positive constants c and and n 0, such that: 0 f(n) c g(n) for all n n 0

15 c g(n) f(n) n n 0

16 Is 17n n + 2 O(n 2 )?

17 Is 17n n + 2 O(n 2 )? Is 17n n + 2 O(n 157 )? Is 17n n + 2 O(2 n)?

18 Big-O Notation (Algorithm Order) O(n) is an upper bound on the growth of the function

19 Big-O Notation (Algorithm Order) O(n) is an upper bound on the growth of the function Two algorithms with the same order are considered roughly equivalent Is this always a good idea?

20 Ω-Notation Formal Definition The function f(n) is a member of the set Ω(g(n)) if there exist positive constants c and and n 0, such that: 0 c g(n) f(n) for all n n 0

21 f(n) c g(n) n n 0

22 ϴ-Notation Formal Definition The function f(n) is a member of the set ϴ(g(n)) if there exist positive constants c 1, c 2, and n 0, such that: 0 c 1 g(n) f(n) c 2 g(n) for all n n 0

23 c 2 g(n) f(n) c 1 g(n) n n 0

24 Algorithm Running Times Linear search? Binary search? Selection sort? Quicksort? Generating chess future positions? Database select query?

25 TM Running Times Input length: n Time complexity: f(n) Where f is the maximum number of steps taken by the TM for an input of size n

26 TM Example TM, M, with L(M) = {0 k 1 k } M's algorithm: 1. Scan across tape and reject if a 0 is found to the right of a 1 2. Repeat: Cross off a single 0 and a single 1 3. If extra 0's or 1's remain, then reject. Otherwise, accept.

27 TM Example TM, M, with L(M) = {0 k 1 k } n steps M's algorithm: 1. Scan across tape and reject if a 0 is found to the right of a 1 2. Repeat: Cross off a single 0 and a single 1 3. If extra 0's or 1's remain, then reject. Otherwise, accept.

28 TM Example Also, move R/W head back to TM, M, with L(M) = {0 k 1 k } beginning: n steps M's algorithm: 1. Scan across tape and reject if a 0 is found to the right of a 1 2. Repeat: Cross off a single 0 and a single 1 3. If extra 0's or 1's remain, then reject. Otherwise, accept.

29 TM Example TM, M, with L(M) = {0 k 1 k } Total: 2n O(n) M's algorithm: 1. Scan across tape and reject if a 0 is found to the right of a 1 2. Repeat: Cross off a single 0 and a single 1 3. If extra 0's or 1's remain, then reject. Otherwise, accept.

30 TM Example TM, M, with L(M) = {0 k 1 k } M's algorithm: Crossing off one pair: O(n) 1. Scan across tape and reject if a 0 is found to the right of a 1 2. Repeat: Cross off a single 0 and a single 1 3. If extra 0's or 1's remain, then reject. Otherwise, accept.

31 TM Example TM, M, with L(M) = {0 k 1 k } M's algorithm: There are at most n/2 pairs 1. Scan across tape and reject if a 0 is found to the right of a 1 2. Repeat: Cross off a single 0 and a single 1 3. If extra 0's or 1's remain, then reject. Otherwise, accept.

32 TM Example TM, M, with L(M) = {0 k 1 k } M's algorithm: (n/2)o(n) = O(n 2 ) 1. Scan across tape and reject if a 0 is found to the right of a 1 2. Repeat: Cross off a single 0 and a single 1 3. If extra 0's or 1's remain, then reject. Otherwise, accept.

33 TM Example TM, M, with L(M) = {0 k 1 k } M's algorithm: 1. Scan across tape and reject if a 0 is found to A single the scan to check right of a 1 for extras: 2. Repeat: O(n) Cross off a single 0 and a single 1 3. If extra 0's or 1's remain, then reject. Otherwise, accept.

34 TM Example TM, M, with L(M) = {0 k 1 k } M's algorithm: 1. Scan across tape and reject if a 0 is found to the right of a 1 2. Repeat: Cross off a single 0 and a single 1 3. If extra 0's or 1's remain, then reject. Otherwise, accept. Whole algorithm: O(n) + O(n 2 ) + O(n) = O(n 2 )

35 TM with 2 Tapes Example TM, M 2, with L(M) = {0 k 1 k } M 2 's algorithm: 1. Scan across tape and reject if a 0 is found to the right of a 1 2. Scan across 0's on tape 1 and copy each 0 to tape 2 3. Scan across 1's on tape 1 and cross out each 0 on tape 2 4. If we run out of 0's on tape 2 or we have extra 0's then reject. Otherwise, accept.

36 TM with 2 Tapes Example TM, M 2, with L(M) = {0 k 1 k } Time complexity? M 2 's algorithm: 1. Scan across tape and reject if a 0 is found to the right of a 1 2. Scan across 0's on tape 1 and copy each 0 to tape 2 3. Scan across 1's on tape 1 and cross out each 0 on tape 2 4. If we run out of 0's on tape 2 or we have extra 0's then reject. Otherwise, accept.

37 TM with 2 Tapes Example TM, M 2, with L(M) = {0 k 1 k } Time complexity? O(n) M 2 's algorithm: 1. Scan across tape and reject if a 0 is found to the right of a 1 2. Scan across 0's on tape 1 and copy each 0 to tape 2 3. Scan across 1's on tape 1 and cross out each 0 on tape 2 4. If we run out of 0's on tape 2 or we have extra 0's then reject. Otherwise, accept.

38 TM with 2 Tapes Example Time complexity depends on the computational TM, M 2, with L(M) = {0 k 1 k } model selected. M 2 's algorithm: 1. Scan across tape and reject if a 0 is found to the right of a 1 2. Scan across 0's on tape 1 and copy each 0 to tape 2 3. Scan across 1's on tape 1 and cross out each 0 on tape 2 4. If we run out of 0's on tape 2 or we have extra 0's then reject. Otherwise, accept.

39 TM with k Tapes vs. TM with 1 Tape Tape 1 Tape 2 Tape 3 a b b b a b a b a b a a a a b How long does a single tape simulation of a k-tape machine take? Single Tape a b b b a # b a b a b # a a a a b

40 TM with k Tapes vs. TM with 1 Tape Tape 1 Tape 2 Tape 3 Each single transition in the k-tape machine takes two full scans in the single-tape machine: a b b b a b a b a b a a a a b 1. Get the current location of the R/W head for each tape. 2. Actually make the required changes. Single Tape a b b b a # b a b a b # a a a a b

41 TM with k Tapes vs. TM with 1 Tape Tape 1 Tape 2 Tape 3 a b b b a b a b a b a a a a b Simulated tapes can get bigger. If the k-tape machine takes t(n) time, then the increase in used tape length is O(t(n)) Single Tape a b b b a # b a b a b # a a a a b

42 TM with k Tapes vs. TM with 1 Tape Tape 1 a b b b a Tape 2 Tape 3 b a b a b (# steps in k-tape) (cost per step in 1-tape machine) t(n) O(t(n)) = O(t 2 (n)) a a a a b Single Tape a b b b a # b a b a b # a a a a b

43 Nondeterministic TM Decider

44 Nondeterministic TM Decider To be a decider, every branch must halt.

45 Nondeterministic We can simulate a TM Decider nondeterministic TM with a deterministic TM: Build a branching tree of TM configurations in a breadth-first fashion.

46 Nondeterministic TM Decider If a ND TM has time complexity t(n), then there is a corresponding D TM with time complexity 2 O(t(n))

47 Class P P = {A: where the language A is decidable in polynomial time on a deterministic, single-tape TM} An algorithm, M, decides a language in polynomial time if: there exists an integer k 1, such that O(M) = O(n k )

48 Example 1: Find a Graph Path PATH = { G, s, e : G is a directed graph, s is the start node, e is the end node} A start B C D E end

49 Example 1: Find a Graph Path Breadth-first search PATH = { G, s, e : G is a directed from graph, start to end: s is the start node, e is the end node} O( G ) A start PATH P B C D E end

50 Example 2: Relatively Prime RELPRIME = { x,y : x and y are relatively prime (1 is the largest integer that evenly divides both)} Ex: 10 and 21 are relatively prime

51 Example 2: Relatively Prime RELPRIME = { x,y : x and y are relatively prime (1 is the largest integer that evenly divides both)} Ex: 10 and 21 are relatively prime Algorithm: Input x and y Try all possible divisors up to min(x,y)

52 Example 2: Relatively Prime RELPRIME = { x,y : x and y are relatively prime (1 is the largest integer that evenly divides both)} Ex: 10 and 21 are relatively prime Algorithm: Input x and y Try all possible divisors up to min(x,y) Problem: 10,21 = 5, divisorstotest = 9 101,56 = 6, divisorstotest = 55

53 Example 2: Relatively Prime RELPRIME = { x,y : x and y are relatively prime (1 is the largest integer that evenly divides both)} Ex: 10 and 21 are relatively prime Brute force test is Algorithm: exponential. Input x and y Try all possible divisors up to min(x,y) Problem: 10,21 = 5, divisorstotest = 9 101,56 = 6, divisorstotest = 55

54 Example 2: Relatively Prime RELPRIME = { x,y : x and y are relatively prime (1 is the largest integer that evenly divides both)} A new encoding? ,1111 = 11, divisorstotest = , = 16, divisorstotest = 6

55 Example 2: Relatively Prime Doesn't look too RELPRIME = { x,y : x and y are relatively bad... prime (1 is the largest integer that even divides both)} A new encoding? ,1111 = 11, divisorstotest = , = 16, divisorstotest = 6

56 Example 2: Relatively Prime Unary encoding is RELPRIME = { x,y : x and y are relatively cheating. prime (1 is the largest integer that even divides both)} A new encoding? ,1111 = 11, divisorstotest = , = 16, divisorstotest = 6

57 Example 2: Relatively Prime RELPRIME = { x,y : x and y are relatively prime (1 is the largest integer that even divides both)} RELPRIME P Euclid's GCD Algorithm: while y!= 0: x = x % y swap(x, y) return x

58 An interesting problem E = (x 1 x x ) 2 3

59 An interesting problem Is there a combination of x's so that E is TRUE? E = (x 1 x x ) 2 3

60 An interesting problem E = (x 1 x ~x ) (x ~x x )

61 Is this solution correct? An interesting problem x = TRUE 1 x 2 = FALSE x 3 = TRUE x 4 = TRUE E = (x 1 x ~x ) (x ~x x )

62 Is this solution correct? x = TRUE 1 An interesting problem x 2 = FALSE x 3 = TRUE x 4 = TRUE E = (T F F) (F F T) E = (T) E = T (T)

63 An interesting problem It reduces to TRUE. How hard was it to E = (T F F) (F F T) test? E = (T) E = T (T)

64 An interesting problem How can we produce a set of variable values that satisfies the formula? E = (x 1 x ~x ) (x ~x x )

65 An interesting problem How can we produce a set of variable values that satisfies the formula? If n is the number of variables, we can enumerate possible solutions: = 2 n

66 Satisfiability Problem Checking whether an answer is correct isn't too bad

67 Satisfiability Problem Checking whether an answer is correct isn't too bad Finding a correct solution seems harder

68 Class NP NP = {A: if there exists a polynomial, p, and verification language B P, s.t. for all strings w: w A iff s: s p( w ) and w,s B}

69 Class NP Every problem, w, in A... NP = {A: if there exists a polynomial, p, and verification language B P, s.t. for all strings w: w A iff s: s p( w ) and w,s B}

70 Class NP...has a solution... NP = {A: if there exists a polynomial, p, and verification language B P, s.t. for all strings w: w A iff s: s p( w ) and w,s B}

71 Class NP...that's within polynomial length of w... NP = {A: if there exists a polynomial, p, and verification language B P, s.t. for all strings w: w A iff s: s p( w ) and w,s B}

72 Class NP...and the solution can be checked quickly. NP = {A: if there exists a polynomial, p, and verification language B P, s.t. for all strings w: w A iff s: s p( w ) and w,s B}

73 Class NP NP = {A: if there exists a polynomial, p, and verification language B P, s.t. for all strings w: w A iff s: s p( w ) and w,s B} NP = Can be decided by a nondeterministic TM in polynomial time.

74 P NP How do we know this?

75 Does P = NP? This is the most famous unsolved problem in theoretical computer science

76 P = NP? If P = NP... Everyone who could appreciate a symphony would be Mozart; everyone who could follow a step-by-step argument would be Gauss. Scott Aaronson

77 Which one is correct?

78 Polynomial Time Reduction Given two languages, A and B, A is polynomial time reducible to B if a polynomial time function, f, exists: A P B: w A iff f(w) B

79 Polynomial Time Reduction If A P B and B P, then...

80 Polynomial Time Reduction If A P B and B P, then A P.

81 Satisfiability: a special problem Every other problem in NP can be transformed to SAT in polynomial time SAT is in the class called NP-Complete

82 NP-Complete Class Imagine you just figured out a polynomial time solution to SAT.

83 NP-Complete Class Imagine you just figured out a polynomial time solution to SAT. You can now solve any NP problem in polynomial time!

84 NP-Complete Class Imagine you just figured out a polynomial time solution to SAT. You can now solve any NP problem in polynomial time! Then: P = NP

85 Use an Oracle Think of your solution to SAT as an oracle that runs in polynomial time

86 Use an Oracle Think of your solution to SAT as an oracle that runs in polynomial time Other NP problems can simply ask your oracle for help (think function call)

87 Use an Oracle Think of your solution to SAT as an oracle that runs in polynomial time Other NP problems can simply ask your oracle for help (think function call) Since every other NP problem can be reduced to SAT in polynomial time, all can be solved in polynomial time

88 Subset Sum Problem Given a set of integers, determine if there is a non-empty subset that sums to 0. Set = {17, 2, -15, -9, 1, 22, -5}

89 Subset Sum Problem Given a set of integers, determine if there is a non-empty subset that sums to 0. Set = {17, 2, -15, -9, 1, 22, -5} Subset = {1, 2, 17, -15, -5}

90 Subset Sum Problem Given a set of integers, determine if there is a non-empty subset that sums to 0. Set = {17, 2, -15, -9, 1, 22, -5} Subset = {1, 2, 17, -15, -5} NP-Complete

91 Knapsack Problem Given a set of items each with a weight and a value, determine the maximum value while staying under a set weight limit Items = {(3 lbs, $4), (6 lbs, $2), (2 lbs, $1)}

92 Knapsack Problem 0-1 Knapsack Problem: For each item, you either take it or leave it

93 Knapsack Problem 0-1 Knapsack Problem: For each item, you either take it or leave it Unbounded Knapsack Problem: For each item, you may take as many copies of it as you like

94 Knapsack Problem 0-1 Knapsack Problem: For each item, you either take it or leave it Unbounded Knapsack Problem: For each item, you may take as many copies of it as you like Both are NP-Complete

95 L Finite Languages Regular Languages Context-Free Languages P NP Decidable Languages Enumerable Languages

96

97 P = NP A possible proof? Theorem: P = NP Proof: Set N = 1 Clearly, P = NP