I'm in a course about computing and complexity, and am unable to understand what these terms mean.
All I know is that NP is a subset of NP-complete, which is a subset of NP-hard, but I have no idea what they actually mean. Wikipedia isn't much help either, as the explanations are still a bit too high level.
Asked By : agent154
Best Answer from StackOverflow
Question Source : http://cs.stackexchange.com/questions/9556
Answered By : Kaveh
I think the Wikipedia articles $\mathsf{P}$, $\mathsf{NP}$, and $\mathsf{P}$ vs. $\mathsf{NP}$ are quite good. Still here is what I would say: Part I, Part II
[I will use remarks inside brackets to discuss some technical details which you can skip if you want.]
Part I
Decision Problems
There are various kinds of computational problems. However in an introduction to computational complexity theory course it is easier to focus on decision problem, i.e. problems where the answer is either YES or NO. There are other kinds of computational problems but most of the time questions about them can be reduced to similar questions about decision problems. Moreover decision problems are very simple. Therefore in an introduction to computational complexity theory course we focus our attention to the study of decision problems.
We can identify a decision problem with the subset of inputs that have answer YES. This simplifies notation and allows us to write $x\in Q$ in place of $Q(x)=YES$ and $x \notin Q$ in place of $Q(x)=NO$.
Another perspective is that we are talking about membership queries in a set. Here is an example:
Decision Problem:
Input: a natural number $x$,
Question: is $x$ an even number?
Membership Problem:
Input: a natural number $x$,
Question: is $x$ in $Even = \{0,2,4,6,\cdots\}$?
We refer to the YES answer on an input as accepting the input and to the NO answer on an input as rejecting the input.
We will look at algorithms for decision problems and discuss how efficient those algorithms are in their usage of computable resources. I will rely on your intuition from programming in a language like C in place of formally defining what we mean by an algorithm and computational resources.
[Remarks: 1. If we wanted to do everything formally and precisely we would need to fix a model of computation like the standard Turing machine model to precisely define what we mean by an algorithm and its usage of computational resources. 2. If we want to talk about computation over objects that the model cannot directly handle, we would need to encode them as objects that the machine model can handle, e.g. if we are using Turing machines we need to encode objects like natural numbers and graphs as binary strings.]
$\mathsf{P}$ = Problems with Efficient Algorithms for Finding Solutions
Assume that efficient algorithms means algorithms that use at most polynomial amount of computational resources. The main resource we care about is the worst-case running time of algorithms with respect to the input size, i.e. the number of basic steps an algorithm takes on an input of size $n$. The size of an input $x$ is $n$ if it takes $n$-bits of computer memory to store $x$, in which case we write $|x| = n$. So by efficient algorithms we mean algorithms that have polynomial worst-case running time.
The assumption that polynomial-time algorithms capture the intuitive notion of efficient algorithms is known as Cobham's thesis. I will not discuss at this point whether $\mathsf{P}$ is the right model for efficiently solvable problems and whether $\mathsf{P}$ does or does not capture what can be computed efficiently in practice and related issues. For now there are good reasons to make this assumption so for our purpose we assume this is the case. If you do not accept Cobham's thesis it does not make what I write below incorrect, the only thing we will lose is the intuition about efficient computation in practice. I think it is a helpful assumption for someone who is starting to learn about complexity theory.
$\mathsf{P}$ is the class of decision problems that can be solved efficiently,
i.e. decision problems which have polynomial-time algorithms.
More formally, we say a decision problem $Q$ is in $\mathsf{P}$ iff
there is an efficient algorithm $A$ such that
for all inputs $x$,
- if $Q(x)=YES$ then $A(x)=YES$,
- if $Q(x)=NO$ then $A(x)=NO$.
I can simply write $A(x)=Q(x)$ but I write it this way so we can compare it to the definition of $\mathsf{NP}$.
$\mathsf{NP}$ = Problems with Efficient Algorithms for Verifying Proofs/Certificates/Witnesses
Sometimes we do not know any efficient way of finding the answer to a decision problem, however if someone tells us the answer and gives us a proof we can efficiently verify that the answer is correct by checking the proof to see if it is a valid proof. This is the idea behind the complexity class $\mathsf{NP}$.
If the proof is too long it is not really useful, it can take too long to just read the proof let alone check if it is valid. We want the time required for verification to be reasonable in the size of the original input, not the size of the given proof! This means what we really want is not arbitrary long proofs but short proofs. Note that if the verifier's running time is polynomial in the size of the original input then it can only read a polynomial part of the proof. So by short we mean of polynomial size.
Form this point on whenever I use the word "proof" I mean "short proof".
Here is an example of a problem which we do not know how to solve efficiently but we can efficiently verify proofs:
Partition
Input: a finite set of natural numbers $S$,
Question: is it possible to partition $S$ into two sets $A$ and $B$ ($A \cup B = S$ and $A \cap B = \emptyset$)
such that the sum of the numbers in $A$ is equal to the sum of number in $B$ ($\sum_{x\in A}x=\sum_{x\in B}x$)?
If I give you $S$ and ask you if we can partition it into two sets such that their sums are equal, you do not know any efficient algorithm to solve it. You will probably try all possible ways of partitioning the numbers into two sets until you find a partition where the sums are equal or until you have tried all possible partitions and none has worked. If any of them worked you would say YES, otherwise you would say NO.
But there are exponentially many possible partitions so it will take a lot of time. However if I give you two sets $A$ and $B$, you can easily check if the sums are equal and if $A$ and $B$ is a partition of $S$. Note that we can compute sums efficiently.
Here the pair of $A$ and $B$ that I give you is a proof for a YES answer. You can efficiently verify my claim by looking at my proof and checking if it is a valid proof. If the answer is YES then there is a valid proof, and I can give it to you and you can verify it efficiently. If the answer is NO then there is no valid proof. So whatever I give you you can check and see it is not a valid proof. I cannot trick you by an invalid proof that the answer is YES. Recall that if the answer is too big it will take a lot of time to verify it, we do not want this to happen, so we only care about efficient proofs, i.e. proofs which have polynomial size.
Sometimes people use "certificate" or "witness" in place of "proof".
Note I am giving you enough information about the answer for a given input $x$ so that you can find and verify the answer efficiently. For example, in our partition example I do not tell you the answer, I just give you a partition, and you can check if it is valid or not. Note that you have to verify the answer yourself, you cannot trust me about what I say. Moreover you can only check the correctness of my proof. If my proof is valid it means the answer is YES. But if my proof is invalid it does not mean the answer is NO. You have seen that one proof was invalid, not that there are no valid proofs. We are talking about proofs for YES. We are not talking about proofs for NO.
Let us look at an example: $A=\{2,4\}$ and $B=\{1,5\}$ is a proof that $S=\{1,2,4,5\}$ can be partitioned into two sets with equal sums. We just need to sum up the numbers in $A$ and the numbers in $B$ and see if the results are equal, and check if $A$, $B$ is partition of $S$.
If I gave you $A=\{2,5\}$ and $B=\{1,4\}$, you will check and see that my proof is invalid. It does not mean the answer is NO, it just means that this particular proof was invalid. Your task here is not to find the answer, but only to check if the proof you are given is valid.
It is like a student solving a question in an exam and a professor checking if the answer is correct. :) (unfortunately often students do not give enough information to verify the correctness of their answer and the professors have to guess the rest of their partial answer and decide how much mark they should give to the students for their partial answers, indeed a quite difficult task).
The amazing thing is that the same situation applies to many other natural problems that we want to solve: we can efficiently verify if a given short proof is valid, but we do not know any efficient way of finding the answer. This is the motivation why the complexity class $\mathsf{NP}$ is extremely interesting (though this was not the original motivation for defining it). Whatever you do (not just in CS, but also in math, biology, physics, chemistry, economics, management, sociology, business, ...) you will face computational problems that fall in this class. To get an idea of how many problems turn out to be in $\mathsf{NP}$ check out a compendium of NP optimization problems. Indeed you will have hard time finding natural problems which are not in $\mathsf{NP}$. It is simply amazing.
$\mathsf{NP}$ is the class of problems which have efficient verifiers, i.e.
there is a polynomial time algorithm that can verify if a given solution is correct.
More formally, we say a decision problem $Q$ is in $\mathsf{NP}$ iff
there is an efficient algorithm $V$ called verifier such that
for all inputs $x$,
- if $Q(x)=YES$ then there is a proof $y$ such that $V(x,y)=YES$,
- if $Q(x)=NO$ then for all proofs $y$, $V(x,y)=NO$.
We say a verifier is sound if it does not accept any proof when the answer is NO. In other words, a sound verifier cannot be tricked to accept a proof if the answer is really NO. No false positives.
Similarly, we say a verifier is complete if it accepts at least one proof when the answer is YES. In other words, a complete verifier can be convinced of the answer being YES.
The terminology comes from logic and proof systems. We cannot use a sound proof system to prove any false statements. We can use a complete proof system to prove all true statements.
The verifier $V$ gets two inputs,
- $x$ : the original input for $Q$, and
- $y$ : a suggested proof for $Q(x)=YES$.
Note that we want $V$ to be efficient in the size of $x$. If $y$ is a big proof the verifier will be able to read only a polynomial part of $y$. That is why we require the proofs to be short. If $y$ is short saying that $V$ is efficient in $x$ is the same as saying that $V$ is efficient in $x$ and $y$ (because the size of $y$ is bounded by a fixed polynomial in the size of $x$).
In summery, to show that a decision problem $Q$ is in $\mathsf{NP}$ we have to give an efficient verifier algorithm which is sound and complete.
Historical Note: historically this is not the original definition of $\mathsf{NP}$. The original definition uses what is called non-deterministic Turing machines. These machines do not correspond to any actual machine model and are difficult to get used to (at least when you are starting to learn about complexity theory). I have read that many experts think that they would have used the verifier definition as the main definition and even would have named the class $\mathsf{VP}$ (for verifiable in polynomial-time) in place of $\mathsf{NP}$ if they go back to the dawn of the computational complexity theory. The verifier definition is more natural, easier to understand conceptually, and easier to use to show problems are in $\mathsf{NP}$.
$\mathsf{P}\subseteq \mathsf{NP}$
Therefore we have $\mathsf{P}$=efficient solvable and $\mathsf{NP}$=efficiently verifiable. So $\mathsf{P}=\mathsf{NP}$ iff the problems that can be efficiently verified are the same as the problems that can be efficiently solved.
Note that any problem in $\mathsf{P}$ is also in $\mathsf{NP}$, i.e. if you can solve the problem you can also verify if a given proof is correct: the verifier will just ignore the proof!
That is because we do not need it, the verifier can compute the answer by itself, it can decide if the answer is YES or NO without any help. If the answer is NO we know there should be no proofs and our verifier will just reject every suggested proof. If the answer is YES, there should be a proof, and in fact we will just accept anything as a proof.
[We could have made our verifier accept only some of them, that is also fine, as long as our verifier accept at lest one proof the verifier works correctly for the problem.]
Here is an example:
Sum
Input: a list of $n+1$ natural numbers $a_1,\cdots,a_n$, and $s$,
Question: is $\Sigma_{i=1}^n a_i = s$?
The problem is in $\mathsf{P}$ because we can sum up the numbers and then compare it with $s$, we return YES if they are equal, and NO if they are not.
The problem is also in $\mathsf{NP}$. Consider a verifier $V$ that gets a proof plus the input for Sum. It acts the same way as the algorithm in $\mathsf{P}$ that we described above. This is an efficient verifier for Sum.
Note that there are other efficient verifiers for Sum, and some of them might use the proof given to them. However the one we designed does not and that is also fine. Since we gave an efficient verifier for Sum the problem is in $\mathsf{NP}$. The same trick works for all other problems in $\mathsf{P}$ so $\mathsf{P} \subseteq \mathsf{NP}$.
Brute-Force/Exhaustive-Search Algorithms for $\mathsf{NP}$ and $\mathsf{NP}\subseteq \mathsf{ExpTime}$
The best algorithms we know of for solving an arbitrary problem in $\mathsf{NP}$ are brute-force/exhaustive-search algorithms. Pick an efficient verifier for the problem (it has an efficient verifier by our assumption that it is in $\mathsf{NP}$) and check all possible proofs once by one. If the verifier accepts one of them then the answer is YES. Otherwise the answer is NO.
In our partition example, we try all possible partitions and check if the sums are equal in any of them.
Note that the brute-force algorithm runes in worst-case exponential time. The size of the proofs is polynomial in the size of input. If the size of the proofs is $m$ then there are $2^m$ possible proofs. Checking each of them will take polynomial time by the verifier. So in total the brute-force algorithm takes exponential time.
This shows that any $\mathsf{NP}$ problem can be solved in exponential time, i.e. $\mathsf{NP}\subseteq \mathsf{ExpTime}$. (Moreover the brute-force algorithm will use only a polynomial amount of space, i.e. $\mathsf{NP}\subseteq \mathsf{PSpace}$ but that is a story for another day).
A problem in $\mathsf{NP}$ can have much faster algorithms, for example any problem in $\mathsf{P}$ has a polynomial-time algorithm. However for an arbitrary problem in $\mathsf{NP}$ we do not know algorithms that can do much better. In other words, if you just tell me that your problem is in $\mathsf{NP}$ (and nothing else about the problem) then the fastest algorithm that we know of for solving it takes exponential time.
However it does not mean that there are not any better algorithms, we do not know that. As far as we know it is still possible (though thought to be very unlikely by almost all complexity theorists) that $\mathsf{NP}=\mathsf{P}$ and all $\mathsf{NP}$ problems can be solved in polynomial time.
Furthermore, some experts conjecture that we cannot do much better, i.e. there are problems in $\mathsf{NP}$ that cannot be solved much more efficiently than brute-force search algorithms which take exponential amount of time. See the Exponential Time Hypothesis for more information. But this is not proven, it is only a conjecture. It just shows how far we are from finding polynomial time algorithms for arbitrary $\mathsf{NP}$ problems.
This association with exponential time confuses some people: they think incorrectly that $\mathsf{NP}$ problems require exponential-time to solve (or even worse there are no algorithm for them at all). Stating that a problem is in $\mathsf{NP}$ does not mean a problem is difficult to solve, it just means that it is easy to verify, it is an upper bound on the difficulty of solving the problem, and many $\mathsf{NP}$ problems are easy to solve since $\mathsf{P}\subseteq\mathsf{NP}$.
Never the less, there are $\mathsf{NP}$ problems which seem to be hard to solve. I will return to this in when we discuss $\mathsf{NP}$-hardness.
Lower Bounds Seem Difficult to Prove
OK, so we now know that there are many natural problems that are in $\mathsf{NP}$ and we do not know any efficient way of solving them and we suspect that they really require exponential time to solve. Can we prove this?
Unfortunately the task of proving lower bounds is very difficult. We cannot even prove that these problems require more than linear time! let alone requiring exponential time.
Proving linear-time lower bounds is rather easy: the algorithm needs to read the input after all. Proving super-linear lower bounds is a completely different story. We can prove super-linear lower bounds with more restrictions about the kind of algorithms we are considering, e.g. sorting algorithms using comparison, but we do not know lower-bounds without those restrictions.
To prove an upper bound for a problem we just need to design a good enough algorithm. It often needs knowledge, creative thinking, and even ingenuity to come up with such an algorithm.
However the task is considerably simpler compared to proving a lower bound. We have to show that there are no good algorithms. Not that we do not know of any good enough algorithms right now, but that there does not exist any good algorithms, that no one will ever come up with a good algorithm. Think about it for a minute if you have not before, how can we show such an impossibility result?
This is another place where people get confused. Here "impossibility" is a mathematical impossibility, i.e. it is not a short coming on our part that some genius can fix in future. When we say impossible we mean it is absolutely impossible, as impossible as $1=0$. No scientific advance can make it possible. That is what we are doing when we are proving lower bounds.
To prove a lower bound, i.e. to show that a problem requires some amount of time to solve, means that we have to prove that any algorithm, even very ingenuous ones that do not know yet, cannot solve the problem faster. There are many intelligent ideas that we know of (greedy, matching, dynamic programming, linear programming, semidefinite programming, sum-of-squares programming, and many other intelligent ideas) and there are many many more that we do not know of yet. Ruling out one algorithm or one particular idea of designing algorithms is not sufficient, we need to rule out all of them, even those we do not know about yet, even those may not ever know about! And one can combine all of these in an algorithm, so we need to rule out their combinations also. There has been some progress towards showing that some ideas cannot solve difficult $\mathsf{NP}$ problems, e.g. greedy and its extensions cannot work, and there are some work related to dynamic programming algorithms, and there are some work on particular ways of using linear programming. But these are not even close to ruling out the intelligent ideas that we know of (search for lower-bounds in restricted models of computation if you are interested).
Barriers: Lower Bounds Are Difficult to Prove
On the other hand we have mathematical results called barriers that say that a lower-bound proof cannot be such and such, and such and such almost covers all techniques that we have used to prove lower bounds! In fact many researchers gave up working on proving lower bounds after Alexander Razbarov and Steven Rudich's natural proofs barrier result. It turns out that the existence of particular kind of lower-bound proofs would imply the insecurity of cryptographic pseudorandom number generators and many other cryptographic tools.
I say almost because in recent years there has been some progress mainly by Ryan Williams that has been able to intelligently circumvent the barrier results, still the results so far are for very weak models of computation and quite far from ruling out general polynomial-time algorithms.
But I am diverging. The main point I wanted to make was that proving lower bounds is difficult and we do not have strong lower bounds for general algorithms solving $\mathsf{NP}$ problems.
[On the other hand, Ryan Williams' work shows that there are close connections between proving lower bounds and proving upper bounds. See his talk at ICM 2014 if you are interested.]
Reductions: Solving a Problem Using Another Problem as a Subroutine/Oracle/Black Box
The idea of a reduction is very simple: to solve a problem, use an algorithm for another problem.
Here is simple example: assume we want to compute the sum of a list of $n$ natural numbers and we have an algorithm $Sum$ that returns the sum of two given numbers. Can we use $Sum$ to add up the numbers in the list? Of course!
Problem:
Input: a list of $n$ natural numbers $x_1,\ldots,x_n$,
Output: return $\sum_{i=1}^{n} x_i$.
Reduction Algorithm:
- $s = 0$
- for $i$ from $1$ to $n$
2.1. $s = Sum(s,x_i)$- return $s$
Here we are using $Sum$ in our algorithm as a subroutine. Note that we do not care about how $Sum$ works, it acts like black box for us, we do not care what is going on inside $Sum$. We often refer to the subroutine $Sum$ as oracle. It is like the oracle of Delphi in Greek mythology, we ask questions and the oracle answers them and we use the answers.
This is essentially what a reduction is: assume that we have algorithm for a problem and use it as an oracle to solve another problem. Here efficient means efficient assuming that the oracle answers in a unit of time, i.e. we count each execution of the oracle a single step.
If the oracle returns a large answer we need to read it and that can take some time, so we should count the time it takes us to read the answer that oracle has given to us. Similarly for writing/asking the question from the oracle. But oracle works instantly, i.e. as soon as we ask the question from the oracle the oracle writes the answer for us in a single unit of time. All the work that oracle does is counted a single step, but this excludes the time it takes us to write the question and read the answer.
Because we do not care how oracle works but only about the answers it returns we can make a simplification and consider the oracle to be the problem itself in place of an algorithm for it. In other words, we do not care if the oracle is not an algorithm, we do not care how oracles comes up with its replies.
For example, $Sum$ in the question above is the addition function itself (not an algorithm for computing addition).
We can ask multiple questions from an oracle, and the questions does not need to be predetermined: we can ask a question and based on the answer that oracle returns we perform some computations by ourselves and then ask another question based on the answer we got for the previous question.
Another way of looking at this is thinking about it as an interactive computation. Interactive computation in itself is large topic so I will not get into it here, but I think mentioning this perspective of reductions can be helpful.
An algorithm $A$ that uses a oracle/black box $O$ is usually denoted as $A^O$.
The reduction we discussed above is the most general form of a reduction and is known as black-box reduction (a.k.a. oracle reduction, Turing reduction).
More formally:
We say that problem $Q$ is black-box reducible to problem $O$ and write $Q \leq_T O$ iff
there is an algorithm $A$ such that for all inputs $x$,
$Q(x) = A^O(x)$.
In other words if there is an algorithm $A$ which uses the oracle $O$ as a subroutine and solves problem $Q$.
If our reduction algorithm $A$ runs in polynomial time we call it a polynomial-time black-box reduction or simply a Cook reduction (in honor of Stephen A. Cook) and write $Q\leq^\mathsf{P}_T O$. (The subscript $T$ stands for "Turing" in the honor of Alan Turing).
However we may want to put some restrictions on the way the reduction algorithm interacts with the oracle. There are several restrictions that are studied but the most useful restriction is the one called many-one reductions (a.k.a. mapping reductions).
The idea here is that on a given input $x$, we perform some polynomial-time computation and generate a $y$ that is an instance of the problem the oracle solves. We then ask the oracle and return the answer it returns to us. We are allowed to ask a single question from the oracle and the oracle's answers is what will be returned.
More formally,
We say that problem $Q$ is many-one reducible to problem $O$ and write $Q \leq_m O$ iff
there is an algorithm $A$ such that for all inputs $x$,
$Q(x) = O(A(x))$.
When the reduction algorithm is polynomial time we call it polynomial-time many-one reduction or simply Karp reduction (in honor of Richard M. Karp) and denote it by $Q \leq_m^\mathsf{P} O$.
The main reason for the interest in this particular non-interactive reduction is that it preserves $\mathsf{NP}$ problems: if there is a polynomial-time many-one reduction from a problem $A$ to an $\mathsf{NP}$ problem $B$, then $A$ is also in $\mathsf{NP}$.
The simple notion of reduction is one of the most fundamental notions in complexity theory along with $\mathsf{P}$, $\mathsf{NP}$, and $\mathsf{NP}$-complete (which we will discuss below).
The post has become too long and exceeds the limit of an answer (30000 characters). I will continue the answer in Part II.
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