P versus NP problem

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The P versus NP problem is a major unsolved problem in computer science. Informally, it asks whether every problem whose solution can be efficiently checked by a computer can also be efficiently solved by a computer. It was introduced in 1971 by Stephen Cook in his paper "The complexity of theorem proving procedures" and is considered by many to be the most important open problem in the field. It is one of the seven Millennium Prize Problems selected by the Clay Mathematics Institute to carry a US$ 1,000,000 prize for the first correct solution.

In essence, the question P = NP? asks:

The theoretical notion of quick used here is an algorithm that runs in polynomial time. The general class of questions for which some algorithm can provide an answer in polynomial time is called "class P" or just "P".

For some questions, there is no known way to find an answer quickly, but if one is provided with information showing what the answer is, it may be possible to verify the answer quickly. The class of questions for which an answer can be verified in polynomial time is called NP.

Consider the subset sum problem, an example of a problem that is easy to verify, but whose answer may be difficult to compute. Given a set of integers, does some nonempty subset of them sum to 0? For instance, does a subset of the set add up to 0? The answer "yes, because add up to zero" can be quickly verified with three additions. However, there is no known algorithm to find such a subset in polynomial time (there is one, however, in exponential time, which consists of 2ⁿ-1 tries), and indeed such an algorithm cannot exist if the two complexity classes are not the same; hence this problem is in NP (quickly checkable) but not necessarily in P (quickly solvable).

An answer to the P = NP question would determine whether problems like the subset-sum problem that can be verified in polynomial time can also be solved in polynomial time. If it turned out that P does not equal NP, it would mean that there are problems in NP (such as NP-complete problems) that are harder to compute than to verify: they could not be solved in polynomial time, but the answer could be verified in polynomial time.

Context

The relation between the complexity classes P and NP is studied in computational complexity theory, the part of the theory of computation dealing with the resources required during computation to solve a given problem. The most common resources are time (how many steps it takes to solve a problem) and space (how much memory it takes to solve a problem).

In such analysis, a model of the computer for which time must be analyzed is required. Typically such models assume that the computer is deterministic (given the computer's present state and any inputs, there is only one possible action that the computer might take) and sequential (it performs actions one after the other).

In this theory, the class P consists of all those decision problems (defined below) that can be solved on a deterministic sequential machine in an amount of time that is polynomial in the size of the input; the class NP consists of all those decision problems whose positive solutions can be verified in polynomial time given the right information, or equivalently, whose solution can be found in polynomial time on a non-deterministic machine. Clearly, P ⊆ NP. Arguably the biggest open question in theoretical computer science concerns the relationship between those two classes: :Is P equal to NP? In a 2002 poll of 100 researchers, 61 believed the answer to be no, 9 believed the answer is yes, and 22 were unsure; 8 believed the question may be independent of the currently accepted axioms and so impossible to prove or disprove.

NP-complete

for P, NP, NP-complete, and NP-hard set of problems]] To attack the P = NP question the concept of NP-completeness is very useful. Informally the NP-complete problems are the "toughest" problems in NP in the sense that they are the ones most likely not to be in P. NP-complete problems are a set of problems that any other NP-problem can be reduced to in polynomial time, but retain the ability to have their solution verified in polynomial time. In comparison, NP-hard problems are those at least as hard as NP-complete problems, meaning all NP-problems can be reduced to them, but not all NP-hard problems are in NP, meaning not all of them have solutions verifiable in polynomial time.

For instance, the decision problem version of the travelling salesman problem is NP-complete, so any instance of any problem in NP can be transformed mechanically into an instance of the traveling salesman problem, in polynomial time. The traveling salesman problem is one of many such NP-complete problems. If any NP-complete problem is in P, then it would follow that P = NP. Unfortunately, many important problems have been shown to be NP-complete, and as of 2011 not a single fast algorithm for any of them is known.

Based on the definition alone it's not obvious that NP-complete problems exist. A trivial and contrived NP-complete problem can be formulated as: given a description of a Turing machine M guaranteed to halt in polynomial time, does there exist a polynomial-size input that M will accept? It is in NP because (given an input) it is simple to check whether or not M accepts the input by simulating M; it is NP-complete because the verifier for any particular instance of a problem in NP can be encoded as a polynomial-time machine M that takes the solution to be verified as input. Then the question of whether the instance is a yes or no instance is determined by whether a valid input exists.

The first natural problem proven to be NP-complete was the Boolean satisfiability problem. This result came to be known as Cook–Levin theorem; its proof that satisfiability is NP-complete contains technical details about Turing machines as they relate to the definition of NP. However, after this problem was proved to be NP-complete, proof by reduction provided a simpler way to show that many other problems are in this class. Thus, a vast class of seemingly unrelated problems are all reducible to one another, and are in a sense "the same problem".

Harder problems

Although it is unknown whether P = NP, problems outside of P are known. A number of succinct problems (problems that operate not on normal input, but on a computational description of the input) are known to be EXPTIME-complete. Because it can be shown that P $\backslash subsetneq$ EXPTIME, these problems are outside P, and so require more than polynomial time. In fact, by the time hierarchy theorem, they cannot be solved in significantly less than exponential time. Examples include finding a perfect strategy for chess (on an N×N board) and some other board games.

The problem of deciding the truth of a statement in Presburger arithmetic requires even more time. Fischer and Rabin proved in 1974 that every algorithm that decides the truth of Presburger statements has a runtime of at least $2^\{2^\{cn\}\}$ for some constant c. Here, n is the length of the Presburger statement. Hence, the problem is known to need more than exponential run time. Even more difficult are the undecidable problems, such as the halting problem. They cannot be completely solved by any algorithm, in the sense that for any particular algorithm there is at least one input for which that algorithm will not produce the right answer; it will either produce the wrong answer, finish without giving a conclusive answer, or otherwise run forever without producing any answer at all.

Problems in NP not known to be in P or NP-complete

It was shown by Ladner that if P ≠ NP then there exist problems in NP that are neither in P nor NP-complete. If graph isomorphism is NP-complete, the polynomial time hierarchy collapses to its second level. Since it is widely believed that the polynomial hierarchy does not collapse to any finite level, it is believed that graph isomorphism is not NP-complete. The best algorithm for this problem, due to Laszlo Babai and Eugene Luks has run time 2^{O(√(n log n))} for graphs with n vertices.

The integer factorization problem is the computational problem of determining the prime factorization of a given integer. Phrased as a decision problem, it is the problem of deciding whether the input has a factor less than k. No efficient integer factorization algorithm is known, and this fact forms the basis of several modern cryptographic systems, such as the RSA algorithm. The integer factorization problem is in NP and in co-NP (and even in UP and co-UP). If the problem is NP-complete, the polynomial time hierarchy will collapse to its first level (i.e., NP will equal co-NP). The best known algorithm for integer factorization is the general number field sieve, which takes expected time O(e^{(64/9)1/3(n.log 2)1/3(log (n.log 2))2/3}) to factor an n-bit integer. However, the best known quantum algorithm for this problem, Shor's algorithm, does run in polynomial time. Unfortunately, this fact doesn't say much about where the problem lies with respect to non-quantum complexity classes.

Does P mean "easy"?

All of the above discussion has assumed that P means "easy" and "not in P" means "hard". This assumption, known as Cobham's thesis, though a common and reasonably accurate assumption in complexity theory, is not always true in practice; the size of constant factors or exponents may have practical importance, or there may be solutions that work for situations encountered in practice despite having poor worst-case performance in theory (this is the case for instance for the simplex algorithm in linear programming). Other solutions violate the Turing machine model on which P and NP are defined by introducing concepts like randomness and quantum computation.

Because of these factors, even if a problem is shown to be NP-complete, and even if P ≠ NP, there may still be effective approaches to tackling the problem in practice. There are algorithms for many NP-complete problems, such as the knapsack problem, the travelling salesman problem and the boolean satisfiability problem, that can solve to optimality many real-world instances in reasonable time. The empirical average-case complexity (time vs. problem size) of such algorithms can be surprisingly low.

Reasons to believe P ≠ NP

According to a poll,

On the other hand, some researchers believe that there is overconfidence in believing P ≠ NP and that researchers should explore proofs of P = NP as well. For example, in 2002 these statements were made:, a foundation for many modern security applications such as secure economic transactions over the Internet, and symmetric ciphers such as AES or 3DES, used for the encryption of communications data. These would need to be modified or replaced by information-theoretically secure solutions.

On the other hand, there are enormous positive consequences that would follow from rendering tractable many currently mathematically intractable problems. For instance, many problems in operations research are NP-complete, such as some types of integer programming, and the travelling salesman problem, to name two of the most famous examples. Efficient solutions to these problems would have enormous implications for logistics. Many other important problems, such as some problems in protein structure prediction, are also NP-complete; if these problems were efficiently solvable it could spur considerable advances in biology.

But such changes may pale in significance compared to the revolution an efficient method for solving NP-complete problems would cause in mathematics itself. According to Stephen Cook,

Research mathematicians spend their careers trying to prove theorems, and some proofs have taken decades or even centuries to find after problems have been stated—for instance, Fermat's Last Theorem took over three centuries to prove. A method that is guaranteed to find proofs to theorems, should one exist of a "reasonable" size, would essentially end this struggle.

A proof that showed that P ≠ NP would lack the practical computational benefits of a proof that P = NP, but would nevertheless represent a very significant advance in computational complexity theory and provide guidance for future research. It would allow one to show in a formal way that many common problems cannot be solved efficiently, so that the attention of researchers can be focused on partial solutions or solutions to other problems. Due to widespread belief in P ≠ NP, much of this focusing of research has already taken place.

A "not equal" resolution to the P versus NP problem still leaves open the average-case complexity of hard problems in NP. For example, it is possible that SAT requires exponential time in the worst case, but that almost all randomly selected instances of it are efficiently solvable. Russell Impagliazzo has described five hypothetical "worlds" that could result from different possible resolutions to the average-case complexity question. These range from "Algorithmica", where P=NP and problems like SAT can be solved efficiently in all instances, to "Cryptomania", where P≠NP and generating hard instances of problems outside P is easy, with three intermediate possibilities reflecting different possible distributions of difficulty over instances of NP-hard problems. The "world" where P≠NP but all problems in NP are tractable in the average case is called "Heuristica" in the paper. A Princeton University workshop in 2009 studied the status of the five worlds.

Results about difficulty of proof

Although the P = NP? problem itself remains open, despite a million-dollar prize and a huge amount of dedicated research, efforts to solve the problem have led to several new techniques. In particular, some of the most fruitful research related to the P = NP problem has been in showing that existing proof techniques are not powerful enough to answer the question, thus suggesting that novel technical approaches are required.

As additional evidence for the difficulty of the problem, essentially all known proof techniques in computational complexity theory fall into one of the following classifications, each of which is known to be insufficient to prove that P ≠ NP:

These barriers are another reason why NP-complete problems are useful: if a polynomial-time algorithm can be demonstrated for an NP-complete problem, this would solve the P = NP problem in a way not excluded by the above results.

These barriers have also led some computer scientists to suggest that the P versus NP problem may be independent of standard axiom systems like ZFC (cannot be proved or disproved within them). The interpretation of an independence result could be that either no polynomial-time algorithm exists for any NP-complete problem, and such a proof cannot be constructed in (e.g.) ZFC, or that polynomial-time algorithms for NP-complete problems may exist, but it's impossible to prove in ZFC that such algorithms are correct. However, if it can be shown, using techniques of the sort that are currently known to be applicable, that the problem cannot be decided even with much weaker assumptions extending the Peano axioms (PA) for integer arithmetic, then there would necessarily exist nearly-polynomial-time algorithms for every problem in NP. Therefore, if one believes (as most complexity theorists do) that not all problems in NP have efficient algorithms, it would follow that proofs of independence using those techniques cannot be possible. Additionally, this result implies that proving independence from PA or ZFC using currently known techniques is no easier than proving the existence of efficient algorithms for all problems in NP.

Claimed solutions

While the P versus NP problem is generally considered unsolved, many amateur and some professional researchers have claimed solutions. Woeginger (2010) has a comprehensive list. An August 2010 claim of proof that P ≠ NP, by Vinay Deolalikar, researcher at HP Labs, Palo Alto, received heavy Internet and press attention after being initially described as "seem[ing] to be a relatively serious attempt" by two leading specialists. The proof has been reviewed publicly by academics, and Neil Immerman, an expert in the field, had pointed out two possibly fatal errors in the proof. As of September 15, 2010, Deolalikar was reported to be working on a detailed expansion of his attempted proof. However, the general consensus amongst theoretical computer scientists is now that the attempted proof is not correct, nor even a significant advancement in our understanding of the problem.

Logical characterizations

The P = NP problem can be restated in terms of expressible certain classes of logical statements, as a result of work in descriptive complexity. All languages (of finite structures with a fixed signature including a linear order relation) in P can be expressed in first-order logic with the addition of a suitable least fixed point combinator (effectively, this, in combination with the order, allows the definition of recursive functions); indeed, (as long as the signature contains at least one predicate or function in addition to the distinguished order relation [so that the amount of space taken to store such finite structures is actually polynomial in the number of elements in the structure]), this precisely characterizes P. Similarly, NP is the set of languages expressible in existential second-order logic—that is, second-order logic restricted to exclude universal quantification over relations, functions, and subsets. The languages in the polynomial hierarchy, PH, correspond to all of second-order logic. Thus, the question "is P a proper subset of NP" can be reformulated as "is existential second-order logic able to describe languages (of finite linearly ordered structures with nontrivial signature) that first-order logic with least fixed point cannot?". The word "existential" can even be dropped from the previous characterization, since P = NP if and only if P = PH (as the former would establish that NP = co-NP, which in turn implies that NP = PH). PSPACE = NPSPACE as established Savitch's theorem, this follows directly from the fact that the square of a polynomial function is still a polynomial function. However, it is believed, but not proven, that a similar relationship may not exist between the polynomial time complexity classes P and NP, so the question is still open.

Polynomial-time algorithms

No algorithm for any NP-complete problem is known to run in polynomial time. However, there are algorithms for NP-complete problems with the property that if P = NP, then the algorithm runs in polynomial time (although with enormous constants, making the algorithm impractical). The following algorithm, due to Levin, is such an example. It correctly accepts the NP-complete language SUBSET-SUM, and runs in polynomial time if and only if P = NP:

// Algorithm that accepts the NP-complete language SUBSET-SUM. // // This is a polynomial-time algorithm if and only if P=NP. // // "Polynomial-time" means it returns "yes" in polynomial time when // the answer should be "yes", and runs forever when it is "no". // // Input: S = a finite set of integers // Output: "yes" if any subset of S adds up to 0. // Runs forever with no output otherwise. // Note: "Program number P" is the program obtained by // writing the integer P in binary, then // considering that string of bits to be a // program. Every possible program can be // generated this way, though most do nothing // because of syntax errors. FOR N = 1...infinity FOR P = 1...N Run program number P for N steps with input S IF the program outputs a list of distinct integers AND the integers are all in S AND the integers sum to 0 THEN OUTPUT "yes" and HALT

If, and only if, P = NP, then this is a polynomial-time algorithm accepting an NP-complete language. "Accepting" means it gives "yes" answers in polynomial time, but is allowed to run forever when the answer is "no".

This algorithm is enormously impractical, even if P = NP. If the shortest program that can solve SUBSET-SUM in polynomial time is b bits long, the above algorithm will try at least 2^b−1 other programs first.

Formal definitions for P and NP

Conceptually a decision problem is a problem that takes as input some string w over an alphabet $\backslash Sigma$, and outputs "yes" or "no". If there is an algorithm (say a Turing machine, or a computer program with unbounded memory) that can produce the correct answer for any input string of length n in at most $c\; \backslash cdot\; n^k\; +\; c$ steps, where k and c are constants independent of the input string, then we say that the problem can be solved in polynomial time and we place it in the class P. Formally, P is defined as the set of all languages that can be decided by a deterministic polynomial-time Turing machine. That is,

P = $\backslash \{\; L\; :\; L=L(M)\; \backslash text\{\; for\; some\; deterministic\; polynomial-time\; Turing\; machine\; \}\; M\; \backslash \}$

where $L(M)\; =\; \backslash \{\; w\backslash in\backslash Sigma^\{*\}:\; M\; \backslash text\{\; accepts\; \}\; w\; \backslash \}$

and a deterministic polynomial-time Turing machine is a deterministic Turing machine M that satisfies the following two conditions:

# $M\; \backslash text\{\; halts\; on\; all\; input\; \}\; w$; and # there exists $k\; \backslash in\; N$ such that $T\_M(n)\backslash in\; O(n^\{k\})$ (where O refers to the big O notation), ::where $T\_M(n)\; =\; \backslash max\backslash \{\; t\_M(w)\; :\; w\backslash in\backslash Sigma^\{*\},\; \backslash left|w\backslash right|\; =\; n\; \backslash \}$ ::and $t\_M(w)\; =\; \backslash text\{\; number\; of\; steps\; \}M\backslash text\{\; takes\; to\; halt\; on\; input\; \}w.$

NP can be defined similarly using nondeterministic Turing machines (the traditional way). However, a modern approach to define NP is to use the concept of certificate and verifier. Formally, NP is defined as the set of languages over a finite alphabet that have a verifier that runs in polynomial time, where the notion of "verifier" is defined as follows.

Let L be a language over a finite alphabet, $\backslash Sigma$.

$L\backslash in\backslash mathbf\{NP\}$ if, and only if, there exists a binary relation $R\backslash subset\backslash Sigma^\{*\}\backslash times\backslash Sigma^\{*\}$ and a positive integer k such that the following two conditions are satisfied:

# For all $x\backslash in\backslash Sigma^\{*\}$, $x\backslash in\; L\; \backslash Leftrightarrow\backslash exists\; y\backslash in\backslash Sigma^\{*\}$ such that $(x,y)\backslash in\; R\backslash ;$ and $\backslash left|y\backslash right|\backslash in\; O(\backslash left|x\backslash right|^\{k\})$; and # the language $L\_\{R\}\; =\; \backslash \{\; x\backslash \#\; y:(x,y)\backslash in\; R\backslash \}$ over $\backslash Sigma\backslash cup\backslash \{\backslash \#\backslash \}$ is decidable by a Turing machine in polynomial time.

A Turing machine that decides $L\_\{R\}$ is called a verifier for L and a y such that $(x,y)\backslash in\; R$ is called a certificate of membership of x in L.

In general, a verifier does not have to be polynomial-time. However, for L to be in NP, there must be a verifier that runs in polynomial time.

Example

Let $\backslash mathit\{COMPOSITE\}\; =\; \backslash \{x\backslash in\backslash mathbb\; N:x=pq\; \backslash ;\backslash text\{for\; integers\}\backslash ;\; p,\; q\; >\; 1\; \backslash \}$ and

Clearly, the question of whether a given x is a composite is equivalent to the question of whether x is a member of $\backslash mathit\{COMPOSITE\}$. It can be shown that $\backslash mathit\{COMPOSITE\}\backslash in\backslash mathbf\{NP\}$ by verifying that $\backslash mathit\{COMPOSITE\}$ satisfies the above definition (if we identify natural numbers with their binary representations).

$\backslash mathit\{COMPOSITE\}$ also happens to be in P.

Formal definition for NP-completeness

There are many equivalent ways of describing NP-completeness.

Let $\backslash \; L$ be a language over a finite alphabet $\backslash \; \backslash Sigma$.

$\backslash \; L$ is NP-complete if, and only if, the following two conditions are satisfied:

# $L\backslash in\backslash mathbf\{NP\}$; and # any $L^\{\text{'}\}\backslash in\backslash mathbf\{NP\}$ is polynomial-time-reducible to $\backslash \; L$ (written as $L^\{\text{'}\}\backslash leq\_\{p\}\; L$), where $L^\{\text{'}\}\backslash leq\_\{p\}\; L$ if, and only if, the following two conditions are satisfied: ## There exists $f\; :\; \backslash Sigma^\{*\}\backslash rightarrow\backslash Sigma^\{*\}$ such that $\backslash forall\; w\backslash in\backslash Sigma^\{*\}(w\backslash in\; L^\{\text{'}\}\backslash Leftrightarrow\; f(w)\backslash in\; L)$; and ## there exists a polynomial-time Turing machine that halts with $\backslash \; f(w)$ on its tape on any input $\backslash \; w$.

See also

Game complexity

Unsolved problems in computer science

Unsolved problems in mathematics

Notes

Further reading

External links

The Clay Mathematics Institute Millennium Prize Problems

Ian Stewart on Minesweeper as NP-complete at The Clay Math Institute

Gerhard J. Woeginger. The P-versus-NP page. A list of links to a number of purported solutions to the problem. Some of these links state that P equals NP, some of them state the opposite. It is probable that all these alleged solutions are incorrect.

Computational Complexity of Games and Puzzles

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Scott Aaronson 's Shtetl Optimized blog: Reasons to believe, a list of justifications for the belief that P ≠ NP

Category:Structural complexity theory Category:Mathematical optimization Category:Conjectures Category:Unsolved problems in mathematics Category:Unsolved problems in computer science Category:Millennium Prize Problems