1. Hardness Results for Problems
Example Problems
P: Class of “easy to solve” problems
Absolute hardness results
Relative hardness results
Reduction technique

2. Example: Clique Problem
Input: Undirected graph G = (V,E), integer k
Y/N Question: Does G contain a clique of size ≥ k?
k=4
k=5

3. Example: Vertex Cover k=3 k=2
Input: Undirected graph G = (V,E), integer k
Y/N Question: Does G contain a vertex cover of size ≤ k?
Vertex cover: A set of vertices C such that for every edge (u,v) in E, either u is in C or v is in C (or both are in C)
k=3
k=2

4. Example: Satisfiability
Input: Set of variables X and set of clauses C over X
Y/N Question: Is there a satisfying truth assignment T for the variables in X such that all clauses in C are true?

5. Hardness Results for Problems
Example Problems
P: Class of “easy to solve” problems
Absolute hardness results
Relative hardness results
Reduction technique

6. Fundamental Setting
When faced with a new problem P, we alternate between the following two goals
Find a “good” algorithm for solving P
Use algorithm design techniques
Prove a “hardness result” for problem P
No “good” algorithm exists for problem P

7. Complexity Class P
P is the set of problems that can be solved using a polynomial-time algorithm
Sometimes we focus only on decision problems
The task of a decision problem is to answer a yes/no question
If a problem belongs to P, it is considered to be “efficiently solvable”
If a problem is not in P, it is generally considered to be NOT “efficiently solvable”
Looking back at previous slide, our goals are to:
Prove that P belongs to P
Prove that P does not belong to P

8. Hardness Results for Problems
Example Problems
P: Class of “easy to solve” problems
Absolute hardness results
Relative hardness results
Reduction technique

9. Absolute Hardness Results
Fuzzy Definition
A hardness result for a problem P without reference to another problem
Examples
Solving the clique problem requires W(n) time in the worst-case
Solving the clique problem requires W(2n) time in the worst-case.
The clique problem is not in P.

10. Proof Techniques Diagonalization Information Theory argument
Can be used to prove some problems are not in P
Information Theory argument
W(nlog n) lower bound for sorting
Typically not a superpolynomial lower bounds
“Size of input” argument
Prove that solving the graph connectivity problem requires W(V2) time
Prove that solving the maximum clique problem requires W(V2) time
Typically not a superpolynomial lower bound

11. Status Many natural problems can be shown to be in P
Graph connectivity
Shortest Paths
Minimum Spanning Tree
Very few natural problems have been proven to NOT be in P
Variants of halting problem are one example
Many natural problems cannot be placed in or out of P
Satisfiability
Longest Path Problem
Clique Problem
Vertex Cover Problem

12. Hardness Results for Problems
Example Problems
P: Class of “easy to solve” problems
Absolute hardness results
Relative hardness results
Reduction technique

13. Relative Hardness Results
Fuzzy Definition
A hardness result for a problem P with reference to another problem
Examples
Satisfiability is at least as hard as Clique to solve
If Satisfiability is unsolvable, then Clique is unsolvable.
If Satisfiability is in P, then Clique is in P
If Clique is not in P, then Satisfiability is not in P

14. Important Observation
We are interested in relative hardness results BECAUSE of our inability to prove absolute hardness results
That is, if we could prove strong absolute hardness results, we would not be as interested in relative hardness results
Example
If I could prove “Satisfiability is not in P”, then I would be less interested in proving “If Clique is not in P, then Satisfiability is not in P”.

15. Relative Hardness Proof Technique
We show that P2 is at least as hard as P1 in the following way
Informal: We show how to solve problem P1 using a procedure P2 that solves P2 as a subroutine

16. Examples Multiplication and Squaring square(x) = mult(x,x)
Proves multiplication is at least as hard as squaring
mult(x,y) = (square(x+y) – square(x-y))/4
Prove squaring is at least as hard as multiplication
Assumes that addition, subtraction, and division by 4 can be done with no substantial increase in complexity
Specific complexity of multiplication may be higher as there are two calls to square, but the difference is polynomially bounded

17. Hardness Results for Problems
Example Problems
P: Class of “easy to solve” problems
Absolute hardness results
Relative hardness results
Reduction technique

18. Decision Problems We restrict our attention to decision problems
Key characteristic: 2 types of inputs
Yes input instances
No input instances
Almost all natural problems can be converted into an equivalent decision problem without changing the complexity of the problem
One technique: add an extra input variable that represents the solution for the original problem

19. Optimization to Decision
Example using clique problem
Optimization Problems
Input: Graph G=(V,E)
Task: Output size of maximum clique in G
Task 2: Output a maximum sized clique of G
Decision Problem
Input: Graph G=(V,E), integer k ≤ |V|
Y/N Question: Does G contain a clique of size k?
Your task
Show that if we can solve decision clique in polynomial-time, then we can solve the optimization clique problems in polynomial-time.

20. Polynomial-time Reduction Technique
Can describe as a polynomial-time “Answer-preserving input transformation”
Consider two problems П1 and П2, and suppose I want to show
If П2 is in P, then П1 is in P
If П1 is not in P, then П2 is not in P
The basic idea
Develop a function (reduction) R that maps input instances of problem П1 to input instances of problem П2
The function R should be computable in polynomial time
x is a yes input to П1 ↔ R(x) is a yes input to П2
Notation: П1 ≤p П2

21. What П1 ≤p П2 Means If R exists, then: If П2 is in P, then П1 is in P
Yes Input
to П2
P2 solves
П2 in poly
time
Yes Input to П1
Yes R
No Input
to П2 No
No Input to П1
P1 solves П1 in polynomial-time
If R exists, then:
If П2 is in P, then П1 is in P
If П1 is not in P, then П2 is not in P

22. Showing П1 ≤p П2 For any x input for П1, specify what R(x) will be
Show that R(x) has polynomial size relative to x
You should show that R runs in polynomial time; I only require the size requirement above
Show that if x is a yes instance for П1, then R(x) is a yes instance for П2
Show that if x is a no instance for П1, then R(x) is a no instance for П2
Often done by showing that if R(x) is a yes instance for П2, then x must have been a yes instance for П1