Computational complexity theory and Vertex cover

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Difference between Computational complexity theory and Vertex cover

Computational complexity theory vs. Vertex cover

Computational complexity theory is a branch of the theory of computation in theoretical computer science that focuses on classifying computational problems according to their inherent difficulty, and relating those classes to each other. In the mathematical discipline of graph theory, a vertex cover (sometimes node cover) of a graph is a set of vertices such that each edge of the graph is incident to at least one vertex of the set.

Similarities between Computational complexity theory and Vertex cover

Computational complexity theory and Vertex cover have 9 things in common (in Unionpedia): Boolean satisfiability problem, Decision problem, Graph (discrete mathematics), Graph theory, NP-completeness, NP-hardness, Optimization problem, Parameterized complexity, Time complexity.

Boolean satisfiability problem

In computer science, the Boolean satisfiability problem (sometimes called propositional satisfiability problem and abbreviated as SATISFIABILITY or SAT) is the problem of determining if there exists an interpretation that satisfies a given Boolean formula.

Boolean satisfiability problem and Computational complexity theory · Boolean satisfiability problem and Vertex cover · See more »

Decision problem

In computability theory and computational complexity theory, a decision problem is a problem that can be posed as a yes-no question of the input values.

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Graph (discrete mathematics)

In mathematics, and more specifically in graph theory, a graph is a structure amounting to a set of objects in which some pairs of the objects are in some sense "related".

Computational complexity theory and Graph (discrete mathematics) · Graph (discrete mathematics) and Vertex cover · See more »

Graph theory

In mathematics, graph theory is the study of graphs, which are mathematical structures used to model pairwise relations between objects.

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NP-completeness

In computational complexity theory, an NP-complete decision problem is one belonging to both the NP and the NP-hard complexity classes.

Computational complexity theory and NP-completeness · NP-completeness and Vertex cover · See more »

NP-hardness

NP-hardness (''n''on-deterministic ''p''olynomial-time hardness), in computational complexity theory, is the defining property of a class of problems that are, informally, "at least as hard as the hardest problems in NP".

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Optimization problem

In mathematics and computer science, an optimization problem is the problem of finding the best solution from all feasible solutions.

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Parameterized complexity

In computer science, parameterized complexity is a branch of computational complexity theory that focuses on classifying computational problems according to their inherent difficulty with respect to multiple parameters of the input or output.

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Time complexity

In computer science, the time complexity is the computational complexity that describes the amount of time it takes to run an algorithm.

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The list above answers the following questions

What Computational complexity theory and Vertex cover have in common
What are the similarities between Computational complexity theory and Vertex cover

Computational complexity theory and Vertex cover Comparison

Computational complexity theory has 164 relations, while Vertex cover has 51. As they have in common 9, the Jaccard index is 4.19% = 9 / (164 + 51).

References

This article shows the relationship between Computational complexity theory and Vertex cover. To access each article from which the information was extracted, please visit:

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