CS 6520: Computational Complexity Problem Set 1, Assignments of Computer Science

Ten problems related to computational complexity, including reductions between problems, proofs of np-completeness, and properties of self-reducible languages. Topics covered include clique, sat, quadratic, np minimization, σp-completeness, factoring, self-reducibility, and nl-completeness.

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CS 6520: Computational Complexity
Problem Set 1
Due March 4, 2008
Problem 1
Give a Karp reduction from CLIQUE to SAT.
Problem 2
Let Quadratic be the problem of deciding whether a given system of
quadratic multivariate polynomial equations with integer coefficients has
a solution modulo 2. Prove that Quadratic is NP-Complete.
Problem 3
An NP minimization problem is defined by an objective function Obj :
Σ×ΣNfor which there is a constant cand an algorithm that for every
x, y {0,1}, computes Obj(x, y) in time O(|x|c). For such an objective
function Obj, the corresponding NP minimization problem is: Given xΣ,
find a yΣsuch that Obj(x, y) is minimized. Prove that P=NP if and
only if every NP minimization problem has a polynomial-time algorithm.1
Problem 4
Prove that for each iN, Σi-SAT is ΣP
i-Complete.
Problem 5
Consider the Factoring problem: Given a natural number N, express N
as a product of its prime factors. To date no polynomial-time algorithm
for Factoring is known, and the conjectured hardness of Factoring has
been the basis of several public-key cryptosystems.
1. Prove that if P=NP coNP, then Factoring can be solved by a
polynomial-time algorithm.
You may find the following facts useful:
1An NP maximization problem can be defined similarly, for which an analogous result
holds.
1
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CS 6520: Computational Complexity

Problem Set 1

Due March 4, 2008

Problem 1

Give a Karp reduction from CLIQUE to SAT.

Problem 2

Let Quadratic be the problem of deciding whether a given system of quadratic multivariate polynomial equations with integer coefficients has a solution modulo 2. Prove that Quadratic is NP-Complete.

Problem 3

An NP minimization problem is defined by an objective function Obj : Σ∗^ × Σ∗^ → N for which there is a constant c and an algorithm that for every x, y ∈ { 0 , 1 }∗, computes Obj(x, y) in time O(|x|c). For such an objective function Obj, the corresponding NP minimization problem is: Given x ∈ Σ∗, find a y ∈ Σ∗^ such that Obj(x, y) is minimized. Prove that P = NP if and only if every NP minimization problem has a polynomial-time algorithm.^1

Problem 4

Prove that for each i ∈ N, Σi-SAT is ΣPi - Complete.

Problem 5

Consider the Factoring problem: Given a natural number N , express N as a product of its prime factors. To date no polynomial-time algorithm for Factoring is known, and the conjectured hardness of Factoring has been the basis of several public-key cryptosystems.

  1. Prove that if P = NP ∩ coNP, then Factoring can be solved by a polynomial-time algorithm. You may find the following facts useful: (^1) An NP maximization problem can be defined similarly, for which an analogous result holds.
  • The Fundamental Theorem of Arithmetics: Every integer greater than one can be decomposed into a product of prime factors, and moreover such a decomposition is unique up to the order of the prime factors.
  • There is a polynomial-time algorithm for deciding whether a given integer is a prime.
  1. Prove that unless NP = coNP, Factoring is not NP-Hard under Cook reduction. Conclude that unless NP = PH, Factoring is not NP-Hard under Cook reduction.

Problem 6

A language L is self-reducible if there is a polynomial-time oracle machine M such that (a) M L^ decides L, and (b) on every input x, M only makes queries of length strictly smaller than |x|. For instance, as shown in class, SAT and TQBF are self-reducible. Prove that every self-reducible language is in PSPACE.

Problem 7

  1. A directed graph G = (V, E) is strongly connected if for every pair of vertices u, v ∈ V , there is a path from u to v in G. Prove that the problem of deciding whether a given directed graph is strongly connected is NL-Complete.
  2. Prove that 2 - SAT is NL-Complete.
  3. Let Bipartite be the language consisting of bipartite graphs. Prove that Bipartite ∈ NL.
  4. Let USTCONN be the following problem: Given an undirected graph G = (V, E) and two vertices s, t ∈ V , decide whether there is a path from s to t in G. Prove that USTCONN and Bipartite are computa- tionally equivalent under log-space reductions.