Примеры использования Approximation algorithm на Английском языке и их переводы на Русский язык
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There is no f(n)-approximation algorithm for the MSCP unless P NP.
Since the original paper of Goemans andWilliamson, SDPs have been applied to develop numerous approximation algorithms.
Not all approximation algorithms are suitable for direct practical applications.
Annotation: The paper describes the implementation of surface approximation algorithm for rings and cylinders by orthogonal rectangles.
The best known approximation algorithm has the non-constant approximation ratio Olog n log log n.
Despite the equivalence of the two problems from the point of view of exact solutions,they are not equivalent for approximation algorithms.
This is a constant factor approximation algorithm with an approximation factor of 2.
The method is particularly relevant in the context of randomized rounding which uses the probabilistic method to design approximation algorithms.
There are also efficient approximation algorithms for approximating cr(G) on graphs of bounded degree.
The following example illustrates how randomized rounding can be used to design an approximation algorithm for the Set Cover problem.
Thus, every polynomial-time approximation algorithm achieves an approximation ratio strictly less than one.
As with minor-closed graph families of bounded local treewidth,this property has pointed the way to efficient approximation algorithms for these graphs.
There is a simple polynomial-time approximation algorithm with approximation factor 2: find any maximal matching.
If an algorithm A guarantees to return solutions with a performance guarantee of at most r(n),then A is said to be an r(n)-approximation algorithm and has an approximation ratio of rn.
No approximation algorithms for computing P( G, x){\displaystyle P(G, x)} are known for any x except for the three easy points.
It was also proven that the problem does not have an approximation algorithm running in polynomial time for any(constant) factor, unless P NP.
The PCP theorem is the cornerstone of the theory of computational hardness of approximation, which investigates the inherent difficulty in designing efficient approximation algorithms for various optimization problems.
The error level in the approximation algorithm is measured as an approximation factor, which is defined as the ratio between the approximation and the optimum.
However, under plausible complexity-theoretic assumptions,there is no polynomial-time approximation algorithm with a sub-logarithmic approximation factor.
The best polynomial time approximation algorithm known for this case achieves only a very weak approximation ratio, n/ exp( Ω( log n)){\displaystyle n/\exp\Omega{\sqrt{\log n.
Apex-minor-free graph families obey a strengthened version of the graph structure theorem,leading to additional approximation algorithms for graph coloring and the travelling salesman problem.
An approximation algorithm is known, and the problem may be solved efficiently for lines that fall into a small number of parallel families(as is typical for urban street grids), but the general problem remains open.
Based on these properties, numerous algorithms for planar graphs,such as Baker's technique for designing approximation algorithms, can be extended to 1-planar graphs.
There were developed approximation algorithms for rational transfer functions of a high order,approximation models for objects with distributed parameters, which are described by irrational and transcendental transfer functions.
Although it is NP-hard to compute the clique-width when it is unbounded, and unknown whether it can be computed in polynomial time when it is bounded,efficient approximation algorithms for the clique-width are known.
These characterizations have been used as an important tool in the construction of approximation algorithms and subexponential-time exact algorithms for NP-complete optimization problems on minor-closed graph families.
The separator based divide and conquer paradigm has also been used to design data structures for dynamic graph algorithms and point location, algorithms for polygon triangulation, shortest paths, andthe construction of nearest neighbor graphs, and approximation algorithms for the maximum independent set of a planar graph.
There is a polynomial-time approximation algorithm with a logarithmic approximation guarantee, that is, it is possible to find a domatic partition whose size is within a factor O( log| V|){\displaystyle O(\log|V|)} of the optimum.
Since it is conjectured that NP-complete problems do not have quasi-polynomial time algorithms, some inapproximability results in the field of approximation algorithms make the assumption that NP-complete problems do not have quasi-polynomial time algorithms. .
More specifically, a polynomial-time approximation algorithm for domatic partition with the approximation factor( 1- ϵ) ln| V|{\displaystyle(1-\epsilon)\ln|V|} for a constant ϵ> 0{\displaystyle\epsilon>0} would imply that all problems in NP can be solved in slightly super-polynomial time n O( log log n){\displaystyle n^{O\log\log n.