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Review article
# The Status of the P Versus NP Problem

When editor-in-chief Moshe Vardi asked me to write this piece
for *Communications*, my first reaction was the article
could be written in two words:

Still open.

When I started graduate school in the mid-1980s, many believed
that the quickly developing area of circuit complexity would soon
settle the **P** versus **NP** problem, whether every
algorithmic problem with efficiently verifiable solutions have
efficiently computable solutions. But circuit complexity and
other approaches to the problem have stalled and we have little
reason to believe we will see a proof separating **P** from
**NP** in the near future.

Nevertheless, the computer science landscape has dramatically
changed in the nearly four decades since Steve Cook presented his
seminal **NP**-completeness paper "The Complexity of
Theorem-Proving Procedures"^{10} in
Shaker Heights, OH in early May, 1971. Computational power has
dramatically increased, the cost of computing has dramatically
decreased, not to mention the power of the Internet. Computation
has become a standard tool in just about every academic field.
Whole subfields of biology, chemistry, physics, economics and
others are devoted to large-scale computational modeling,
simulations, and problem solving.

As we solve larger and more complex problems with greater
computational power and cleverer algorithms, the problems we
cannot tackle begin to stand out. The theory of
**NP**-completeness helps us understand these limitations and
the **P** versus **NP** problem begins to loom large not
just as an interesting theoretical question in computer science,
but as a basic principle that permeates all the sciences.

So while we don't expect the **P** versus **NP** problem
to be resolved in the near future, the question has driven
research in a number of topics to help us understand, handle, and
even take advantage of the hardness of various computational
problems.

In this article I look at how people have tried to solve the
**P** versus **NP** problem as well as how this question
has shaped so much of the research in computer science and
beyond. I will look at how to handle **NP**-complete problems
and the theory that has developed from those approaches. I show
how a new type of "interactive proof systems" led to limitations
of approximation algorithms and consider whether quantum
computing can solve **NP**-complete problems (short answer:
not likely). And I close by describing a new long-term project
that will try to separate **P** from **NP** using
algebraic-geometric techniques.

This article does not try to be totally accurate or complete
either technically or historically, but rather informally
describes the **P** versus **NP** problem and the major
directions in computer science inspired by this question over the
past several decades.

Suppose we have a large group of students that we need to pair up to work on projects. We know which students are compatible with each other and we want to put them in compatible groups of two. We could search all possible pairings but even for 40 students we would have more than 300 billion trillion possible pairings.

In 1965, Jack Edmonds^{12} gave an
efficient algorithm to solve this matching problem and suggested
a formal definition of "efficient computation" (runs in time a
fixed polynomial of the input size). The class of problems with
efficient solutions would later become known as **P** for
"Polynomial Time."

But many related problems do not seem to have such an efficient algorithm. What if we wanted to make groups of three students with each pair of students in each group compatible (Partition into Triangles)? What if we wanted to find a large group of students all of whom are compatible with each other (Clique)? What if we wanted to sit students around a large round table with no incompatible students sitting next to each other (Hamiltonian Cycle)? What if we put the students into three groups so that each student is in the same group with only his or her compatibles (3-Coloring)?

All these problems have a similar favor: Given a potential
solution, for example, a seating chart for the round table, we
can validate that solution efficiently. The collection of
problems that have efficiently verifiable solutions is known as
**NP** (for "Nondeterministic Polynomial-Time," if you have to
ask).

So **P** = **NP** means that for every problem that has
an efficiently verifiable solution, we can find that solution
efficiently as well.

We call the very hardest **NP** problems (which include
Partition into Triangles, Clique, Hamiltonian Cycle and
3-Coloring) "NP-complete," that is, given an efficient algorithm
for one of them, we can find an efficient algorithm for all of
them and in fact any problem in **NP**. Steve Cook, Leonid
Levin, and Richard Karp^{10,
24, 27} developed the
initial theory of **NP**-completeness that generated multiple
ACM Turing Awards.

In the 1970s, theoretical computer scientists showed hundreds
more problems **NP**-complete (see Garey and
Johnson^{16}). An efficient solution to
any **NP**-complete problem would imply **P** = **NP**
and an efficient solution to every **NP**-complete
problem.

Most computer scientists quickly came to believe **P** ≠
**NP** and trying to prove it quickly became the single most
important question in all of theoretical computer science and one
of the most important in all of mathematics. Soon the **P**
versus **NP** problem became an important computational issue
in nearly every scientific discipline.

As computers grew cheaper and more powerful, computation
started playing a major role in nearly every academic field,
especially the sciences. The more scientists can do with
computers, the more they realize some problems seem
computationally difficult. Many of these fundamental problems
turn out to be **NP**-complete. A small sample:

- Finding a DNA sequence that best fits a collection of
fragments of the sequence (see
Gusfield
^{20}). - Finding a ground state in the Ising model of phase
transitions (see Cipra
^{8}). - Finding Nash Equilbriums with specific properties in a number
of environments (see Conitzer
^{9}). - Finding optimal protein threading
procedures.
^{26} - Determining if a mathematical statement has a short proof
(follows from Cook
^{10}).

In 2000, the Clay Math Institute named the **P** versus
**NP** problem as one of the seven most important open
questions in mathematics and has offered a million-dollar prize
for a proof that determines whether or not **P** =
**NP**.

To understand the importance of the **P** versus **NP**
problem let us imagine a world where **P** = **NP**.
Technically we could have **P** = **NP**, but not have
practical algorithms for most **NP**-complete problems. But
suppose in fact we do have very quick algorithms for all these
problems.

Many focus on the negative, that if **P** = **NP** then
public-key cryptography becomes impossible. True, but what we
will gain from **P** = **NP** will make the whole Internet
look like a footnote in history.

What we would gain from P = NP will make the whole Internet look like a footnote in history.

Since all the **NP**-complete optimization problems become
easy, everything will be much more efficient. Transportation of
all forms will be scheduled optimally to move people and goods
around quicker and cheaper. Manufacturers can improve their
production to increase speed and create less waste. And I'm just
scratching the surface.

Learning becomes easy by using the principle of Occam's razor—we simply find the smallest program consistent with the data. Near perfect vision recognition, language comprehension and translation and all other learning tasks become trivial. We will also have much better predictions of weather and earthquakes and other natural phenomenon.

**P** = **NP** would also have big implications in
mathematics. One could find short, fully logical proofs for
theorems but these proofs are usually extremely long. But we can
use the Occam razor principle to recognize and verify
mathematical proofs as typically written in journals. We can then
find proofs of theorems that have reasonable length proofs say in
under 100 pages. A person who proves **P** = **NP** would
walk home from the Clay Institute not with $1 million check but
with seven (actually six since the Poincaré Conjecture
appears solved).

Don't get your hopes up. Complexity theorists generally
believe **P** ≠ **NP** and such a beautiful world cannot
exist.

Here, I present a number of ways we have tried and failed to
prove **P** ≠ **NP**. The survey of Fortnow and
Homer^{14} gives a fuller historical
overview of these techniques.

*Diagonalization*. Can we just construct an **NP**
language *L* specifically designed so that every single
polynomial-time algorithm fails to compute *L* properly on
some input? This approach, known as diagonalization, goes back to
the 19th century.

In 1874, Georg Cantor^{7} showed the
real numbers are uncountable using a technique known as
diagonalization. Given a countable list of reals, Cantor showed
how to create a new real number not on that list.

Alan Turing, in his seminal paper on
computation,^{38} used a similar
technique to show that the Halting problem is not computable. In
the 1960s complexity theorists used diagonalization to show that
given more time or memory one can solve more problems. Why not
use diagonalization to separate **NP** from **P**?

Diagonalization requires simulation and we don't know how a
fixed **NP** machine can simulate an arbitrary **P**
machine. Also a diagonalization proof would likely relativize,
that is, work even if all machines involved have access to the
same additional information. Baker, Gill and
Solovay^{6} showed no relativizable proof
can settle the **P** versus **NP** problem in either
direction.

Complexity theorists have used diagonalization techniques to
show some **NP**-complete problems like Boolean formula
satisfiability cannot have algorithms that use both a small
amount of time and memory,^{39} but this
is a long way from **P** ≠ **NP**.

*Circuit Complexity*. To show **P** ≠ **NP** it
is sufficient to show some -complete problem cannot be solved by
relatively small circuits of AND, OR, and NOT gates (the number
of gates bounded by a fixed polynomial in the input size).

In 1984, Furst, Saxe, and Sipser^{15}
showed that small circuits cannot solve the parity function if
the circuits have a fixed number of layers of gates. In 1985,
Razborov^{31} showed the
**NP**-complete problem of finding a large clique does not
have small circuits if one only allows AND and OR gates (no NOT
gates). If one extends Razborov's result to general circuits one
will have proved **P** ≠ **NP**.

Razborov later showed his techniques would fail miserably if
one allows NOT gates.^{32} Razborov and
Rudich^{33} develop a notion of
"natural" proofs and give evidence that our limited techniques in
circuit complexity cannot be pushed much further. And, in fact,
we haven't seen any significantly new circuit lower bounds in the
past 20 years.

*Proof Complexity*. Consider the set of Tautologies, the
Boolean formulas θ of variables over ANDs, ORs, and NOTs
such that every setting of the variables to True and False makes
θ true, for example the formula

A literal is a variable or its negation, such as *x* or
NOT *x*. A formula, like the one here, is in Disjunctive
Normal Form (DNF) if it is the OR of ANDs of one or more
literals.

If a formula θ is not a tautology, we can give an easy
proof of that fact by exhibiting an assignment of the variables
that makes θ false. But if θ were indeed a tautology,
we don't expect short proofs. If one could prove there are no
short proofs of tautology that would imply **P** ≠
**NP**.

Resolution is a standard approach to proving tautologies of
DNFs by finding two clauses of the form (ψ_{1} AND
*x*) and (ψ_{2} AND NOT *x*) and adding the
clause (ψ_{1} AND ψ_{2}). A formula is a
tautology exactly when one can produce an empty clause in this
manner.

In 1985, Wolfgang Haken^{21} showed
that tautologies that encode the pigeonhole principle (*n* +
1 pigeons in *n* holes means some hole has more than one
pigeon) do not have short resolution proofs.

Since then complexity theorists have shown similar weaknesses in a number of other proof systems including cutting planes, algebraic proof systems based on polynomials, and restricted versions of proofs using the Frege axioms, the basic axioms one learns in an introductory logic course.

But to prove **P** ≠ **NP** we would need to show
that tautologies cannot have short proofs in an arbitrary proof
system. Even a breakthrough result showing tautologies don't have
short general Frege proofs would not suffice in separating
**NP** from **P**.

So you have an **NP**-complete problem you just have to
solve. If, as we believe, **P** ≠ **NP** you won't find
a general algorithm that will correctly and accurately solve your
problem all the time. But sometimes you need to solve the problem
anyway. All hope is not lost. Here, I describe some of the tools
one can use on **NP**-complete problems and how computational
complexity theory studies these approaches. Typically one needs
to combine several of these approaches when tackling
**NP**-complete problems in the real world.

*Brute Force*. Computers have gotten faster, much faster
since **NP**-completeness was first developed. Brute force
search through all possibilities is now possible for some small
problem instances. With some clever algorithms we can even solve
some moderate size problems with ease.

The **NP**-complete traveling salesperson problem asks for
the smallest distance tour through a set of specified cities.
Using extensions of the cutting-plane method we can now solve, in
practice, traveling salespeople problems with more than 10,000
cities (see Applegate^{3}).

Consider the 3SAT problem, solving Boolean formula
satisfiability where formulas are in the form of the AND of
several clauses where each clause is the OR of three literal
variables or negations of variables). 3SAT remains
**NP**-complete but the best algorithms can in practice solve
SAT problems on about 100 variables. We have similar results for
other variations of satisfiability and many other
**NP**-complete problems.

But for satisfiability on general formulae and on many other
**NP**-complete problems we do not know algorithms better than
essentially searching all the possibilities. In addition, all
these algorithms have exponential growth in their running times,
so even a small increase in the problem size can kill what was an
efficient algorithm. Brute force alone will not solve
**NP**-complete problems no matter how clever we are.

*Parameterized Complexity*. Consider the Vertex Cover
problem, find a set of *k* "central people" such that for
every compatible pair of people, at least one of them is central.
For small *k* we can determine whether a central set of
people exists efficiently no matter the total number *n* of
people we are considering. For the Clique problem even for small
*k* the problem can still be difficult.

Downey and Fellows^{11} developed a
theory of parameterized complexity that gives a fine-grained
analysis of the complexity of **NP**-complete problems based
on their parameter size.

*Approximation*. We cannot hope to solve
**NP**-complete optimization problems exactly but often we can
get a good approximate answer. Consider the traveling salesperson
problem again with distances between cities given as the crow
flies (Euclidean distance). This problem remains
**NP**-complete but Arora^{4} gives an
efficient algorithm that gets very close to the best possible
route.

Consider the MAX-CUT problem of dividing people into two
groups to maximize the number of incompatibles between the
groups. Goemans and Williamson^{17} use
semi-definite programming to give a division of people only a
.878567 factor of the best possible.

*Heuristics and Average-Case Complexity*. The study of
**NP**-completeness focuses on how algorithms perform on the
worst possible inputs. However the specific problems that arise
in practice may be much easier to solve. Many computer scientists
employ various heuristics to solve **NP**-complete problems
that arise from the specific problems in their fields.

While we create heuristics for many of the **NP**-complete
problems, Boolean formula Satisfiability (SAT) receives more
attention than any other. Boolean formulas, especially those in
conjunctive normal form (CNF), the AND of ORs of variables and
their negations, have a very simple description and yet are
general enough to apply to a large number of practical scenarios
particularly in software verification and artificial
intelligence. Most natural **NP**-complete problems have
simple efficient reductions to the satisfiability of Boolean
formulas. In competition these SAT solvers can often settle
satisfiability of formulas of one million
variables.^{a}

Computational complexity theorists study heuristics by considering average-case complexity—how well can algorithms perform on average from instances generated by some specific distribution.

Leonid Levin^{28} developed a theory
of efficient algorithms over a specific distribution and
formulated a distributional version of the **P** versus
**NP** problem.

Some problems, like versions of the shortest vector problem in
a lattice or computing the permanent of a matrix, are hard on
average exactly when they are hard on worst-case inputs, but
neither of these problems is believed to be **NP**-complete.
Whether similar worst-to-average reductions hold for
**NP**-complete sets is an important open problem.

Average-case complexity plays an important role in many areas of computer science, particularly cryptography, as discussed later.

Previously, we saw how sometimes one can get good approximate
solutions to **NP**-complete optimization problems. Many times
though we seem to hit a limit on our ability to even get good
approximations. We now know that we cannot achieve better
approximations on many of these problems unless **P** =
**NP** and we could solve these problems exactly. The
techniques to show these negative results came out of a new model
of proof system originally developed for cryptography and to
classify group theoretic algorithmic problems.

As mentioned earlier, we don't expect to have short traditional proofs of tautologies. But consider an "interactive proof" model where a prover Peggy tries to convince a verifier Victor that a formula θ is a tautology. Victor can ask Peggy randomly generated questions and need only be convinced with high confidence. Quite surprisingly, these proof systems have been shown to exist not only for tautologies but for any problem computable in a reasonable amount of memory.

A variation known as a "probabilistically checkable proof
system" (PCPs), where Peggy writes down an encoded proof and
Victor can make randomized queries to the bits of the proof, has
applications for approximations. The "PCP Theorem" optimizes
parameters, which in its strong form shows that every language in
**NP** has a PCP where Victor uses a tiny number of random
coins and queries only three bits of the proof.

One can use this PCP theorem to show the limitations of
approximation for a large number of optimization questions. For
example, one cannot approximate the largest clique in a group of
*n* people by more than a multiplicative ratio of nearly
unless **P** = **NP**. See Madhu Sudan's
recent article in *Communications* for more details and
references on PCPs.^{36}

One can do even better assuming a "Unique Games Conjecture"
that there exists PCPs for **NP** problems with some stronger
properties. Consider the MAX-CUT problem of dividing people
discussed earlier. If the unique games conjecture holds one
cannot do better than the .878567 factor given by the
Goemans-Williamson approximation
algorithm.^{26} Recent work shows how to
get a provably best approximation for essentially any constrained
problem assuming this
conjecture.^{30}

We expect P ≠ NP to hold in very strong ways. We can use strong hardness assumptions as a positive tool, particularly to create cryptographic protocols and to reduce or even eliminate the need of random bits in probabilistic algorithms.

In "What If **P** = **NP**?" we saw the nice world that
arises when we assume **P** = **NP**. But we expect
**P** ≠ **NP** to hold in very strong ways. We can use
strong hardness assumptions as a positive tool, particularly to
create cryptographic protocols and to reduce or even eliminate
the need of random bits in probabilistic algorithms.

*Cryptography*. We take it for granted these days, the
little key or lock on our Web page that tells us that someone
listening to the network won't get the credit card number I just
sent to an online store or the password to the bank that controls
my money. But public-key cryptography, the ability to send secure
messages between two parties that have never privately exchanged
keys, is a relatively new development based on hardness
assumptions of computational problems.

If **P** = **NP** then public-key cryptography is
impossible. Assuming **P** ≠ **NP** is not enough to get
public-key protocols, instead we need strong average-case
assumptions about the difficulty of factoring or related
problems.

We can do much more than just public-key cryptography using
hard problems. Suppose Alice's husband Bob is working on a Sudoku
puzzle and Alice claims she has a solution to the puzzle (solving
a *n* × *n* Sudoku puzzle is **NP**-complete).
Can Alice convince Bob that she knows a solution without
revealing any piece of it?

Alice can use a "zero-knowledge proof," an interactive proof
with the additional feature that the verifier learns nothing
other than some property holds, like a Sudoku puzzle having a
solution. Every **NP** search problem has a zero-knowledge
proof under the appropriate hardness assumptions.

Online poker is generally played through some "trusted" Web
site, usually somewhere in the Caribbean. Can we play poker over
the Internet without a trusted server? Using the right
cryptographic assumptions, not only poker but any protocol that
uses a trusted party can be replaced by one that uses no trusted
party and the players can't cheat or learn anything new beyond
what they could do with the trusted
party.^{b}

*Eliminating Randomness*. In the 1970s we saw a new type
of algorithm, one that used random bits to aid in finding a
solution to a problem. Most notably we had probabilistic
algorithms^{35} for determining whether
a number is prime, an important routine needed for modern
cryptography. In 2004, we discovered we don't need randomness at
all to efficiently determine if a number is
prime.^{2} Does randomness help us at all
in finding solutions to **NP** problems?

Truly independent and uniform random bits are either very difficult or impossible to produce (depending on your beliefs about quantum mechanics). Computer algorithms instead use pseudorandom generators to generate a sequence of bits from some given seed. The generators typically found on our computers usually work well but occasionally give incorrect results both in theory and in practice.

We can create theoretically better pseudorandom generators in two different ways, one based on the strong hardness assumptions of cryptography and the other based on worst-case complexity assumptions. I will focus on this second approach.

We need to assume a bit more than **P** ≠ **NP**,
roughly that **NP**-complete problems cannot be solved by
smaller than expected AND-OR-NOT circuits. A long series of
papers showed that, under this assumption, any problem with an
efficient probabilistic algorithm also has an efficient algorithm
that uses a pseudorandom generator with a very short seed, a
surprising connection between hard languages and
pseudo-randomness (see
Impagliazzo^{23}). The seed is so short
we can try all possible seeds efficiently and avoid the need for
randomness altogether.

Thus complexity theorists generally believe having randomness
does not help in solving **NP** search problems and that
**NP**-complete problems do not have efficient solutions,
either with or without using truly random bits.

While randomness doesn't seem necessary for solving search problems, the unpredictability of random bits plays a critical role in cryptography and interactive proof systems and likely cannot be avoided in these scenarios.

While we have randomized and nonrandomized efficient algorithms for determining whether a number is prime, these algorithms usually don't give us the factors of a composite number. Much of modern cryptography relies on the fact that factoring or similar problems do not have efficient algorithms.

In the mid-1990s, Peter Shor^{34}
showed how to factor numbers using a hypothetical quantum
computer. He also developed a similar quantum algorithm to solve
the discrete logarithm problem. The hardness of discrete
logarithm on classical computers is also used as a basis for many
cryptographic protocols. Nevertheless, we don't expect that
factoring or finding discrete logarithms are **NP**-complete.
While we don't think we have efficient algorithms to solve
factoring or discrete logarithm, we also don't believe we can
reduce **NP**-complete problems like Clique to the factoring
or discrete logarithm problems.

So could quantum computers one day solve **NP**-complete
problems? Unlikely.

I'm not a physicist so I won't address the problem as to whether these machines can actually be built at a large enough scale to solve factoring problems larger than we can with current technology (about 200 digits). After billions of dollars of funding of quantum computing research we still have a long way to go.

Even if we could build these machines, Shor's algorithm relies
heavily on the algebraic structures of numbers that we don't see
in the known **NP**-complete problems. We know that his
algorithm cannot be applied to generic "black-box" search
problems so any algorithm would have to use some special
structure of **NP**-complete problems that we don't know
about. We have used some algebraic structure of
**NP**-complete problems for interactive and zero-knowledge
proofs but quantum algorithms would seem to require much
more.

Lov Grover^{19} did find a quantum
algorithm that works on general **NP** problems but that
algorithm only achieves a quadratic speed-up and we have evidence
that those techniques will not go further.

Meanwhile quantum cryptography, using quantum mechanics to achieve some cryptographic protocols without hardness assumptions, has had some success both in theory and in practice.

Ketan Mulmuley and Milind Sohoni have presented an approach to
the **P** versus **NP** problem through algebraic geometry,
dubbed Geometric Complexity Theory, or
GCT.^{29}

This approach seems to avoid the difficulties mentioned earlier, but requires deep mathematics that could require many years or decades to carry through.

In essence, they define a family of high-dimension polygons
*P*_{n} based on group representations on
certain algebraic varieties. Roughly speaking, for each *n*,
if *P*_{n} contains an integral point, then
any circuit family for the Hamiltonian path problem must have
size at least *n*^{log n} on inputs of size
*n*, which implies **P** ≠ **NP**. Thus, to show
that **P** ≠ **NP** it suffices to show that
*P*_{n} contains an integral point for all
*n*.

Although all that is necessary is to show that
*P*_{n} contains an integral point for all
*n*, Mulmuley and Sohoni argue that this direct approach
would be difficult and instead suggest first showing that the
integer programming problem for the family
*P*_{n} is, in fact, in **P**. Under this
approach, there are three significant steps remaining:

- Prove that the LP relaxation solves the integer programming
problem for
*P*_{n}in polynomial time; - Find an efficient, simple combinatorial algorithm for the
integer programming problem for
*P*_{n}, and; - Prove that this simple algorithm always answers "yes."

Since the polygons *P*_{n} are
algebro-geometric in nature, solving (1) is thought to require
algebraic geometry, representation theory, and the theory of
quantum groups. Mulmuley and Sohoni have given reasonable
algebro-geometric conditions that imply (1). These conditions
have classical analogues that are known to hold, based on the
Riemann Hypothesis over finite fields (a theorem proved by
André Weil in the 1960s). Mulmuley and Sohoni suggest that
an analogous Riemann Hypothesis-like statement is required here
(though not the classical Riemann Hypothesis).

Although step (1) is difficult, Mulmuley and Sohoni have
provided definite conjectures based on reasonable mathematical
analogies that would solve (1). In contrast, the path to
completing steps (2) and (3) is less clear. Despite these
remaining hurdles, even solving the conjectures involved in (1)
could provide some insight to the **P** versus **NP**
problem.

Mulmuley and Sohoni have reduced a question about the
nonexistence of polynomial-time algorithms for all
**NP**-complete problems to a question about the existence of
a polynomial-time algorithm (with certain properties) for a
specific problem. This should give us some hope, even in the face
of problems (1)–(3).

Nevertheless, Mulmuley believes it will take about 100 years to carry out this program, if it works at all.

This survey focused on the **P** versus **NP** problem,
its importance, our attempts to prove **P** ≠ **NP** and
the approaches we use to deal with the **NP**-complete
problems that nature and society throws at us. Much of the work
mentioned required a long series of mathematically difficult
research papers that I could not hope to adequately cover in this
short article. Also the field of computational complexity goes
well beyond just the **P** versus **NP** problem that I
haven't discussed here. In "Further Reading," a number of
references are presented for those interested in a deeper
understanding of the **P** versus **NP** problem and
computational complexity.

The **P** versus **NP** problem has gone from an
interesting problem related to logic to perhaps the most
fundamental and important mathematical question of our time,
whose importance only grows as computers become more powerful and
widespread. The question has even hit popular culture appearing
in television shows such as *The Simpsons* and
*Numb3rs*. Yet many only know of the basic principles of
**P** versus **NP** and I hope this survey has given you a
small feeling of the depth of research inspired by this
mathematical problem.

Proving **P** ≠ **NP** would not be the end of the
story, it would just show that **NP**-complete problem, don't
have efficient algorithms for all inputs but many questions might
remain. Cryptography, for example, would require that a problem
like factoring (not believed to be **NP**-complete) is hard
for randomly drawn composite numbers.

Proving **P** ≠ **NP** might not be the start of the
story either. Weaker separations remain perplexingly difficult,
for example showing that Boolean-formula Satisfiability cannot be
solved in near-linear time or showing that some problem using a
certain amount of memory cannot be solved using roughly the same
amount of time.

None of us truly understands the **P** versus **NP**
problem, we have only begun to peel the layers around this
increasingly complex question. Perhaps we will see a resolution
of the **P** versus **NP** problem in the near future but I
almost hope not. The **P** versus **NP** problem continues
to inspire and boggle the mind and continued exploration of this
problem will lead us to yet even new complexities in that truly
mysterious process we call computation.

Recommendations for a more in-depth look at the **P**
versus **NP** problem and the other topics discussed in this
article:

- Steve Homer and I have written a detailed historical view of
computational complexity.
^{14} - The 1979 book of Garey and Johnson still gives the best
overview of the
**P**versus**NP**problem with an incredibly useful list of**NP**-complete problems.^{16} - Scott Aaronson looks at the unlikely possibility that the
**P**versus**NP**problem is formally independent.^{1} - Russell Impagliazzo gives a wonderful description of five
possible worlds of complexity.
^{22} - Sanjeev Arora and Boaz Barak have a new computational
complexity textbook with an emphasis on recent research
directions.
^{5} - The
*Foundations and Trends in Theoretical Computer Science*journal and the Computational Complexity columns of the*Bulletin of the European Association of Theoretical Computer Science*and*SIGACT News*have many wonderful surveys on various topics in theory including those mentioned in this article. - Read the blog Computational Complexity and you will be among
the first to know about any updates of the status of the
**P**versus**NP**problem.^{13}

Thanks to Rahul Santhanam for many useful discussions and
comments. Josh Grochow wrote an early draft. The anonymous
referees provided critical advice. Some of the material in this
article has appeared in my earlier surveys and my
blog.^{13}

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a. http://www.satcompetition.org.

b. See the survey of
Goldreich^{18} for details.

DOI: http://doi.acm.org/10.1145/1562164.1562186

Figure. The software written for this illustration makes a stylized version of a network graph that draws connections between elements based on proximity. The graph constantly changes as the elements sort themselves.

Figure. NP can be seen as a graph where every element is connected to every other element. Over these pages a deconstruction of the graph is shown.

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See new book: "The Golden Ticket: P, NP, and the Search for the Impossible" by Lance Fortnow.

http://www.amazon.com/The-Golden-Ticket-Search-Impossible/dp/0691156492/ref=sr_1_1?ie=UTF8&qid=1364540017&sr=8-1&keywords=lance+fortnow

It will be ironic if the proof (or the disproof) of the P != NP conjecture will be carried out by machines and the proof will be checked by humans !

Vinod

P = NP, The Collapse of Hierarchies

https://www.academia.edu/7518078/P_NP_The_Collapse_of_Hierarchies

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