SDSU CS660 Combinatorial Algorithms
Fall Semester, 1996
NP Completeness

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San Diego State University -- This page last updated Dec 5, 1996

Contents of NP Completeness

  1. Reference
  2. NP Completeness

NP Completeness Slide # 1


Introduction to Algorithms, by Cormen, Leiserson, Rivest, Chapter 36

NP Completeness Slide # 2

NP Completeness

Abstract problem Q
Q is a binary relation on set of problem instances I and a set of problem solutions S

Example - Shortest Path
Find the shortest path in a graph between two vertices.
I = { (G, s, e) | G = (V, E) is unweighted undirected graph, s V is the start vertex, e V is the end vertex }
S = { (G, p) | G = (V, E), p is a path in G }
Let a =( G, s, e ) I then
Q(a) = { (G, p) |p is the shortest path between s and e in G }
Usually use Shortest Path instead of Q

NP Completeness Slide # 3

Decision Problems - Problems with yes/no answers

Given a graph G = (V, E) and two vertices s, e V does there exist a path in G between s and e whose length is at most k?
I = { (G, s, e, k) | G = (V, E) is unweighted undirected graph, s V is the start vertex, e V is the end vertex, k upper bound on shortest path }
S = { 0, 1 }

Let a = ( G, s, e, k ) I then
Path(a) = 1 if shortest path from s to e has length <= k
Path(a) = 0 if shortest path from s to e has length > k
Decision problems simplify theory

Most problems can be restated as a decision problem

NP Completeness Slide # 4

Concrete Problem is a problem whose instance is the set of binary strings

Let x be an integer, is x even?
Let I = { binary strings}, S = { 0, 1 }
Let x I then we have
Even(x) = 1
if x1 = 1
Even(x) = 0
if x0 = 1

An algorithm solves a concrete problem in time O(T(n)) if
given a I with n = |a| the algorithm finds the solution in at most O( T(n) ) time

Polynomial-time solvable
A concrete problem is polynomial-time solvable if there exits an algorithm to solve it in time O(nk) for some constant k

Complexity class P = { concrete problem that are polynomial-time solvable }

NP Completeness Slide # 5

An encoding "e" is a mapping from a set S to binary strings
Use encodings to map abstract problems to concrete problems

Example - Shortest Path
              s             a             b             c             d             e             
s             0             1             0             1             0             0             
a             1             0             1             0             1             0             
b             0             1             0             1             0             1             
c             1             0             1             1             0             0             
d             0             1             0             1             0             1             
e             0             0             1             0             1             0             


NP Completeness Slide # 6
Encodings change the time complexity

Example Is K a prime number?
divide K by all integers 2, 3, 4, ..., K -1
If any integer divides K it is not prime
Worst case requires K -2 divisions (Use K for simplicity)
Embedding 1 K -> binary representation of K
Input length is n = lg(K)
Time Complexity T(n) = K = 2n
Embedding 2 Place the J'th bit of K in location 2J, fill rest of locations with 0.
Input length is n = K
Time Complexity T(n) = n

f : {0, 1}* -> {0, 1}* is polynomial-time computable if there exists a polynomial-time algorithm A that, given any input x {0, 1}*, produces as output f(x)

Given set of problem instances I, encodings e1 and e2 are polynomially related if there exists polynomial-time computable functions f1 and f2 such that
a I -> f1( e1( a ) ) = e2( a ) and f2( e2( a ) ) = e1( a )

Let Q be an abstract decision problem on an instance set I, and let e1 and e2 be encodings polynomially related encodings on I. Then
e1( Q ) P if and only if e2( Q ) P

NP Completeness Slide # 8
The Final Step - Formal-language

Alphabet a finite set of symbols

Language L over
any set of strings made up of symbols from

Empty string denoted by

Empty language by

language of all strings over

Complement of L

Concatenation of two languages L1 and L2 is the language:
L = { xy : x L1 and y L2 }

NP Completeness Slide # 9

Closure or Kleene Star of L is
Decision Problems as a language
Let = {0, 1} then set of binary strings =
Let Q be a decision problem
L = {x : Q(x) = 1 }

Algorithm A accepts a string x { 0, 1 }* if A(x) = 1

Algorithm A rejects a string x { 0, 1 }* if A(x) = 0

NP Completeness Slide # 10

The language accepted by algorithm A is
L = { x { 0, 1 }* : A(x) = 1}

A language L is decided by algorithm A if every binary string is either accepted or rejected by algorithm A

A language L is accepted in polynomial time by algorithm A if for any string of length n in L is accepted by A in time O(nk)

A language L is decided in polynomial time by algorithm A if for any string of length n in { 0, 1 }* is decided by A in time O(nk)

We can redefine P as:
P = { : there exists an algorithm A that decides L in polynomial time }

P = { L : L is accepted by a polynomial-time algorithm }

NP Completeness Slide # 11
Polynomial-time Verification

Hamiltonian cycle of an undirected graph G= (V, E) is a simple cycle that contains each vertex in V

A hamiltonian graph is a graph that has a hamiltonian cycle.

Let m = |V|, it takes m! operations to determine if G= (V, E) is a hamiltonian graph

If Mary claims a graph is hamiltonian graph and provides a the vertices in order on the hamiltonian cycle then we can verify his claim in polynomial time

The potential cycle is called the certificate.

A verification algorithm is a two-argument algorithm A

One argument is an ordinary input string x, the other is the certificate a binary string y.

"A" verifies input x if there exists a certificate such that
A(x, y) = 1

The language verified by A is:

L = { x { 0, 1 }* : there exists y { 0, 1 }* such that A(x, y) = 1}

NP Completeness Slide # 12
Complexity Class NP

Complexity class NP is the class of languages that can be verified by a polynomial-time algorithm


If L P then L is accepted in polynomial time by algorithm B
That is for any x L we can compute B(x) in polynomial time and B(x) = 1
Now let A( x ,y ) = B( x )
Then A( x, y ) verifies x in polynomial time

NP Completeness Slide # 13

Language L1 is polynomial-time reducible to language L2 if there exists a polynomial-time computable function
f : { 0, 1 }* -> { 0, 1 }*
such that for all x { 0, 1 }* we have:
x L1 if and only if f( x ) L2

We write L1 <=p L2

Lemma 36.3 Assume that L1 <=p L2, then L2 P implies L1 P

A language is NP-complete if

1. L NP

2. L1 <=p L for all L1 NP

Theorem 36.4
a. If any NP-complete problem is polynomial solvable then
P = NP.
b. If any NP-complete problem is not polynomial solvable then all NP-complete problems are not polynomial solvable