If NP = coNP, then any inconsistent Boolean formula of size n has a proof of inconsistency of size polynomial in n.
If NP does not equal coNP, then P does not equal NP. But the other direction is not known.
See also: NP ∩ coNP.
Every problem in coNP has an IP (interactive proof) system, where moreover the prover can be restricted to BPP#P. If every problem in coNP has an interactive protocol whose rounds are bounded by a polylogarithmic function, then EH collapses to the third level [SS04].
Co-NP is equal to SO-A, the second-order queries where the second-order quantifiers are only universals.
The class of decision problems for which a "yes" answer can be verified by an Arthur-Merlin protocol, as follows.
Arthur, a BPP (i.e. probabilistic polynomial-time) verifier, generates a "challenge" based on the input, and sends it together with his random coins to Merlin. Merlin sends back a response, and then Arthur decides whether to accept. Given an algorithm for Arthur, we require that
Surprisingly, it turns out that such a system is just as powerful as a private-coin one, in which Arthur does not need to send his random coins to Merlin [GS86]. So, Arthur never needs to hide information from Merlin.
Furthermore, define AM[k] similarly to AM, except that Arthur and Merlin have k rounds of interaction. Then for all constant k>2, AM[k] = AM[2] = AM [BM88]. Also, the result of [GS86] can then be stated as follows: IP[k] is contained in AM[k+2] for every k (constant or non-constant).
AM contains graph nonisomorphism.
Contains NP, BPP, and SZK, and is contained in NP/poly.AM is also contained in Π2P and this proof relativizes so the containment holds relative to any oracle.
If AM contains coNP then PH collapses to Σ2P ∩ Π2P [BHZ87].
There exists an oracle relative to which AM is not contained in PP [Ver92].
AM = NP under a strong derandomization assumption: namely that some language in NE ∩ coNE requires nondeterministic circuits of size 2Ω(n) ([MV99], improving [KM99]). (A nondeterministic circuit C has two inputs, x and y, and accepts on x if there exists a y such that C(x,y)=1.)
Same as AM, except that Arthur is exponential-time and can exchange exponentially long messages with Merlin.
Contains MAEXP, and is contained in EH and indeed S2-EXP•PNP.
If coNP is contained in AM[polylog] then EH collapses to AMEXP [PV04].
Same as AM, except that we allow polylog(n) rounds of interaction between Arthur and Merlin instead of a constant number.
Not much is known about AM[polylog] -- for example, whether it sits in PH. However, [SS04] show that if AM[polylog] contains coNP, then EH collapses to S2-EXP•PNP. ([PV04] improved the collapse to AMEXP.)
The smallest class that contains NP and is closed under union, intersection, and complement.
The levels are defined as follows:
Then BH is the union over all i of BHi.
Defined in [WW85].
For more detail see [CGH+88].
Contained in Δ2P and indeed in PNP[log].
If BH collapses at any level, then PH collapses to Σ3P [Kad88].
The class of decision problems such that (1) they're in coNP and (2) every problem in coNP is reducible to them (under some notion of reduction). In other words, the hardest problems in coNP.
Has roughly the same relationship to E as PH does to P.
More formally, EH is defined as the union of E, NE, NENP, NE with Σ2P oracle, and so on.
See [Har87] for more information.
If coNP is contained in AM[polylog], then EH collapses to S2-EXP•PNP [SS04] and indeed AMEXP [PV04].
On the other hand, coNE is contained in NE/poly, so perhaps it wouldn't be so surprising if NE collapses.
There exists an oracle relative to which EH does not contain SEH [Hem89]. EH and SEH are incomparable for all anyone knows.
The class of decision problems for which a "yes" answer can be verified by
For example, GC(p(n),P) = NP where p is a polynomial.
Defined in [CC93].
Umans [Uma98] has shown that given a DNF expression Φ, the Shortest Implicant problem is GC(log2n, coNP)-complete.
The class of decision problems for which a "yes" answer can be verified by an interactive proof. Here a probabilistic polynomial-time verifier sends messages back and forth with an all-powerful prover. They can have polynomially many rounds of interaction. Given the verifier's algorithm, at the end:
Defined in [GMR89], with the motivation of providing a framework for the introduction of zero-knowledge proofs (see the class ZK). Interestingly, the power of general interactive proof systems is not decreased if the verifier is only allowed random queries (i.e., it merely tosses coins and sends any outcome to the prover). The latter model, known as the Arthur-Merlin (or public-coin) model was introduced independently (but later) in [Bab85], and a strong equivalent (which preserves the number of rounds) is proved in [GS86]. Often, it is required that the prover can convince the verifier to accept correct assertions with probability 1; this is called perfect completeness.However, the definitions with one-sided and two-sided error can be shown to be equivalent (see [FGM+89]).
First demonstration to the power of interactive proofs was given by showing that for graph nonisomorphism (a problem not known in NP) has such proofs [GMW91]. Five years later is was shown thatIP contains PH [LFK+90], and indeed (this was discovered only a few weeks later) equals PSPACE [Sha90]. On the other hand, coNP is not contained in IP relative to a random oracle [CCG+94].
The verifier for PSPACE only uses logarithmic space when given two way, read only access to its randomness. On the other hand, given only read once access to its randomness, a log space verifier can only verify languages in P [Sha90]. Further, a log space verifier with read once access to its randomness can verifier any language in P [GKR15]. See BPL vs BP•L for a comparison between read once and read multiple access to randomness.
The class of dashed hopes and idle dreams.
More formally: an "NP machine" is a nondeterministic polynomial-time Turing machine.
Then NP is the class of decision problems solvable by an NP machine such that
Equivalently, NP is the class of decision problems such that, if the answer is "yes," then there is a proof of this fact, of length polynomial in the size of the input, that can be verified in P (i.e. by a deterministic polynomial-time algorithm). On the other hand, if the answer is "no," then the algorithm must declare invalid any purported proof that the answer is "yes."
For example, the SAT problem is to decide whether a given Boolean formula has any satisfying truth assignments. SAT is in NP, since a "yes" answer can be proved by just exhibiting a satisfying assignment.
A decision problem is NP-complete if (1) it is in NP, and (2) any problem in NP can be reduced to it (under some notion of reduction). The class of NP-complete problems is sometimes called NPC.
That NP-complete problems exist is immediate from the definition. The seminal result of Cook [Coo71], Karp [Kar72], and Levin [Lev73] is that many natural problems (that have nothing to do with Turing machines) are NP-complete.
The first such problem to be shown NP-complete was SAT [Coo71]. Other classic NP-complete problems include:
For many, many more NP-complete problems, see [GJ79].
There exists an oracle relative to which P and NP are unequal [BGS75]. Indeed, P and NP are unequal relative to a random oracle with probability 1 [BG81] (see [AFM01] for a novel take on this result). Though random oracle results are not always indicative about the unrelativized case [CCG+94].
There even exists an oracle relative to which the P versus NP problem is outside the usual axioms of set theory [HH76].
If we restrict to monotone classes, mP is strictly contained in mNP [Raz85].
Perhaps the most important insight anyone has had into P versus NP is to be found in [RR97]. There the authors show that no 'natural proof' can separate P from NP (or more precisely, place NP outside of P/poly), unless secure pseudorandom generators do not exist. A proof is 'natural' if it satisfies two conditions called constructivity and largeness; essentially all lower bound techniques known to date satisfy these conditions. To obtain unnatural proof techniques, some people suspect we need to relate P versus NP to heavy-duty 'traditional' mathematics, for instance algebraic geometry. See [MS02] (and the survey article [Reg02]) for a development of this point of view.
For more on P versus NP (circa 1992) see [Sip92]. For an opinion poll, see [Gas02]. P vs NP is a "Millenium Prize" problem [CMI00].
If P equals NP, then NP equals its complement coNP. Whether NP equals coNP is also open. NP and coNP can be extended to the polynomial hierarchy PH.
The set of decision problems in NP, but not in P or NPC, is sometimes called NPI. If P does not equal NP then NPI is nonempty [Lad75].
Probabilistic generalizations of NP include MA and AM. If NP is in coAM (or BPP) then PH collapses to Σ2P [BHZ87].
PH also collapses to Σ2P if NP is in P/poly [KL82].
There exist oracles relative to which NP is not in BQP [BBB+97].
An alternate characterization is NP = PCP(log n, O(1)) [ALM+98].
Also, [Fag74] showed that NP is precisely the class of decision problems reducible to a graph-theoretic property expressible in second-order existential logic. This leads to the subclass SNP.
It is known that if any NP-complete language is sparse (contains no more than a polynomial number of strings of length ), then P = NP. [BH08] improved this result, showing that if any language in NP has an NP-hard set of subexponential density, then coNP is contained in NP/poly and thus, by [Yap82], PH collapses to the third level.
NP is equal to SO-E, the second-order queries where the second-order quantifiers are only existantials.
The class of problems in both NP and coNP.
Contains graph isomorphism under the assumption that some language in NE ∩ coNE requires nondeterministic circuits of size 2Ω(n) ([MV99], improving [KM99]). (A nondeterministic circuit C has two inputs, x and y, and accepts on x if there exists a y such that C(x,y)=1.)
Equals PNP ∩ coNP [Bra79]. In particular, if a problem in NP ∩ coNP is NP-hard with Turing reduction, then NP = coNP.
Is not believed to contain complete problems.
Is equal to Low(NP)={L : NPL=NP} [Sch83].
Together with NP/poly ∩ coNP/poly, has the same relation to NP ∩ coNP as P/poly has to P. A language in (NP ∩ coNP)/poly is defined by a single language in NP ∩ coNP which is then modified by advice. A language in NP/poly ∩ coNP/poly comes from two possibly different languages in NP and coNP which become the same with good advice.
There is an oracle relative to which NP/poly ∩ coNP/poly, indeed NP/1 ∩ coNP/1, is not contained in (NP ∩ coNP)/poly [FFK+93]. Recently they improved this to NP/1 ∩ coNP [FF..].
If NP is contained in (NP ∩ coNP)/poly, then PH collapses to S2PNP ∩ coNP [CCH+01].
Has the same relation to NP as P/poly does to P.
Contains AM. On the other hand, if NP/poly contains coNP then PH collapses to the third level.
NP/poly-natural proofs cannot show that circuit families are outside P/poly, under a pseudorandomness assumption [Rud97].
Let Δ0P = Σ0P = Π0P = P. Then for i>0, let
Then PH is the union of these classes for all nonnegative constants i.
PH can also be defined using alternating quantifiers: it's the class of problems of the form, "given an input x, does there exist a y such that for all z, there exists a w ... such that φ(x,y,z,w,...)," where y,z,w,... are polynomial-size strings and φ is a polynomial-time computable predicate. It's not totally obvious that this is equivalent to the first definition, since the first one involves adaptive NP oracle queries and the second one doesn't, but it is.
Defined in [Sto76].
Contained in P with a PP oracle [Tod89].
Relative to a random oracle, PH is strictly contained in PSPACE with probability 1 [Cai86].
Furthermore, there exist oracles separating any ΣiP from Σi+1P. In fact, relative to a random oracle, the hierarchy is infinite: each level is strictly contained in the next, with probability 1 [RST15] (this was an open problem for 29 years). Previously, it had been known that if it had collapsed relative to a random oracle, then it would have collapsed unrelativized [Boo94].
It was shown in [CPO7] that if the NP Machine Hypothesis holds, then.
For a compendium of problems complete for different classes of the Polynomial Hierarchy see [Sch02a] and [Sch02b].
PH is equal to the set of boolean queries recognizable by a concurent random acess machine using exponentially many processors and constant time[Imm89].
Since NP is the class of query expressible in second-order existantial logic, PH can also be defined as the query expressible in second-order logic.
Complement of Σ2P.
Along with Σ2P, comprises the second level of PH, the polynomial hierarchy. For any fixed k, there is a problem in Π2P ∩ Σ2P that cannot be solved by circuits of size nk [Kan82].
One of the caged classes of the Complexity Zoo.
Has been implicated in a collapse scandal involving AM[polylog], coNP, and EH.
Contains languages whose complements are in Π2P.
Along with Π2P, comprises the second level of PH, the polynomial hierarchy.
Note that this is equal to NP with a coNP oracle.
[Uma98] has shown that the following problems are complete for Σ2P:
The problem of deciding if a perfect graph is 2-clique-colorable (defined in [FMF16]) has been shown to be complete for Σ2P.
For any fixed k, there is a problem in Σ2P ∩ Π2P that cannot be solved by circuits of size nk [Kan82].
We define second-order variable has got an arity and represent any proposition of arity . They are usually written in upper-case.
Second order logic is the set of FO formulae where we add quantification over second-order variables.
Every formuale is equivalent to a formulae in prenex normal form, where we first write quantification on variable on second order and then a FO-formulae in prenex normal form.
In Descriptive complexity we can see that SO is equal to PH, more precisely we have that formulae in prenex normal form where existential and universal of second order alternate times are the th level of the polynomial hierarchy.
This means that SO with only existential second-order quantification is equal to which is NP, and with only universal quantification is equal to which is Co-NP.
The all-American counting class.
The class of decision problems solvable by an NP machine such that the answer is 'yes' if and only if exactly one computation path accepts.
In contrast to UP, a machine can legally have more than one accepting path - that just means that the corresponding input is not in the language.
Defined in [BG82].
Contains coNP.
The class of decision problems for which there exists a polynomial-time machine M such that:
Defined in a recent post of the blog Shtetl-Optimized. See there for an explanation of the class's name.
Contains ZPP by the same argument that places BPP in P/poly.
Also contains P with TALLY ∩ NP ∩ coNP oracle.
Is contained in NP ∩ coNP and YPP.
Is equal to ONP ∩ coONP.
![{\displaystyle {\mathsf {P}}^{\mathrm {SAT} [1]}={\mathsf {P}}^{\mathrm {SAT} [2]}\Rightarrow {\mathsf {PH}}\subseteq {\mathsf {NP}}}](https://wikimedia.org/api/rest_v1/media/math/render/svg/7d8cb3e8275ecee8473c27fa153879d889159b1d)
