1. An Introduction to Computational Complexity Edith Elkind IAM, ECS

2. Knapsack Problem n items, each item has – a weight w i – a value v i Knapsack capacity W Target value V Question: can we pack the knapsack to reach the target value? – is there a vector (x 1,..., x n )  {0, 1} n such that w 1 x 1 +... + w n x n ≤ W v 1 x 1 +... + v n x n ≥ V

3. Knapsack Problem: Special Cases Subset Sum: – for each item weight = value – capacity of knapsack = target value – Question: is there a vector (x 1,..., x n )  {0, 1} n such that w 1 x 1 +... + w n x n = W Partition: can we split a given list into 2 equal-weight parts? – like Subset Sum, but additionally w 1 +... + w n = 2W

4. Subset Sum is NP-complete Reduction from 1-in-3-SAT – n variables, m clauses – can you set the variables so that in each clause, exactly one variable is satisfied? x1 V ¬x2 V x3, x2 V x3 V x4: x1=T, x2=T, x3=F, x4=F YES x1 V x2 V x3, x1 V ¬x2 V x3, ¬ x1 V x2 V x3: x1=T => contradiction x1=F => contradiction NO

5. Subset Sum is NP-complete Proof: – 2n numbers: 2 per variable (one for x i, one for ¬x i ) – each number has 2(n+m) binary digits – digits 2i-1, 2i encode variable i 001000000 xixi ¬x i 2i-12i 001000000011010... W forced to pick exactly one of x i, ¬x i 2n...

6. Subset Sum is NP-complete Proof: – 2n numbers: 2 per variable – each number has 2(n+m) digits – digits 2n+2j-1, 2n+2j encode clause j 0010... x1x1 ¬x 3 2j-12j 0010... 0110 0010 W x7x7 forced to pick exactly one of x 1, ¬x 3, x 7 c j = x 1 V ¬x 3 V x 7 2n

7. Subset Sum is NP-complete Proof: – 2n numbers: 2 per variable – each number has 2(n+m) digits – digits 2i-1, 2i encode variable i – digits 2n+2j-1, 2n+2j encode clause j – pick exactly one of the numbers for x i, ¬x i – can obtain W iff exactly one variable per clause is satisfied

8. What about Partition and Knapsack? Subset Sum is a special case of Knapsack => Knapsack is NP-hard Can reduce Subset Sum to Partition: – instance of Subset Sum: (w 1,..., w n ; W) – set X = w 1 +... + w n – instance of Partition: (3X-W, 2X+W, w 1,..., w n ) yes-instance of SS => yes-instance of P: 3X-W + subset of w i s of weight W has weight 3X 2X+W + subset of w i s of weight X-W has weight 3X yes-instance of P => yes-instance of SS any set of weight 3X contains exactly one of 3X-W, 2X+W

9. Knapsack: an (Efficient?) Algorithm Dynamic programming: V(i, w): maximum value you can achieve by using a subset of the first i items of weight w – V(1, w): 0 if w ≠ w 1 and v 1 if w = w 1 – V(i+1, w): max {V(i, w), v i+1 +V(i, w - w i+1 )} 000050000038050000 item 1: w 1 = 4, v 1 = 5 item 2: w 2 = 2, v 1 = 3...xy z z = max {y, v i+1 + x} w i+1 i=2 i=1 i+1 i

10. Knapsack: an (Efficient?) Algorithm V(i, w): maximum value you can achieve by using a subset of the first i items of weight w V(n, w): maximum value of a subset of weight w if V(n, w) ≥ V for some w ≤ W, the answer is “yes”, else “no” Running time: – each V(i, w) can be computed in log V time – nW values to compute – final scan: O(W) O(nW log V)

11. What Is an Efficient Algorithm? Knapsack input: – n+1 numbers of size at most W each – n+1 numbers of size at most V each – representation size: O(n(log W + log V)) numbers are represented in binary Dynamic programming algorithm: O(nW log V) – exponential in input size: W is exponentially bigger than log W

12. Small Numbers Knapsack input: – n+1 numbers of size at most W each – n+1 numbers of size at most V each – representation size: O(n(log W + log V)) What if weights an values are small? – W = poly(n), V = poly(n) – DP running time: O(nW log V) = poly (n): polynomial in input size!

13. Binary vs. unary notation Knapsack input: – n+1 numbers of size at most W each – n+1 numbers of size at most V each What if inputs are given in unary? – w is represented as 111.....111 – representation size: O(n(W + V)) DP running time: O(nW log V): polynomial in input size! w times

14. Strong vs. Weak NP-hardness A problem is weakly NP-hard, if it is hard when inputs are given in binary, but not in unary A problem is strongly NP-hard, if it is hard even when inputs are given in unary Example: Longest Path – input: Graph G=(V, E), source s, sink t, target k, edge lengths l 1, …, l |E| – question: is there a loop-free path from s to t of length at least k? – hard even for small weights (in fact, 0/1 weights)

15. Implications If you have an NP-hard problem, try to figure out where the hardness comes from: – big numbers in the problem? – structure of the problem? – both? Useful for understanding which special cases are likely to be easy

16. Coping With Intractability Good algorithm: (1)returns exact answer (2)works on all instances (3)runs in poly-time For NP-hard problems, can have at most 2 out of 3! – (1)+(2): exp-time algorithms (can be practical) – (1)+(3): heuristics – (2)+(3): approximation algorithms

17. Approximation Algorithms yes/no problems => problems with numeric answers problem X: I (instance) => S(I)  R (solution space) maximization problems: OPT(I) = max S(I) – max-value Knapsack, longest path minimization problems: OPT(I) = min S(I) – shortest TSP tour, smallest vertex cover

18. Approximation Algorithms Definition: – an algorithm A is an c-approximation for a maximization problem X if for every instance I, A outputs A(I)  S(I) such that OPT(I)/c ≤ A(I) ≤ OPT(I) – an algorithm A is an c-approximation for a minimization problem X if for every instance I, A outputs A(I)  S(I) such that OPT(I) ≤ A(I) ≤ cOPT(I) c: approximation ratio

19. How Do You Bound the Approximation Ratio? We do not know the value of OPT(I) How can we prove that something is within a constant factor from OPT(I)? General idea (minimization version): – find an easily computable lower bound on OPT(I) i.e., OPT(I) ≥ X – show that A(I) is within a constant factor of X i.e., A(I) ≤ cX

20. Example: Vertex Cover Instance: Graph G=(V, E), target K Question: is there a V’  V such that – |V’|≤ K and for any (u, v)  E either u  V’ or v  V’ Optimization version: what is the size of the smallest vertex cover?

21. Vertex Cover: 2-Approximation 1.Start with V’ empty 2.While there are unmarked edges left, repeat: Pick an arbitrary edge e=(u, v) and mark it Add u, v to V’ Remove from G all unmarked edges incident to u or v except (u, v)

22. Vertex Cover: Proof Claim 1: marked edges form a matching (do not share vertices) Claim 2: Given any matching M = {(u 1, v 1 ),..., (u k, v k )}, any vertex cover (including OPT(I)) must contain at least one of u i, v i for each pair (u i, v i ) in M V’ contains both – hence, V’ is at most twice as large as OPT(I)

23. Can We Do Better? Claim: unless P ≠ NP, any poly-time approximation algorithm for VC has approximation ratio at least 1+1/(n+1) Proof: suppose OPT(I) ≤ A(I) ≤ OPT(I)+OPT(I)/(n+1) – OPT(I) < n, so OPT(I)/(n+1) < 1 – A(I), OPT(I) are both integer and differ by < 1 – A(I) = OPT(I) and P = NP

24. Polynomial-time Approximation Scheme (PTAS) Definition (maximization version): A is a PTAS for a problem P if: – inputs: instance I of P, an error parameter  – output: A(I,  ) such that (1-  )OPT(I) ≤ A(I,  ) ≤ OPT(I) – for every fixed , running time of A is poly(I) FPTAS: same, but running time of A is poly(I, 1/  ) for all  (n/  2 is an FPTAS, n 1/  is a PTAS, but not FPTAS)

25. Tool: Another Pseudopolynomial Algorithm for Knapsack Knapsack: n items, each item has a weight w i and a value v i capacity W, target value V poly (n, W, log V) algorithm: fill out table P(i, w): maximal value of a subset of {1,..., i} of weight w Claim: there is an algorithm for Knapsack that runs in time poly (n, V, log W) Proof: fill out table W(i, v), i=1,..., n, v=1,..., V: minimal weight of a subset of {1,..., i} of value v

26. Tool: Another Pseudopolynomial Algorithm for Knapsack Claim: there is an algorithm for Knapsack that runs in time poly (n, V, log W) Proof: fill out table W(i, v), i=1,..., n, v=1,..., V: W(i, v): minimal weight of a subset of {1,..., i} of value V – W(1, v): +∞ if v ≠ v 1 and w 1 if v = v 1 – W(i+1, v): min {W(i, v), w i+1 +W(i, v - v i+1 ) Check if W(n, v) ≤ W for some v ≥ V

27. Pseudopolynomial Algorithm => FPTAS Algorithm from previous slide has running time poly (n, V, log W) – polynomial if V = poly(n) – also polynomial if values are drawn from a “small” set: e.g., all values are of the form kX, where k is at most poly(n); X can be huge – idea: make the set of possible values small by rounding

28. FPTAS for Knapsack Knapsack: n items, each item has a weight w i and a value v i capacity W, target value V Parameter  Algorithm: set  =  v max /n, and set v’ i = max{k  | k  < v i } Let k i = v’ i /  ; observe that k i ≤ n/  (n/  = v max ) recall: W(i, v)=min weight of a subset of {1,..., i} of value v W’(i, k): min weight of a subset of {1,..., i} of value k  can be computed in the same way by DP

29. FPTAS for Knapsack: Bounding the Error W’(i, k): min weight of a subset of {1,..., i} of value k  Compute W’(i, k) for i=1,..., n, k=1,..., n 2 /  n 2 /  ≥ V) Let V’ = max {k  | W’(n, k) ≤ W} How different is V’ from OPT? – consider an opt solution i 1,...., i t – v i1 +... + v it = OPT – we have V’ ≥ v’ i1 +... + v’ it – also v’ i1 ≥ v i1 -  for all i 1,...., i t V’ ≥ OPT - n  = OPT –  v max ≥ OPT –  OPT ≥ OPT(1-  )

30. More Complexity Theory Recall: 3-SAT is the question of whether a given Boolean formula is satisfiable Tautology: is a given formula a tautology, i.e., is it true for all values of variables? Does not seem to be in NP: what’s the witness? Easy to prove that a formula is NOT a tautology: exhibit a falsifying assignment coNP: problems whose complements are in NP

31. More about coNP Complement of a problem: Q is a complement of P if a yes-instance of P is a no-instance of Q and vice versa How to prove that a problem is in coNP? – prove that its complement is in NP coNP-hard: as hard as any problem in coNP How to prove that a problem is coNP-hard? – prove that its complement is NP-hard It is believed that coNP ≠ P, coNP ≠ NP

32. Even Harder Problems (1/2) Minimal Circuit: given a Boolean circuit C with n inputs, is it minimal? – is it the case that for any circuit C’ with |C’|<|C| there is an input x such that C’(x) ≠ C(x)? Does not seem to be in NP... Complement: is a given Boolean circuit non-minimal? – does not seem to be in NP either: even if you guess C’, need to show C’(x)=C(x) for all x

33. Even Harder Problems (2/2) Team Stability: – set of n agents N – any subset S of N can form a team and earn u(S) assume that u(S) is poly-time computable Question: can we split agents into teams S 1,..., S k and share the earnings so that no group of agents wants to deviate? – is there a partition (S 1,..., S k ) and a vector (p 1,..., p n ) such that  j  Si p j = u(S i ) for all i=1,..., k and  j  S p j ≥ u(S) for all S  N?

34.  2 and  2 Minimal Circuit: is it the case that for any circuit C’ with |C’|<|C| there exists an input x such that C’(x) ≠ C(x)? Team Stability: does there exist a partition (S 1,..., S k ) and a vector (p 1,..., p n ) such that  j  Si p j = u(S i ) for all i=1,..., k and  j  S p j ≥ u(S) for all S  N?  2 : problems of the form  x  y P(x, y)  2 : problems of the form  x  y P(x, y) (where P(x, y) is polynomial-time checkable)

35. Polynomial Hierarchy –  2 : problems of the form  x  y P(x, y) –  2 : problems of the form  x  y P(x, y) – NP (=  1 ): problems of the form  xP(x) – coNP (=  1 ): problems of the form  x P(x) – can extend this further:  k : k alternations of quantifiers starting with   k : k alternations of quantifiers starting with  – It is believed that all these classes are distinct – can show  k   k+1,  k+1 and  k   k+1,  k+1

36. PSPACE PSPACE: the class of problems you can solve using polynomial space P  PSPACE: can’t use up more than poly space in poly time NP  PSPACE: can check all candidates reusing the space  2  PSPACE: e.g., for Min Circuit, need – poly(n) bits to go over circuits – poly(n) bits to go over Boolean strings

37. More about PSPACE all of polynomial hierarchy is in PSPACE EXPTIME: problems solvable in time 2 poly(n) – PSPACE  EXPTIME are there PSPACE-complete problems? Unbounded Quantifier Alternation: check if  x1  y1  x2  y2....P(x1, y1, x2, y2,....) is satisfiable Claim: Unbounded Quantifier Alteration is PSPACE-complete

38. Problems in PSPACE: Other Examples given a 2-player game, does the first player have a winning strategy? – does there exist a move for player 1 such that for any move of player 2 there exists a move of player 1 such that.... Any deterministic 2-player game that is guaranteed to terminate is in PSPACE Some are PSPACE-complete: a version of GO

39. Counting Solutions Sometimes you want to know how many solutions are there – e.g., if there are many, you can sample #P: class of counting problems whose corresponding decision problems are in NP – #SAT: how many satisfying assignments a given formula has #P-completeness: complete under counting reductions (preserve the number of solutions) – #SAT is #P-complete

40. Summary Lecture 1: – polynomial time – P vs. NP – NP-complete problems Lecture 2: – strong NP-hardness – pseudopolynomial algorithms – approximation algorithms – polynomial hierarchy, PSPACE, #P