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lecture24

# lecture24 - Notes on Complexity Theory Last updated...

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Notes on Complexity Theory Last updated: November, 2011 Lecture 24 Jonathan Katz 1 The Complexity of Counting We explore three results related to hardness of counting. Interestingly, at their core each of these results relies on a simple — yet powerful — technique due to Valiant and Vazirani. 1.1 Hardness of Unique- SAT Does SAT become any easier if we are guaranteed that the formula we are given has at most one solution? Alternately, if we are guaranteed that a given boolean formula has a unique solution does it become any easier to find it? We show here that this is not likely to be the case. Define the following promise problem: USAT def = { φ : φ has exactly one satisfying assignment } USAT def = { φ : φ is unsatisfiable } . Clearly, this problem is in promise- NP . We show that if it is in promise- P , then NP = RP . We begin with a lemma about pairwise-independent hashing. Lemma 1 Let S ⊆ { 0 , 1 } n be an arbitrary set with 2 m ≤ | S | ≤ 2 m +1 , and let H n,m +2 be a family of pairwise-independent hash functions mapping { 0 , 1 } n to { 0 , 1 } m +2 . Then Pr h H n,m +2 [ there is a unique x S with h ( x ) = 0 m +2 ] 1 / 8 . Proof Let 0 def = 0 m +2 , and let p def = 2 - ( m +2) . Let N be the random variable (over choice of random h H n,m +2 ) denoting the number of x S for which h ( x ) = 0 . Using the inclusion/exclusion principle, we have Pr[ N 1] X x S Pr[ h ( x ) = 0 ] - 1 2 · X x 6 = x 0 S Pr[ h ( x ) = h ( x 0 ) = 0 ] = | S | · p - | S | 2 p 2 , while Pr[ N 2] x 6 = x 0 S Pr[ h ( x ) = h ( x 0 ) = 0 ] = ( | S | 2 ) p 2 . So Pr[ N = 1] = Pr[ N 1] - Pr[ N 2] ≥ | S | · p - 2 · | S | 2 p 2 ≥ | S | p - | S | 2 p 2 1 / 8 , using the fact that | S | · p [ 1 4 , 1 2 ]. 24-1

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Theorem 2 (Valiant-Vazirani) If ( USAT , USAT ) is in promise- RP , then NP = RP . Proof If ( USAT , USAT ) is in promise- RP , then there is a probabilistic polynomial-time algorithm A such that φ USAT Pr[ A ( φ ) = 1] 1 / 2 φ USAT Pr[ A ( φ ) = 1] = 0 . We design a probabilistic polynomial-time algorithm B for SAT as follows: on input an n -variable boolean formula φ , first choose uniform m ∈ { 0 , . . . , n - 1 } . Then choose random h H n,m +2 . Us- ing the Cook-Levin reduction, rewrite the expression ψ ( x ) def = ( φ ( x ) ( h ( x ) = 0 m +2 )) as a boolean formula φ 0 ( x, z ), using additional variables z if necessary. (Since h is efficiently computable, the size of φ 0 will be polynomial in the size of φ . Furthermore, the number of satisfying assignments to φ 0 ( x, z ) will be the same as the number of satisfying assignments of ψ .) Output A ( φ 0 ). If φ is not satisfiable then φ 0 is not satisfiable, so A (and hence B ) always outputs 0. If φ is satisfiable, with S denoting the set of satisfying assignments, then with probability 1 /n the value of m chosen by B is such that 2 m ≤ | S | ≤ 2 m +1 . In that case, Lemma 1 shows that with probability at least 1/8 the formula φ 0 will have a unique satisfying assignment, in which case A outputs 1 with probability at least 1 / 2. We conclude that when φ is satisfiable then B outputs 1 with probability at least 1 / 16 n .
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