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P and NP
Computational Complexity
• Recall from our sorting examples at the start
of class that we could prove that any sort
would have to do at least some minimal
amount of work (lower bound)
– We proved this using decision trees (ch 7.8)
– (the following decision tree slides are repeated)
// Sorts an array of 3 items
void sortthree(int s[]) {
a=s[1]; b=s[2]; c=s[3];
if (a < b) {
if (b < c) {
S = a,b,c;
} else {
if (a < c) {
S = a,c,b;
} else {
S = c,a,b;
a,b,c
}}
} else if (b < c) {
if (a < c) {
S = b,a,c;
} else {
S = b,c,a;
}}
else {
S = c,b,a;
}
}
a<b
b<c
b<c
a<c
a,c,b
c,a,b
a<c
b,a,c
c,b,a
b,c,a
Decision Trees
• A decision tree can be created for every
comparison-based sorting algorithm
– The following is a decision tree for a 3 element
Exchange sort
• Note that “c < b” means that the Exchange sort
compares the array item whose current value is c
with the one whose current value is b – not that it
compares s[3] to s[2].
b<a
c<a
c<b
b<a
c,b,a
c<a
b,c,a
b,a,c
a<b
c,a,b
c<b
a,c,b
a,b,c
Decision Trees
• So what does this tell us…
– Note that there are 6 leaves in each of the
examples given (each N=3)
• In general there will be N! leaves in a decision tree
corresponding to the N! permutations of the array
– The number of comparisons (“work”) is equal
to the depth of the tree (from root to leaf)
• Worst case behavior is the path from the root to the
deepest leaf
Decision Trees
• Thus, to get a lower bound on the worst
case behavior we need to find the shortest
tree possible that can still hold N! leaves
– No comparison-based sort could do better
• A tree of depth d can hold 2d leaves
– So, what is the minimal d where 2d >= N!
• Solving for d we get d >= log2(N!)
– The minimal depth must be at least log2(N!)
Decision Trees
• According to Lemma 7.4 (p. 291):
log2(N!) >= n log2(n) – 1.45n
• Putting that together with the previous result
d must be at least as great as (n log2(n) – 1.45n)
• Applying Big-O
d must be at least O(n log2(n))
• No comparison-based sorting algorithm can
have a running time better than O(n log2(n))
Decision Trees for other problems?
• Unfortunately, this is a lot of work and the
technique only applies directly to comparisonbased sorts
• What if the problem is Traveling Salesperson?
– The book has only shown exponential time (or
worse) algorithms for this problem
– What if your boss wants a faster implementation?
• Do you try and find one?
• Do you try and prove one doesn’t exist?
Polynomial-Time Algorithms
• A polynomial-time algorithm is one whose
worst-case running time is bounded above
by a polynomial function
– Poly-time examples: 2n, 3n, n5, n log(n), n100000
– Non-poly-time examples: 2n, 20.000001n, n!
• Poly-time is important because for large
problem sizes, all non-poly-time algorithms
will take forever to execute
Intractability
• In Computer Science, a problem is called
intractable if it is impossible to solve it with a
polynomial-time algorithm
– Let me stress that intractability is a property of
the problem, not just of any one algorithm to
solve the problem
• There can be no poly-time algorithm that solves the
problem if the problem is to be considered intractable
• And just because one non-poly-time algorithm exists
for the problem does not make it intractable
Three Categories of Problems
• We can group problems into 3 categories:
– Problems for which poly-time algorithms have been found
– Problems that have been proven to be intractable
• Proven that no poly-time algorithms exist
– Problems that have not been proven to be intractable, but for
which poly-time algorithms have never been found
• No one has found a poly-time algorithm, but no one has proven that
one doesn’t exist either
• The interesting thing is that tons of problems fall into
the 3rd category and almost none into the second
Poly-time Category
• Any problem for which we have found a
poly-time algorithm
– Sorting, searching, matrix multiplication,
chained matrix multiplication, shortest paths,
minimal spanning tree, etc.
Intractable Category
• Two types of problems
– Those that require a non-polynomial amount of output
• Determining all Hamiltonian Circuits
– (n – 1)! Circuits in worst case
– Those that produce a reasonable amount of output, but
the processing time is just too long
• These are called undecidable problems
• Very few of these, but one classic is the Halting Problem
– Takes as input any algorithm and any input and will tell you if the
algorithm halts when run on the input
Unknown Category
• Many problems belong in the category
– 0-1 Knapsack, Traveling Salesperson, mcoloring, Hamiltonian Circuits, etc.
– In general, any problem that we had to solve
using backtracking or bounded backtracking
falls into this category
The Theory of NP
• In the following slides we will show a close and
interesting relationship among many of the
problem in the Unknown Category
• It will be more convenient to develop this theory
restricting ourselves to decision problems
– Problems that have a yes/no answer
– We can always convert non-decision problems into
decision problems
• In 0/1 Knapsack instead of just asking for the optimal profit,
we can instead ask if the optimal profit exceeds some number
• In graph coloring instead of just asking the minimal number of
colors we can instead ask if the minimal number is less than m
The Set P
• The set P is the set of all decision problems
that can be solved by polynomial-time
algorithms
• What problems are in P?
– Obviously all the ones we have found poly-time
solutions for (sorting, etc)
– What about problems like Traveling Salesperson?
The Set NP
• The set NP is the set of all decision problems
that can be solved by a polynomial-time nondeterministic algorithm
– A poly-time non-deterministic algorithm is an
algorithm that is broken into 2 stages:
• Guessing (non-deterministic) stage
• Verification (deterministic) stage
– Where the verification stage can be accomplished
in poly-time
Non-deterministic Algorithms
• For Traveling Salesperson:
– The guessing stage simply guesses possible tours
– The verification stage takes a tour from the guessing stage
and decides yes/no
• is it a tour that has a total weight of no greater than x?
• Obviously, this verification stage can be written in
poly-time
– Note, however, that the guessing stage may or may not be
done in poly-time
• Note that the purpose of this type of algorithm is for
theory and classification – there are usually much
better ways to actually implement the algorithm
NP
• There are thousands of problems that have been
proven to be in NP
• Further note that all problems in P are also in NP
– The guessing stage can do anything it wants
– The verification stage can just run the algorithm
• The only problems proven to not be in NP are the
intractable ones
– And there are only a few of these
P and NP
• Here is the way the
picture of the sets is
usually drawn
• That is, we know that
P is a subset of NP
• However, we don’t
know if it is a proper
subset
P and NP
• That is, no one has ever proven that there
exists a problem in NP that is not in P
• So, NP – P could be an empty set
– If it is then we say that P = NP
• The question of whether P = NP is one of
the more intriguing and important questions
in all of Computer Science
P = NP?
• To prove that P  NP we would have to find a
single problem in NP that is not in P
• To prove P = NP we would have to find a polytime algorithm for each problem in NP
– This route sounds much harder, but over the next few
slides we will show a way to simplify this proof
• If you prove either you get an A in the class, not to
mention famous
– And if you prove P = NP then you also become rich!
NP-Complete Problems
• Over the next few slides we develop the
background for a new set called NP-Complete
• This set will help us in attempting to prove that
P = NP
Conjunctive Normal Form (CNF)
• A logical (boolean) variable is one that can have 2
values: true or false
• A literal is a logical variable (x) or the negation of a
logical variable (!x)
• A clause is a sequence of literals separated by Ors:
(x1 or x2 or !x3)
• A logical expression in conjunctive normal form
(CNF) is a sequence of clauses separated by ANDs:
(x1 or x2 or !x3) and (x2 or !x1) and (x2 or x3)
• We are about to use CNF for our theory, but there
are many uses for CNF including AI
CNF-Satisfiability Problem
• Given a logical expression in CNF, determine
whether there is some truth assignment (some set
of assignments of true and false to the variables)
that makes the whole expression true
• (x1 or x2) and (x2 or !x3) and (!x2)
– Yes, x1 = true, x2 = false, x3 = false
• (x1 or X2) and !x1 and !x2
– No
CNF-Satisfiability Problem
• It is easy to write a poly-time algorithm that takes
as input a logical expression in CNF and a set of
true assignments a verifies if the expression is true
for that assignment
– Therefore, the problem is in the set NP
• Further, no one has ever found a poly-time
algorithm for this entire problem and no one has
ever proven that it cannot be solved in poly-time
– So we do not know if it is in P or not
CNF-Satisfiability Problem
• However, in 1971 Stephen Cook published a paper
proving that if CNF-Satisfiability is in P, then P = NP
• WOW! If we can just prove that this simple problem
is in P then suddenly all NP problems are in P
– And all sorts of things like encryption will break!
• Before we talk about how this works, we need to
introduce the concept of a transformation algorithm
Transformation Algorithms
• Suppose we want to solve decision problem A and
we have an algorithm that solves decision problem
B
• Suppose that we can write an algorithm that
creates an instance y of problem B for every
instance x of problem A s.t.
– The algorithm for B answers yes for y iff the answer to
problem A is yes for x
– Such an algorithm is called a transformation algorithm
Transformation Algorithms
• An simple example of a transformation algorithm
(having nothing to do with NP problems)
• A: given n logical variables, does at least one
have a value true?
• B: Given n integers, is the largest of positive?
• Our transformation is k1,k2..kn = tran(x1,x2..xn)
– Where ki is 1 if xi is true and ki is 0 if xi is false
Reducibility
• If there exists a poly-time transformation
algorithm from decision problem A to
decision problem B, then we say A is polytime reducible to B (or A reduces to B)
AB
• Further, if B is in set P and A  B then A is
also in set P
NP-Complete Problems
• A problem B is called NP-Complete if
– It is in NP and
– For every other problem A in NP, A  B
• By the previous theorem if we could show that
any NP-complete problem is in P then we could
conclude that P = NP
• So, Cook basically proved that CNF-Satisfiability
is NP-complete
– Cook did not reduce all other NP problems to CNFSatisfiability, instead he did it by exploiting common
properties of all NP problems
NP-Complete Problems
• Now we can use transitivity to get:
– A problem C is NP-complete if:
• It is in NP and
• For some other NP-complete problem B, B  C
• Researchers have spent the last 30 years creating
transformation for these problems and we now
have a list of hundreds of NP-complete problems
– If any of these NP-complete problems can be proven to
be in P then P = NP
• And, additionally, we also have a way to solve all the NP
problems (poly-time transformations to the poly-time problem)
The State of P and NP
• All this sounds promising, but…
– Over the last 30 years no one has been able prove
that any problem from NP is not in P
– Over the last 30 years no one has been able to
prove that any problem from NP-complete is in P
– Proving either of these things would give us an
answer to the open problem of P = NP?
• Most people seem to believe that P  NP

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