• Source: Quasi-polynomial time

In computational complexity theory and the analysis of algorithms, an algorithm is said to take quasi-polynomial time if its time complexity is quasi-polynomially bounded. That is, there should exist a constant



c


{\displaystyle c}

such that the worst-case running time of the algorithm, on inputs of size



n


{\displaystyle n}

, has an upper bound of the form





2

O


(


(
log
⁡
n

)

c




)




.


{\displaystyle 2^{O{\bigl (}(\log n)^{c}{\bigr )}}.}


The decision problems with quasi-polynomial time algorithms are natural candidates for being NP-intermediate, neither having polynomial time nor likely to be NP-hard.




Complexity class


The complexity class QP consists of all problems that have quasi-polynomial time algorithms. It can be defined in terms of DTIME as follows.






Q
P


=

⋃

c
∈

N





D
T
I
M
E



(

2

(
log
⁡
n

)

c




)



{\displaystyle {\mathsf {QP}}=\bigcup _{c\in \mathbb {N} }{\mathsf {DTIME}}\left(2^{(\log n)^{c}}\right)}





Examples


An early example of a quasi-polynomial time algorithm was the Adleman–Pomerance–Rumely primality test; however, the problem of testing whether a number is a prime number has subsequently been shown to have a polynomial time algorithm, the AKS primality test.
In some cases, quasi-polynomial time bounds can be proven to be optimal under the exponential time hypothesis or a related computational hardness assumption. For instance, this is true for finding the largest disjoint subset of a collection of unit disks in the hyperbolic plane, and for finding a graph with the fewest vertices that does not appear as an induced subgraph of a given graph.
Other problems for which the best known algorithm takes quasi-polynomial time include:

The planted clique problem, of determining whether a random graph has been modified by adding edges between all pairs of a subset of its vertices.
Monotone dualization, several equivalent problems of converting logical formulas between conjunctive and disjunctive normal form, listing all minimal hitting sets of a family of sets, or listing all minimal set covers of a family of sets, with time complexity measured in the combined input and output size.
Parity games, involving token-passing along the edges of a colored directed graph. The paper giving a quasi-polynomial algorithm for these games won the 2021 Nerode Prize.
Computing the Vapnik–Chervonenkis dimension of a family of sets.
Finding the smallest dominating set in a tournament, a subset of the vertices of the tournament that has at least one directed edge to all other vertices.
Problems for which a quasi-polynomial time algorithm has been announced but not fully published include:

The graph isomorphism problem, determining whether two graphs can be made equal to each other by relabeling their vertices, announced in 2015 and updated in 2017 by László Babai.
The unknotting problem, recognizing whether a knot diagram describes the unknot, announced by Marc Lackenby in 2021.




In approximation algorithms


Quasi-polynomial time has also been used to study approximation algorithms. In particular, a quasi-polynomial-time approximation scheme (QPTAS) is a variant of a polynomial-time approximation scheme whose running time is quasi-polynomial rather than polynomial. Problems with a QPTAS include minimum-weight triangulation, and finding the maximum clique on the intersection graph of disks.
More strongly, the problem of finding an approximate Nash equilibrium has a QPTAS, but cannot have a PTAS under the exponential time hypothesis.




References

Kata Kunci Pencarian:

• Algoritma
• Quasi-polynomial time
• Time complexity
• Quasi-polynomial growth
• Pseudo-polynomial time
• Monotone dualization
• Polynomial-time approximation scheme
• Graph isomorphism problem
• Polylogarithmic function
• Planted clique
• NP-intermediate