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Source: Complexity class

In computational complexity theory, a complexity class is a set of computational problems "of related resource-based complexity". The two most commonly analyzed resources are time and memory.
In general, a complexity class is defined in terms of a type of computational problem, a model of computation, and a bounded resource like time or memory. In particular, most complexity classes consist of decision problems that are solvable with a Turing machine, and are differentiated by their time or space (memory) requirements. For instance, the class P is the set of decision problems solvable by a deterministic Turing machine in polynomial time. There are, however, many complexity classes defined in terms of other types of problems (e.g. counting problems and function problems) and using other models of computation (e.g. probabilistic Turing machines, interactive proof systems, Boolean circuits, and quantum computers).
The study of the relationships between complexity classes is a major area of research in theoretical computer science. There are often general hierarchies of complexity classes; for example, it is known that a number of fundamental time and space complexity classes relate to each other in the following way: L⊆NL⊆P⊆NP⊆PSPACE⊆EXPTIME⊆NEXPTIME⊆EXPSPACE (where ⊆ denotes the subset relation). However, many relationships are not yet known; for example, one of the most famous open problems in computer science concerns whether P equals NP. The relationships between classes often answer questions about the fundamental nature of computation. The P versus NP problem, for instance, is directly related to questions of whether nondeterminism adds any computational power to computers and whether problems having solutions that can be quickly checked for correctness can also be quickly solved.

Background

Complexity

= Computational problems

complexity

class

complexity

= Computational models

complexity

= Resource bounds

Basic complexity classes

= Basic definitions

= Time complexity classes

class

complexity

class

= Space complexity classes

complexity

). However, there are a meaningful number of problems that can be solved in logarithmic space. The definitions of these classes require a two-tape Turing machine so that it is possible for the machine to store the entire input (it can be shown that in terms of computability the two-tape Turing machine is equivalent to the single-tape Turing machine). In the two-tape Turing machine model, one tape is the input tape, which is read-only. The other is the work tape, which allows both reading and writing and is the tape on which the Turing machine performs computations. The space complexity of the Turing machine is measured as the number of cells that are used on the work tape.
L (sometimes lengthened to LOGSPACE) is then defined as the class of problems solvable in logarithmic space on a deterministic Turing machine and NL (sometimes lengthened to NLOGSPACE) is the class of problems solvable in logarithmic space on a nondeterministic Turing machine. Or more formally,

L

=

D
S
P
A
C
E

(
log
⁡
n
)

{\displaystyle {\mathsf {L}}={\mathsf {DSPACE}}(\log n)}

N
L

=

N
S
P
A
C
E

(
log
⁡
n
)

{\displaystyle {\mathsf {NL}}={\mathsf {NSPACE}}(\log n)}

It is known that

L

⊆

N
L

⊆

P

{\displaystyle {\mathsf {L}}\subseteq {\mathsf {NL}}\subseteq {\mathsf {P}}}

. However, it is not known whether any of these relationships is proper.

PSPACE and NPSPACE

The complexity classes PSPACE and NPSPACE are the space analogues to P and NP. That is, PSPACE is the class of problems solvable in polynomial space by a deterministic Turing machine and NPSPACE is the class of problems solvable in polynomial space by a nondeterministic Turing machine. More formally,

P
S
P
A
C
E

=

⋃

k
∈

N

D
S
P
A
C
E

(

n

k

)

{\displaystyle {\mathsf {PSPACE}}=\bigcup _{k\in \mathbb {N} }{\mathsf {DSPACE}}(n^{k})}

N
P
S
P
A
C
E

=

⋃

k
∈

N

N
S
P
A
C
E

(

n

k

)

{\displaystyle {\mathsf {NPSPACE}}=\bigcup _{k\in \mathbb {N} }{\mathsf {NSPACE}}(n^{k})}

While it is not known whether P=NP, Savitch's theorem famously showed that PSPACE=NPSPACE. It is also known that

P

⊆

P
S
P
A
C
E

{\displaystyle {\mathsf {P}}\subseteq {\mathsf {PSPACE}}}

, which follows intuitively from the fact that, since writing to a cell on a Turing machine's tape is defined as taking one unit of time, a Turing machine operating in polynomial time can only write to polynomially many cells. It is suspected that P is strictly smaller than PSPACE, but this has not been proven.

EXPSPACE and NEXPSPACE

The complexity classes EXPSPACE and NEXPSPACE are the space analogues to EXPTIME and NEXPTIME. That is, EXPSPACE is the class of problems solvable in exponential space by a deterministic Turing machine and NEXPSPACE is the class of problems solvable in exponential space by a nondeterministic Turing machine. Or more formally,

E
X
P
S
P
A
C
E

=

⋃

k
∈

N

D
S
P
A
C
E

(

2

n

k

)

{\displaystyle {\mathsf {EXPSPACE}}=\bigcup _{k\in \mathbb {N} }{\mathsf {DSPACE}}(2^{n^{k}})}

N
E
X
P
S
P
A
C
E

=

⋃

k
∈

N

N
S
P
A
C
E

(

2

n

k

)

{\displaystyle {\mathsf {NEXPSPACE}}=\bigcup _{k\in \mathbb {N} }{\mathsf {NSPACE}}(2^{n^{k}})}

Savitch's theorem showed that EXPSPACE=NEXPSPACE. This class is extremely broad: it is known to be a strict superset of PSPACE, NP, and P, and is believed to be a strict superset of EXPTIME.

Properties of complexity classes

= Closure

=
Complexity classes have a variety of closure properties. For example, decision classes may be closed under negation, disjunction, conjunction, or even under all Boolean operations. Moreover, they might also be closed under a variety of quantification schemes. P, for instance, is closed under all Boolean operations, and under quantification over polynomially sized domains. Closure properties can be helpful in separating classes—one possible route to separating two complexity classes is to find some closure property possessed by one class but not by the other.
Each class X that is not closed under negation has a complement class co-X, which consists of the complements of the languages contained in X (i.e.

co-X

=
{
L

|

L
¯

∈

X

}

{\displaystyle {\textsf {co-X}}=\{L|{\overline {L}}\in {\mathsf {X}}\}}

). co-NP, for instance, is one important complement complexity class, and sits at the center of the unsolved problem over whether co-NP=NP.
Closure properties are one of the key reasons many complexity classes are defined in the way that they are. Take, for example, a problem that can be solved in

O
(
n
)

{\displaystyle O(n)}

time (that is, in linear time) and one that can be solved in, at best,

O
(

n

1000

)

{\displaystyle O(n^{1000})}

time. Both of these problems are in P, yet the runtime of the second grows considerably faster than the runtime of the first as the input size increases. One might ask whether it would be better to define the class of "efficiently solvable" problems using some smaller polynomial bound, like

O
(

n

3

)

{\displaystyle O(n^{3})}

, rather than all polynomials, which allows for such large discrepancies. It turns out, however, that the set of all polynomials is the smallest class of functions containing the linear functions that is also closed under addition, multiplication, and composition (for instance,

O
(

n

3

)
∘
O
(

n

2

)
=
O
(

n

6

)

{\displaystyle O(n^{3})\circ O(n^{2})=O(n^{6})}

, which is a polynomial but

O
(

n

6

)
>
O
(

n

3

)

{\displaystyle O(n^{6})>O(n^{3})}

). Since we would like composing one efficient algorithm with another efficient algorithm to still be considered efficient, the polynomials are the smallest class that ensures composition of "efficient algorithms". (Note that the definition of P is also useful because, empirically, almost all problems in P that are practically useful do in fact have low order polynomial runtimes, and almost all problems outside of P that are practically useful do not have any known algorithms with small exponential runtimes, i.e. with

O
(

c

n

)

{\displaystyle O(c^{n})}

runtimes where c is close to 1.)

= Reductions

=

Many complexity classes are defined using the concept of a reduction. A reduction is a transformation of one problem into another problem, i.e. a reduction takes inputs from one problem and transforms them into inputs of another problem. For instance, you can reduce ordinary base-10 addition

x
+
y

{\displaystyle x+y}

to base-2 addition by transforming

x

{\displaystyle x}

and

y

{\displaystyle y}

to their base-2 notation (e.g. 5+7 becomes 101+111). Formally, a problem

X

{\displaystyle X}

reduces to a problem

Y

{\displaystyle Y}

if there exists a function

f

{\displaystyle f}

such that for every

x
∈

Σ

∗

{\displaystyle x\in \Sigma ^{*}}

,

x
∈
X

{\displaystyle x\in X}

if and only if

f
(
x
)
∈
Y

{\displaystyle f(x)\in Y}

.
Generally, reductions are used to capture the notion of a problem being at least as difficult as another problem. Thus we are generally interested in using a polynomial-time reduction, since any problem

X

{\displaystyle X}

that can be efficiently reduced to another problem

Y

{\displaystyle Y}

is no more difficult than

Y

{\displaystyle Y}

. Formally, a problem

X

{\displaystyle X}

is polynomial-time reducible to a problem

Y

{\displaystyle Y}

if there exists a polynomial-time computable function

p

{\displaystyle p}

such that for all

x
∈

Σ

∗

{\displaystyle x\in \Sigma ^{*}}

,

x
∈
X

{\displaystyle x\in X}

if and only if

p
(
x
)
∈
Y

{\displaystyle p(x)\in Y}

.
Note that reductions can be defined in many different ways. Common reductions are Cook reductions, Karp reductions and Levin reductions, and can vary based on resource bounds, such as polynomial-time reductions and log-space reductions.

Hardness

Reductions motivate the concept of a problem being hard for a complexity class. A problem

X

{\displaystyle X}

is hard for a class of problems C if every problem in C can be polynomial-time reduced to

X

{\displaystyle X}

. Thus no problem in C is harder than

X

{\displaystyle X}

, since an algorithm for

X

{\displaystyle X}

allows us to solve any problem in C with at most polynomial slowdown. Of particular importance, the set of problems that are hard for NP is called the set of NP-hard problems.

Completeness

If a problem

X

{\displaystyle X}

is hard for C and is also in C, then

X

{\displaystyle X}

is said to be complete for C. This means that

X

{\displaystyle X}

is the hardest problem in C (since there could be many problems that are equally hard, more precisely

X

{\displaystyle X}

is as hard as the hardest problems in C).
Of particular importance is the class of NP-complete problems—the most difficult problems in NP. Because all problems in NP can be polynomial-time reduced to NP-complete problems, finding an NP-complete problem that can be solved in polynomial time would mean that P = NP.

Relationships between complexity classes

= Savitch's theorem

=

Savitch's theorem establishes the relationship between deterministic and nondetermistic space resources. It shows that if a nondeterministic Turing machine can solve a problem using

f
(
n
)

{\displaystyle f(n)}

space, then a deterministic Turing machine can solve the same problem in

f
(
n

)

2

{\displaystyle f(n)^{2}}

space, i.e. in the square of the space. Formally, Savitch's theorem states that for any

f
(
n
)
>
n

{\displaystyle f(n)>n}

,

N
S
P
A
C
E

(

f

(
n
)

)

⊆

D
S
P
A
C
E

(

f

(
n
)

2

)

.

{\displaystyle {\mathsf {NSPACE}}\left(f\left(n\right)\right)\subseteq {\mathsf {DSPACE}}\left(f\left(n\right)^{2}\right).}

Important corollaries of Savitch's theorem are that PSPACE = NPSPACE (since the square of a polynomial is still a polynomial) and EXPSPACE = NEXPSPACE (since the square of an exponential is still an exponential).
These relationships answer fundamental questions about the power of nondeterminism compared to determinism. Specifically, Savitch's theorem shows that any problem that a nondeterministic Turing machine can solve in polynomial space, a deterministic Turing machine can also solve in polynomial space. Similarly, any problem that a nondeterministic Turing machine can solve in exponential space, a deterministic Turing machine can also solve in exponential space.

= Hierarchy theorems

=

By definition of DTIME, it follows that

D
T
I
M
E

(

n

k

1

)

{\displaystyle {\mathsf {DTIME}}(n^{k_{1}})}

is contained in

D
T
I
M
E

(

n

k

2

)

{\displaystyle {\mathsf {DTIME}}(n^{k_{2}})}

if

k

1

≤

k

2

{\displaystyle k_{1}\leq k_{2}}

, since

O
(

n

k

1

)
⊆
O
(

n

k

2

)

{\displaystyle O(n^{k_{1}})\subseteq O(n^{k_{2}})}

if

k

1

≤

k

2

{\displaystyle k_{1}\leq k_{2}}

. However, this definition gives no indication of whether this inclusion is strict. For time and space requirements, the conditions under which the inclusion is strict are given by the time and space hierarchy theorems, respectively. They are called hierarchy theorems because they induce a proper hierarchy on the classes defined by constraining the respective resources. The hierarchy theorems enable one to make quantitative statements about how much more additional time or space is needed in order to increase the number of problems that can be solved.
The time hierarchy theorem states that

D
T
I
M
E

(

f
(
n
)

)

⊊

D
T
I
M
E

(

f
(
n
)
⋅

log

2

⁡
(
f
(
n
)
)

)

{\displaystyle {\mathsf {DTIME}}{\big (}f(n){\big )}\subsetneq {\mathsf {DTIME}}{\big (}f(n)\cdot \log ^{2}(f(n)){\big )}}

.
The space hierarchy theorem states that

D
S
P
A
C
E

(

f
(
n
)

)

⊊

D
S
P
A
C
E

(

f
(
n
)
⋅
log
⁡
(
f
(
n
)
)

)

{\displaystyle {\mathsf {DSPACE}}{\big (}f(n){\big )}\subsetneq {\mathsf {DSPACE}}{\big (}f(n)\cdot \log(f(n)){\big )}}

.
The time and space hierarchy theorems form the basis for most separation results of complexity classes. For instance, the time hierarchy theorem establishes that P is strictly contained in EXPTIME, and the space hierarchy theorem establishes that L is strictly contained in PSPACE.

Other models of computation

While deterministic and non-deterministic Turing machines are the most commonly used models of computation, many complexity classes are defined in terms of other computational models. In particular,

A number of classes are defined using probabilistic Turing machines, including the classes BPP, PP, RP, and ZPP
A number of classes are defined using interactive proof systems, including the classes IP, MA, and AM
A number of classes are defined using Boolean circuits, including the classes P/poly and its subclasses NC and AC
A number of classes are defined using quantum Turing machines, including the classes BQP and QMA
These are explained in greater detail below.

= Randomized computation

=

A number of important complexity classes are defined using the probabilistic Turing machine, a variant of the Turing machine that can toss random coins. These classes help to better describe the complexity of randomized algorithms.
A probabilistic Turing machine is similar to a deterministic Turing machine, except rather than following a single transition function (a set of rules for how to proceed at each step of the computation) it probabilistically selects between multiple transition functions at each step. The standard definition of a probabilistic Turing machine specifies two transition functions, so that the selection of transition function at each step resembles a coin flip. The randomness introduced at each step of the computation introduces the potential for error; that is, strings that the Turing machine is meant to accept may on some occasions be rejected and strings that the Turing machine is meant to reject may on some occasions be accepted. As a result, the complexity classes based on the probabilistic Turing machine are defined in large part around the amount of error that is allowed. Formally, they are defined using an error probability

ϵ

{\displaystyle \epsilon }

. A probabilistic Turing machine

M

{\displaystyle M}

is said to recognize a language

L

{\displaystyle L}

with error probability

ϵ

{\displaystyle \epsilon }

if:

a string

w

{\displaystyle w}

in

L

{\displaystyle L}

implies that

Pr

[
M

accepts

w
]
≥
1
−
ϵ

{\displaystyle {\text{Pr}}[M{\text{ accepts }}w]\geq 1-\epsilon }

a string

w

{\displaystyle w}

not in

L

{\displaystyle L}

implies that

Pr

[
M

rejects

w
]
≥
1
−
ϵ

{\displaystyle {\text{Pr}}[M{\text{ rejects }}w]\geq 1-\epsilon }

Important complexity classes

The fundamental randomized time complexity classes are ZPP, RP, co-RP, BPP, and PP.
The strictest class is ZPP (zero-error probabilistic polynomial time), the class of problems solvable in polynomial time by a probabilistic Turing machine with error probability 0. Intuitively, this is the strictest class of probabilistic problems because it demands no error whatsoever.
A slightly looser class is RP (randomized polynomial time), which maintains no error for strings not in the language but allows bounded error for strings in the language. More formally, a language is in RP if there is a probabilistic polynomial-time Turing machine

M

{\displaystyle M}

such that if a string is not in the language then

M

{\displaystyle M}

always rejects and if a string is in the language then

M

{\displaystyle M}

accepts with a probability at least 1/2. The class co-RP is similarly defined except the roles are flipped: error is not allowed for strings in the language but is allowed for strings not in the language. Taken together, the classes RP and co-RP encompass all of the problems that can be solved by probabilistic Turing machines with one-sided error.
Loosening the error requirements further to allow for two-sided error yields the class BPP (bounded-error probabilistic polynomial time), the class of problems solvable in polynomial time by a probabilistic Turing machine with error probability less than 1/3 (for both strings in the language and not in the language). BPP is the most practically relevant of the probabilistic complexity classes—problems in BPP have efficient randomized algorithms that can be run quickly on real computers. BPP is also at the center of the important unsolved problem in computer science over whether P=BPP, which if true would mean that randomness does not increase the computational power of computers, i.e. any probabilistic Turing machine could be simulated by a deterministic Turing machine with at most polynomial slowdown.
The broadest class of efficiently-solvable probabilistic problems is PP (probabilistic polynomial time), the set of languages solvable by a probabilistic Turing machine in polynomial time with an error probability of less than 1/2 for all strings.
ZPP, RP and co-RP are all subsets of BPP, which in turn is a subset of PP. The reason for this is intuitive: the classes allowing zero error and only one-sided error are all contained within the class that allows two-sided error, and PP simply relaxes the error probability of BPP. ZPP relates to RP and co-RP in the following way:

ZPP

=

RP

∩

co-RP

{\displaystyle {\textsf {ZPP}}={\textsf {RP}}\cap {\textsf {co-RP}}}

. That is, ZPP consists exactly of those problems that are in both RP and co-RP. Intuitively, this follows from the fact that RP and co-RP allow only one-sided error: co-RP does not allow error for strings in the language and RP does not allow error for strings not in the language. Hence, if a problem is in both RP and co-RP, then there must be no error for strings both in and not in the language (i.e. no error whatsoever), which is exactly the definition of ZPP.
Important randomized space complexity classes include BPL, RL, and RLP.

= Interactive proof systems

=

A number of complexity classes are defined using interactive proof systems. Interactive proofs generalize the proofs definition of the complexity class NP and yield insights into cryptography, approximation algorithms, and formal verification.

Interactive proof systems are abstract machines that model computation as the exchange of messages between two parties: a prover

P

{\displaystyle P}

and a verifier

V

{\displaystyle V}

. The parties interact by exchanging messages, and an input string is accepted by the system if the verifier decides to accept the input on the basis of the messages it has received from the prover. The prover

P

{\displaystyle P}

has unlimited computational power while the verifier has bounded computational power (the standard definition of interactive proof systems defines the verifier to be polynomially-time bounded). The prover, however, is untrustworthy (this prevents all languages from being trivially recognized by the proof system by having the computationally unbounded prover solve for whether a string is in a language and then sending a trustworthy "YES" or "NO" to the verifier), so the verifier must conduct an "interrogation" of the prover by "asking it" successive rounds of questions, accepting only if it develops a high degree of confidence that the string is in the language.

Important complexity classes

The class NP is a simple proof system in which the verifier is restricted to being a deterministic polynomial-time Turing machine and the procedure is restricted to one round (that is, the prover sends only a single, full proof—typically referred to as the certificate—to the verifier). Put another way, in the definition of the class NP (the set of decision problems for which the problem instances, when the answer is "YES", have proofs verifiable in polynomial time by a deterministic Turing machine) is a proof system in which the proof is constructed by an unmentioned prover and the deterministic Turing machine is the verifier. For this reason, NP can also be called dIP (deterministic interactive proof), though it is rarely referred to as such.
It turns out that NP captures the full power of interactive proof systems with deterministic (polynomial-time) verifiers because it can be shown that for any proof system with a deterministic verifier it is never necessary to need more than a single round of messaging between the prover and the verifier. Interactive proof systems that provide greater computational power over standard complexity classes thus require probabilistic verifiers, which means that the verifier's questions to the prover are computed using probabilistic algorithms. As noted in the section above on randomized computation, probabilistic algorithms introduce error into the system, so complexity classes based on probabilistic proof systems are defined in terms of an error probability

ϵ

{\displaystyle \epsilon }

.
The most general complexity class arising out of this characterization is the class IP (interactive polynomial time), which is the class of all problems solvable by an interactive proof system

(
P
,
V
)

{\displaystyle (P,V)}

, where

V

{\displaystyle V}

is probabilistic polynomial-time and the proof system satisfies two properties: for a language

L
∈

I
P

{\displaystyle L\in {\mathsf {IP}}}

(Completeness) a string

w

{\displaystyle w}

in

L

{\displaystyle L}

implies

Pr
[
V

accepts

w

after interacting with

P
]
≥

2
3

{\displaystyle \Pr[V{\text{ accepts }}w{\text{ after interacting with }}P]\geq {\tfrac {2}{3}}}

(Soundness) a string

w

{\displaystyle w}

not in

L

{\displaystyle L}

implies

Pr
[
V

accepts

w

after interacting with

P
]
≤

1
3

{\displaystyle \Pr[V{\text{ accepts }}w{\text{ after interacting with }}P]\leq {\tfrac {1}{3}}}

An important feature of IP is that it equals PSPACE. In other words, any problem that can be solved by a polynomial-time interactive proof system can also be solved by a deterministic Turing machine with polynomial space resources, and vice versa.
A modification of the protocol for IP produces another important complexity class: AM (Arthur–Merlin protocol). In the definition of interactive proof systems used by IP, the prover was not able to see the coins utilized by the verifier in its probabilistic computation—it was only able to see the messages that the verifier produced with these coins. For this reason, the coins are called private random coins. The interactive proof system can be constrained so that the coins used by the verifier are public random coins; that is, the prover is able to see the coins. Formally, AM is defined as the class of languages with an interactive proof in which the verifier sends a random string to the prover, the prover responds with a message, and the verifier either accepts or rejects by applying a deterministic polynomial-time function to the message from the prover. AM can be generalized to AM[k], where k is the number of messages exchanged (so in the generalized form the standard AM defined above is AM[2]). However, it is the case that for all

k
≥
2

{\displaystyle k\geq 2}

, AM[k]=AM[2]. It is also the case that

A
M

[
k
]
⊆

I
P

[
k
]

{\displaystyle {\mathsf {AM}}[k]\subseteq {\mathsf {IP}}[k]}

.
Other complexity classes defined using interactive proof systems include MIP (multiprover interactive polynomial time) and QIP (quantum interactive polynomial time).

= Boolean circuits

=

An alternative model of computation to the Turing machine is the Boolean circuit, a simplified model of the digital circuits used in modern computers. Not only does this model provide an intuitive connection between computation in theory and computation in practice, but it is also a natural model for non-uniform computation (computation in which different input sizes within the same problem use different algorithms).
Formally, a Boolean circuit

C

{\displaystyle C}

is a directed acyclic graph in which edges represent wires (which carry the bit values 0 and 1), the input bits are represented by source vertices (vertices with no incoming edges), and all non-source vertices represent logic gates (generally the AND, OR, and NOT gates). One logic gate is designated the output gate, and represents the end of the computation. The input/output behavior of a circuit

C

{\displaystyle C}

with

n

{\displaystyle n}

input variables is represented by the Boolean function

f

C

:
{
0
,
1

}

n

→
{
0
,
1
}

{\displaystyle f_{C}:\{0,1\}^{n}\to \{0,1\}}

; for example, on input bits

x

1

,

x

2

,
.
.
.
,

x

n

{\displaystyle x_{1},x_{2},...,x_{n}}

, the output bit

b

{\displaystyle b}

of the circuit is represented mathematically as

b
=

f

C

(

x

1

,

x

2

,
.
.
.
,

x

n

)

{\displaystyle b=f_{C}(x_{1},x_{2},...,x_{n})}

. The circuit

C

{\displaystyle C}

is said to compute the Boolean function

f

C

{\displaystyle f_{C}}

.
Any particular circuit has a fixed number of input vertices, so it can only act on inputs of that size. Languages (the formal representations of decision problems), however, contain strings of differing lengths, so languages cannot be fully captured by a single circuit (this contrasts with the Turing machine model, in which a language is fully described by a single Turing machine that can act on any input size). A language is thus represented by a circuit family. A circuit family is an infinite list of circuits

(

C

0

,

C

1

,

C

2

,
.
.
.
)

{\displaystyle (C_{0},C_{1},C_{2},...)}

, where

C

n

{\displaystyle C_{n}}

is a circuit with

n

{\displaystyle n}

input variables. A circuit family is said to decide a language

L

{\displaystyle L}

if, for every string

w

{\displaystyle w}

,

w

{\displaystyle w}

is in the language

L

{\displaystyle L}

if and only if

C

n

(
w
)
=
1

{\displaystyle C_{n}(w)=1}

, where

n

{\displaystyle n}

is the length of

w

{\displaystyle w}

. In other words, a string

w

{\displaystyle w}

of size

n

{\displaystyle n}

is in the language represented by the circuit family

(

C

0

,

C

1

,

C

2

,
.
.
.
)

{\displaystyle (C_{0},C_{1},C_{2},...)}

if the circuit

C

n

{\displaystyle C_{n}}

(the circuit with the same number of input vertices as the number of bits in

w

{\displaystyle w}

) evaluates to 1 when

w

{\displaystyle w}

is its input.
While complexity classes defined using Turing machines are described in terms of time complexity, circuit complexity classes are defined in terms of circuit size — the number of vertices in the circuit. The size complexity of a circuit family

(

C

0

,

C

1

,

C

2

,
.
.
.
)

{\displaystyle (C_{0},C_{1},C_{2},...)}

is the function

f
:

N

→

N

{\displaystyle f:\mathbb {N} \to \mathbb {N} }

, where

f
(
n
)

{\displaystyle f(n)}

is the circuit size of

C

n

{\displaystyle C_{n}}

. The familiar function classes follow naturally from this; for example, a polynomial-size circuit family is one such that the function

f

{\displaystyle f}

is a polynomial.

Important complexity classes

The complexity class P/poly is the set of languages that are decidable by polynomial-size circuit families. It turns out that there is a natural connection between circuit complexity and time complexity. Intuitively, a language with small time complexity (that is, requires relatively few sequential operations on a Turing machine), also has a small circuit complexity (that is, requires relatively few Boolean operations). Formally, it can be shown that if a language is in

D
T
I
M
E

(
t
(
n
)
)

{\displaystyle {\mathsf {DTIME}}(t(n))}

, where

t

{\displaystyle t}

is a function

t
:

N

→

N

{\displaystyle t:\mathbb {N} \to \mathbb {N} }

, then it has circuit complexity

O
(

t

2

(
n
)
)

{\displaystyle O(t^{2}(n))}

. It follows directly from this fact that

P

⊂

P/poly

{\displaystyle {\mathsf {\color {Blue}P}}\subset {\textsf {P/poly}}}

. In other words, any problem that can be solved in polynomial time by a deterministic Turing machine can also be solved by a polynomial-size circuit family. It is further the case that the inclusion is proper, i.e.

P

⊊

P/poly

{\displaystyle {\textsf {P}}\subsetneq {\textsf {P/poly}}}

(for example, there are some undecidable problems that are in P/poly).
P/poly has a number of properties that make it highly useful in the study of the relationships between complexity classes. In particular, it is helpful in investigating problems related to P versus NP. For example, if there is any language in NP that is not in P/poly, then

P

≠

N
P

{\displaystyle {\mathsf {P}}\neq {\mathsf {NP}}}

. P/poly is also helpful in investigating properties of the polynomial hierarchy. For example, if NP ⊆ P/poly, then PH collapses to

Σ

2

P

{\displaystyle \Sigma _{2}^{\mathsf {P}}}

. A full description of the relations between P/poly and other complexity classes is available at "Importance of P/poly". P/poly is also helpful in the general study of the properties of Turing machines, as the class can be equivalently defined as the class of languages recognized by a polynomial-time Turing machine with a polynomial-bounded advice function.
Two subclasses of P/poly that have interesting properties in their own right are NC and AC. These classes are defined not only in terms of their circuit size but also in terms of their depth. The depth of a circuit is the length of the longest directed path from an input node to the output node. The class NC is the set of languages that can be solved by circuit families that are restricted not only to having polynomial-size but also to having polylogarithmic depth. The class AC is defined similarly to NC, however gates are allowed to have unbounded fan-in (that is, the AND and OR gates can be applied to more than two bits). NC is a notable class because it can be equivalently defined as the class of languages that have efficient parallel algorithms.

= Quantum computation

=

The classes BQP and QMA, which are of key importance in quantum information science, are defined using quantum Turing machines.

= Counting problems

=

A counting problem asks not only whether a solution exists (as with a decision problem), but asks how many solutions exist. For example, the decision problem

CYCLE

{\displaystyle {\texttt {CYCLE}}}

asks whether a particular graph

G

{\displaystyle G}

has a simple cycle (the answer is a simple yes/no); the corresponding counting problem

#

CYCLE

{\displaystyle \#{\texttt {CYCLE}}}

(pronounced "sharp cycle") asks how many simple cycles

G

{\displaystyle G}

has. The output to a counting problem is thus a number, in contrast to the output for a decision problem, which is a simple yes/no (or accept/reject, 0/1, or other equivalent scheme).
Thus, whereas decision problems are represented mathematically as formal languages, counting problems are represented mathematically as functions: a counting problem is formalized as the function

f
:
{
0
,
1

}

∗

→

N

{\displaystyle f:\{0,1\}^{*}\to \mathbb {N} }

such that for every input

w
∈
{
0
,
1

}

∗

{\displaystyle w\in \{0,1\}^{*}}

,

f
(
w
)

{\displaystyle f(w)}

is the number of solutions. For example, in the

#

CYCLE

{\displaystyle \#{\texttt {CYCLE}}}

problem, the input is a graph

G
∈
{
0
,
1

}

∗

{\displaystyle G\in \{0,1\}^{*}}

(a graph represented as a string of bits) and

f
(
G
)

{\displaystyle f(G)}

is the number of simple cycles in

G

{\displaystyle G}

.
Counting problems arise in a number of fields, including statistical estimation, statistical physics, network design, and economics.

Important complexity classes

#P (pronounced "sharp P") is an important class of counting problems that can be thought of as the counting version of NP. The connection to NP arises from the fact that the number of solutions to a problem equals the number of accepting branches in a nondeterministic Turing machine's computation tree. #P is thus formally defined as follows:

#P is the set of all functions

f
:
{
0
,
1

}

∗

→

N

{\displaystyle f:\{0,1\}^{*}\to \mathbb {N} }

such that there is a polynomial time nondeterministic Turing machine

M

{\displaystyle M}

such that for all

w
∈
{
0
,
1

}

∗

{\displaystyle w\in \{0,1\}^{*}}

,

f
(
w
)

{\displaystyle f(w)}

equals the number of accepting branches in

M

{\displaystyle M}

's computation tree on

w

{\displaystyle w}

.
And just as NP can be defined both in terms of nondeterminism and in terms of a verifier (i.e. as an interactive proof system), so too can #P be equivalently defined in terms of a verifier. Recall that a decision problem is in NP if there exists a polynomial-time checkable certificate to a given problem instance—that is, NP asks whether there exists a proof of membership (a certificate) for the input that can be checked for correctness in polynomial time. The class #P asks how many such certificates exist. In this context, #P is defined as follows:

#P is the set of functions

f
:
{
0
,
1

}

∗

→

N

{\displaystyle f:\{0,1\}^{*}\to \mathbb {N} }

such that there exists a polynomial

p
:

N

→

N

{\displaystyle p:\mathbb {N} \to \mathbb {N} }

and a polynomial-time Turing machine

V

{\displaystyle V}

(the verifier), such that for every

w
∈
{
0
,
1

}

∗

{\displaystyle w\in \{0,1\}^{*}}

,

f
(
w
)
=

|

{

c
∈
{
0
,
1

}

p
(

|

w

|

)

:
V
(
w
,
c
)
=
1

}

|

{\displaystyle f(w)={\Big |}{\big \{}c\in \{0,1\}^{p(|w|)}:V(w,c)=1{\big \}}{\Big |}}

. In other words,

f
(
w
)

{\displaystyle f(w)}

equals the size of the set containing all of the polynomial-size certificates for

w

{\displaystyle w}

.

= Function problems

=

Counting problems are a subset of a broader class of problems called function problems. A function problem is a type of problem in which the values of a function

f
:
A
→
B

{\displaystyle f:A\to B}

are computed. Formally, a function problem

f

{\displaystyle f}

is defined as a relation

R

{\displaystyle R}

over strings of an arbitrary alphabet

Σ

{\displaystyle \Sigma }

:

R
⊆

Σ

∗

×

Σ

∗

{\displaystyle R\subseteq \Sigma ^{*}\times \Sigma ^{*}}

An algorithm solves

f

{\displaystyle f}

if for every input

x

{\displaystyle x}

such that there exists a

y

{\displaystyle y}

satisfying

(
x
,
y
)
∈
R

{\displaystyle (x,y)\in R}

, the algorithm produces one such

y

{\displaystyle y}

. This is just another way of saying that

f

{\displaystyle f}

is a function and the algorithm solves

f
(
x
)

{\displaystyle f(x)}

for all

x
∈

Σ

∗

{\displaystyle x\in \Sigma ^{*}}

.

Important complexity classes

An important function complexity class is FP, the class of efficiently solvable functions. More specifically, FP is the set of function problems that can be solved by a deterministic Turing machine in polynomial time. FP can be thought of as the function problem equivalent of P. Importantly, FP provides some insight into both counting problems and P versus NP. If #P=FP, then the functions that determine the number of certificates for problems in NP are efficiently solvable. And since computing the number of certificates is at least as hard as determining whether a certificate exists, it must follow that if #P=FP then P=NP (it is not known whether this holds in the reverse, i.e. whether P=NP implies #P=FP).
Just as FP is the function problem equivalent of P, FNP is the function problem equivalent of NP. Importantly, FP=FNP if and only if P=NP.

= Promise problems

=

Promise problems are a generalization of decision problems in which the input to a problem is guaranteed ("promised") to be from a particular subset of all possible inputs. Recall that with a decision problem

L
⊆
{
0
,
1

}

∗

{\displaystyle L\subseteq \{0,1\}^{*}}

, an algorithm

M

{\displaystyle M}

for

L

{\displaystyle L}

must act (correctly) on every

w
∈
{
0
,
1

}

∗

{\displaystyle w\in \{0,1\}^{*}}

. A promise problem loosens the input requirement on

M

{\displaystyle M}

by restricting the input to some subset of

{
0
,
1

}

∗

{\displaystyle \{0,1\}^{*}}

.
Specifically, a promise problem is defined as a pair of non-intersecting sets

(

Π

ACCEPT

,

Π

REJECT

)

{\displaystyle (\Pi _{\text{ACCEPT}},\Pi _{\text{REJECT}})}

, where:

Π

ACCEPT

⊆
{
0
,
1

}

∗

{\displaystyle \Pi _{\text{ACCEPT}}\subseteq \{0,1\}^{*}}

is the set of all inputs that are accepted.

Π

REJECT

⊆
{
0
,
1

}

∗

{\displaystyle \Pi _{\text{REJECT}}\subseteq \{0,1\}^{*}}

is the set of all inputs that are rejected.
The input to an algorithm

M

{\displaystyle M}

for a promise problem

(

Π

ACCEPT

,

Π

REJECT

)

{\displaystyle (\Pi _{\text{ACCEPT}},\Pi _{\text{REJECT}})}

is thus

Π

ACCEPT

∪

Π

REJECT

{\displaystyle \Pi _{\text{ACCEPT}}\cup \Pi _{\text{REJECT}}}

, which is called the promise. Strings in

Π

ACCEPT

∪

Π

REJECT

{\displaystyle \Pi _{\text{ACCEPT}}\cup \Pi _{\text{REJECT}}}

are said to satisfy the promise. By definition,

Π

ACCEPT

{\displaystyle \Pi _{\text{ACCEPT}}}

and

Π

REJECT

{\displaystyle \Pi _{\text{REJECT}}}

must be disjoint, i.e.

Π

ACCEPT

∩

Π

REJECT

=
∅

{\displaystyle \Pi _{\text{ACCEPT}}\cap \Pi _{\text{REJECT}}=\emptyset }

.
Within this formulation, it can be seen that decision problems are just the subset of promise problems with the trivial promise

Π

ACCEPT

∪

Π

REJECT

=
{
0
,
1

}

∗

{\displaystyle \Pi _{\text{ACCEPT}}\cup \Pi _{\text{REJECT}}=\{0,1\}^{*}}

. With decision problems it is thus simpler to simply define the problem as only

Π

ACCEPT

{\displaystyle \Pi _{\text{ACCEPT}}}

(with

Π

REJECT

{\displaystyle \Pi _{\text{REJECT}}}

implicitly being

{
0
,
1

}

∗

/

Π

ACCEPT

{\displaystyle \{0,1\}^{*}/\Pi _{\text{ACCEPT}}}

), which throughout this page is denoted

L

{\displaystyle L}

to emphasize that

Π

ACCEPT

=
L

{\displaystyle \Pi _{\text{ACCEPT}}=L}

is a formal language.
Promise problems make for a more natural formulation of many computational problems. For instance, a computational problem could be something like "given a planar graph, determine whether or not..." This is often stated as a decision problem, where it is assumed that there is some translation schema that takes every string

s
∈
{
0
,
1

}

∗

{\displaystyle s\in \{0,1\}^{*}}

to a planar graph. However, it is more straightforward to define this as a promise problem in which the input is promised to be a planar graph.

Relation to complexity classes

Promise problems provide an alternate definition for standard complexity classes of decision problems. P, for instance, can be defined as a promise problem:

P is the class of promise problems that are solvable in deterministic polynomial time. That is, the promise problem

(

Π

ACCEPT

,

Π

REJECT

)

{\displaystyle (\Pi _{\text{ACCEPT}},\Pi _{\text{REJECT}})}

is in P if there exists a polynomial-time algorithm

M

{\displaystyle M}

such that:
For every

x
∈

Π

ACCEPT

,
M
(
x
)
=
1

{\displaystyle x\in \Pi _{\text{ACCEPT}},M(x)=1}

For ever

x
∈

Π

REJECT

,
M
(
x
)
=
0

{\displaystyle x\in \Pi _{\text{REJECT}},M(x)=0}

Classes of decision problems—that is, classes of problems defined as formal languages—thus translate naturally to promise problems, where a language

L

{\displaystyle L}

in the class is simply

L
=

Π

ACCEPT

{\displaystyle L=\Pi _{\text{ACCEPT}}}

and

Π

REJECT

{\displaystyle \Pi _{\text{REJECT}}}

is implicitly

{
0
,
1

}

∗

/

Π

ACCEPT

{\displaystyle \{0,1\}^{*}/\Pi _{\text{ACCEPT}}}

.
Formulating many basic complexity classes like P as promise problems provides little additional insight into their nature. However, there are some complexity classes for which formulating them as promise problems have been useful to computer scientists. Promise problems have, for instance, played a key role in the study of SZK (statistical zero-knowledge).

Summary of relationships between complexity classes

The following table shows some of the classes of problems that are considered in complexity theory. If class X is a strict subset of Y, then X is shown below Y with a dark line connecting them. If X is a subset, but it is unknown whether they are equal sets, then the line is lighter and dotted. Technically, the breakdown into decidable and undecidable pertains more to the study of computability theory, but is useful for putting the complexity classes in perspective.

Notes

References

Bibliography

Aaronson, Scott (8 January 2017). "P=?NP". Electronic Colloquim on Computational Complexity. Weizmann Institute of Science. Archived from the original on June 17, 2020.
Arora, Sanjeev; Barak, Boaz (2009). Computational Complexity: A Modern Approach. Cambridge University Press. Draft. Archived from the original on February 23, 2022. ISBN 978-0-521-42426-4.
Arora, Sanjeev (Spring 2003). "Complexity classes having to do with counting". Computer Science 522: Computational Complexity Theory. Princeton University. Archived from the original on May 21, 2022.
Barak, Boaz (Spring 2006). "Complexity of counting" (PDF). Computer Science 522: Computational Complexity. Princeton University. Archived from the original on April 3, 2021.
Fortnow, Lance (1997). "Counting Complexity" (PDF). In Hemaspaandra, Lane A.; Selman, Alan L. (eds.). Complexity Theory Retrospective II. Springer. pp. 81–106. ISBN 9780387949734. Archived from the original (PDF) on June 18, 2022.
Gasarch, William I. (2019). "Guest Column: The Third P =? NP Poll" (PDF). University of Maryland. Archived (PDF) from the original on November 2, 2021.
Goldreich, Oded (2006). "On Promise Problems: A Survey" (PDF). In Goldreich, Oded; Rosenberg, Arnold L.; Selman, Alen L. (eds.). Theoretical Computer Science. Lecture Notes in Computer Science, vol 3895 (PDF). Lecture Notes in Computer Science. Vol. 3895. Springer. pp. 254–290. doi:10.1007/11685654_12. ISBN 978-3-540-32881-0. Archived (PDF) from the original on May 6, 2021.
Johnson, David S. (1990). "A Catalog of Complexity Classes". Algorithms and Complexity. Handbook of Theoretical Computer Science. Elsevier. pp. 67–161. doi:10.1016/b978-0-444-88071-0.50007-2.
Lee, James R. (May 22, 2014). "Lecture 16" (PDF). CSE431: Introduction to Theory of Computation. University of Washington. Archived (PDF) from the original on November 29, 2021. Retrieved October 5, 2022.
Rich, Elaine (2008). Automata, Computability and Complexity: Theory and Applications (PDF). Prentice Hall. ISBN 978-0132288064. Archived (PDF) from the original on January 21, 2022.
Sipser, Michael (2006). Introduction to the Theory of Computation (PDF) (2nd ed.). USA: Thomson Course Technology. ISBN 0-534-95097-3. Archived from the original (PDF) on February 7, 2022.
Watrous, John (April 11, 2006). "Lecture 22: Quantum computational complexity" (PDF). University of Waterloo. Archived (PDF) from the original on June 18, 2022.

Background

= Computational problems

= Computational models

= Resource bounds

Basic complexity classes

= Basic definitions

= Time complexity classes

= Space complexity classes

Properties of complexity classes

= Closure

= Reductions

Relationships between complexity classes

= Savitch's theorem

= Hierarchy theorems

Other models of computation

= Randomized computation

= Interactive proof systems

= Boolean circuits

= Quantum computation

Other types of problems

= Counting problems

= Function problems

= Promise problems

Summary of relationships between complexity classes

See also

Notes

References

Bibliography

Further reading

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