# Introduction to Logic in Computer Science: Autumn 2006

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1 Introduction to Logic in Computer Science: Autumn 2006 Ulle Endriss Institute for Logic, Language and Computation University of Amsterdam Ulle Endriss 1

2 Plan for Today Now that we have a basic understanding of what complexity classes are and how they relate to each other, we are going to identify some specific decision problems that are complete with respect to interesting complexity classes (mostly NP): The original NP-complete problem: the satisfiability problem for propositional logic Variants of the satisfiability problem, some of which are also NP-complete, and some of which are not NP-complete problems in graph theory An example for a PSPACE-complete problem: satisfiability for quantified boolean formulas Ulle Endriss 2

3 Cook s Theorem The first decision problem ever to be shown to be NP-complete is the satisfiability problem for propositional logic. Satisfiability (Sat) Instance: Propositional formula ϕ Question: Is ϕ satisfiable? The size of an instance of Sat is the length of ϕ. Clearly, Sat can be solved in exponential time (by trying all possible models), but no (deterministic) polynomial algorithm is known. Theorem 1 (Cook) Sat is NP-complete. Proof: NP-membership is easy to show: if someone guesses a satisfying assignment of truth values to propositional letters (model), then we can check its correctness in polynomial time. The proof of NP-hardness is sketched on the following slides... Ulle Endriss 3

4 Proof of NP-Hardness (1) We need to show that any problem in NP can be reduced to Sat. By definition, for any problem in NP there is a nondeterministic algorithm that accepts or rejects all instances of the problem in polynomial time. We now sketch how to encode this algorithm as a propositional formula. Suppose the algorithm runs in time n k. So it will use at most n k memory cells. Hence, there exists (nondeteminism!) a table of size n k n k for each possible run of the algorithm, with each table cell holding an element belonging to some (small) alphabet. Introduce a propositional variable x ijs for each row i, column j, symbol s, saying whether or not cell (i, j) is holding symbol s. The number of these variables is polynomial in n. If we can fully specify all constraints these x ijk have to meet to represent a well-formed computation table for an accepting run as a formula (of polynomial size), then we are done, as that formula will be satisfiable iff there is an accepting run. Ulle Endriss 4

5 Proof of NP-Hardness (2) Follows Sipser (1997). Each table cell must hold exactly one symbol: 2 3 ϕ cell = ^ x ijs ^ (x ijs x ijs ) 5 1 i,j n k 4 _ s S s,s S We can also encode the initial configuration as a conjunction ϕ start, and the characteristics of an accepting configuration as a formula ϕ accept. To encode what is a legal move from one configuration to the next, we need to be more specific about our machine model. As any algorithm can be implemented on a Turing machine, we may assume that the contents of memory cell (i, j) will only depend on the content of its predecessor and that cells neighbours: (i 1, j 1), (i 1, j), (i 1, j +1). We can write a formula ϕ move(i,j ) specifying all possible moves of this sort for (i, j). Then ϕ move = V 1 i,j n k ϕ move(i,j ) describes all legal moves. Finally, ϕ cell ϕ start ϕ accept ϕ move encodes the algorithm. M. Sipser. Introduction to the Theory of Computation. Thomson, Ulle Endriss 5

6 Variants of Satisfiability If we restrict the structure of propositional formulas, then there s a chance that the satisfiability problem will become easier. k-satisfiability (ksat) Instance: Conjunction ϕ of disjunctions of k literals each Question: Is ϕ satisfiable? Alternative formulation: Check whether a given set S of k-clauses is satisfiable. By close inspection of the proof for Sat, it is possible to show (see book for details): Theorem 2 3Sat is NP-complete. Only once we go down from 3 to 2, we get a positive result... Ulle Endriss 6

7 Theorem 3 2Sat is in P. 2SAT is in P Proof sketch: Recall that Reachability P. We are going to reduce 2Sat to Reachability. Let ϕ be a formula in CNF with clauses of length 2 and let P be the set of propositional variables in ϕ. Build a graph G = (V, E) with V = P { p p P } and (x, y) E iff a there is a clause in ϕ that is equivalent to x y. Observe that ϕ is unsatisfiable iff there is a p P such that there is both a path from p to p and from p to p in G. This condition can be tested by running our algorithm for Reachability several times. Ulle Endriss 7

8 Counting Clauses If not all clauses of a given formula in CNF can be satisfied simultaneously, what is the maximum number of clauses that can? Maximum k-satisfiability (MaxkSat) Instance: Set S of k-clauses and K N Question: Is there a satisfiable S S such that S K? For this kind of problem, we cross the border between P and NP already for k = 2 (rather than k = 3, as before): Theorem 4 Max2Sat is NP-complete. Proof sketch: Max2Sat is clearly in NP: if someone guesses an S S and a model with S K, we can check whether S is true in that model in polynomial time. Next we show NP-hardness by reducing 3Sat to Max2Sat... Ulle Endriss 8

9 Reduction from 3SAT to MAX2SAT Consider the following 10 clauses: (x), (y), (z), (w), ( x y), ( y z), ( z x), (x w), (y w), (z w) Observe: any model satisfying (x y z) can be extended to satisfy (at most) 7 of them; all other models satisfy at most 6 of them. Given an instance of 3Sat, construct an instance of Max2Sat: For each clause C i = (x i y i z i ) in ϕ, write down these 10 clauses with a new w i. If the input has n clauses, set K = 7n. Then ϕ is satisfiable iff (at least) K of the 2-clauses in the new problem are satisfiable. Ulle Endriss 9

10 Satisfiability for Horn Clauses Another restriction of the satisfiability problem: Horn Satisfiability (HornSat) Instance: Conjunction ϕ of Horn clauses Question: is ϕ satisfiable? Recall that Prolog is built around Horn clauses. Theorem 5 HornSat is in P. Proof sketch: The following (polynomial) algorithm does the job. Distinguish negative Horn clauses: ( x 1 x k ); and implications (including atoms: x 1 = true): (x 1 x l y). Initially, make all variables false. Iterate: if (x 1 x l y) not satisfied, then make y true minimal model M for implications. Full formula can only be satisfiable if M satisfies all negative clauses as well. For if not, we d get {x 1,..., x k } M and we d have to make one of the x i false, thereby contradicting minimality. Ulle Endriss 10

11 Independent Sets Many conceptually simple problems that are NP-complete can be formulated as problems in graph theory. Example: Let G = (V, E) be an undirected graph. An independent set is a set I V such that there are no edges between any of the vertices in I. Independent Set Instance: Undirected graph G = (V, E) and K N Question: Does G have an independent set I with I K? Theorem 6 Independent Set is NP-complete. Proof sketch: NP-membership: easy NP-hardness: by reduction from 3Sat with n clauses Given a conjunction ϕ of 3-clauses, construct a graph G = (V, E). V is the set of occurrences of literals in ϕ. Edges: make a triangle for each 3-clause, and connect complementary literals. Set K = n. Then ϕ is satisfiable iff there is an independent set of size K. Ulle Endriss 11

12 More Graph-theoretic Problems All of the following are also NP-complete problems. They each take an undirected graph G = (V, E) and an integer K as input. Clique: Is there a V V with V K such that any two vertices in V are joined by an edge in E? Vertex Cover: Is there a V V with V K such that for any edge (x, y) E either x V or y V? Hamilton Path: Does G have a Hamilton path (that is, a path visiting every vertex)? (no K needed as input) Travelling Salesman Problem: Is there a path K visiting each vertex once? (additional input: distances) MaxCut: Is there a V V such that the number of edges between nodes in V and in V \V exceeds K? Ulle Endriss 12

13 Quantified Boolean Formulas The canonical example for a PSPACE-complete problem is the satisfiability problem for quantified boolean formulas. A quantified boolean formula (QBF) is a propositional formula ϕ preceded by either x or x for each propositional variable in ϕ: x. y. z.[(x y) z] The semantics is exactly what you would expect it to be... This gives rise to a new decision problem: Quantified Satisfiability (QSat) Instance: Quantified boolean formula ϕ Question: Is ϕ satisfiable (true)? The size of a QSat instance is the length of ϕ. Ulle Endriss 13

14 PSPACE-Completeness Theorem 7 QSat is PSPACE-complete. Proof idea: PSPACE-membership: Recursively go through the formula in the obvious manner. The recursion depth is given by the number of propositional variables, and at each state we have to remember what satisfiability requirements we still need to meet (polynomial space). PSPACE-hardness: We need to chow that any problem in PSPACE can be encoded as a QBF. Use the same idea as for Cook s Theorem: encode the computation table as a formula. Problem: the number of rows in the table might be exponential. Solution: Use the idea from the proof of Savitch s Theorem and write a QBF expressing that you can go from configuration c 1 to configuration c 2 in t steps if there s another configuration c such that you can go from c 1 to c and from c to c 2 in t 2 steps each... Ulle Endriss 14

15 Remark The satisfiability and the validity problem coincide for QBFs. This matches up nicely with PSPACE = copspace. For comparison, in propositional logic these are distinct problems and we don t know whether NP =? conp. Ulle Endriss 15

16 References Again, all of today s material can be found in Papadimitriou s book. Cook s Theorem is proved in Chapter 8, and further NP-completeness results are established in Chapter 9. The PSPACE-completeness proof for quantified boolean formulas may be found in Chapter 19. For large collections of NP-complete problems, the books by Garey and Johnson (1979) and Ausiello et al. (1999) are useful references. C.H. Papadimitriou. Computational Complexity. Addison-Wesley, M.R. Garey and D.S. Johnson. Computers and Intractability: A Guide to the Theory of NP-Completeness. W.H. Freeman and Company, G. Ausiello, P. Crescenzi, G. Gambosi, V. Kann, A. Marchetti-Spaccamela, and M. Protasi. Complexity and Approximation. Springer-Verlag, Ulle Endriss 16

17 Summary We have seen a range of NP-complete problems: Logic: Sat, 3Sat, Max2Sat, but not 2Sat and HornSat Graph Theory: Independent Set and others Recall that the P-NP borderline is widely considered to represent the move from tractable to intractable problems, so developing a feel for what sort of problems are NP-complete is important to understand what can and what cannot be computed in practice. In logic, in particular, we also encounter problems that are a lot harder: the PSPACE-completeness result for QSat is an example. Ulle Endriss 17

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