19 relations: Circuit satisfiability problem, Clique (graph theory), Computational complexity theory, Computational problem, Computer science, Dominating set, Function (mathematics), Graph coloring, Independent set (graph theory), Kernelization, NP-completeness, NP-hardness, P versus NP problem, Parameterized complexity, Polynomial-time approximation scheme, Reduction (complexity), Satisfiability, Time complexity, Vertex cover.
In theoretical computer science, the circuit satisfiability problem (also known as CIRCUIT-SAT, CircuitSAT, CSAT, etc.) is the decision problem of determining whether a given Boolean circuit has an assignment of its inputs that makes the output true.
In the mathematical area of graph theory, a clique is subset of vertices of an undirected graph, such that its induced subgraph is complete; that is, every two distinct vertices in the clique are adjacent.
Computational complexity theory is a branch of the theory of computation in theoretical computer science and mathematics that focuses on classifying computational problems according to their inherent difficulty, and relating those classes to each other.
In theoretical computer science, a computational problem is a mathematical object representing a collection of questions that computers might be able to solve.
Computer science deals with the theoretical foundations of information and computation, together with practical techniques for the implementation and application of these foundations Computer science is the scientific and practical approach to computation and its applications.
In graph theory, a dominating set for a graph G.
In mathematics, a function is a relation between a set of inputs and a set of permissible outputs with the property that each input is related to exactly one output.
In graph theory, graph coloring is a special case of graph labeling; it is an assignment of labels traditionally called "colors" to elements of a graph subject to certain constraints.
In graph theory, an independent set or stable set is a set of vertices in a graph, no two of which are adjacent.
In computer science, a kernelization is a technique for designing efficient algorithms that achieve their efficiency by a preprocessing stage in which inputs to the algorithm are replaced by a smaller input, called a "kernel".
In computational complexity theory, a decision problem is NP-complete when it is both in NP and NP-hard.
NP-hardness (''n''on-deterministic ''p''olynomial-time hard), in computational complexity theory, is a class of problems that are, informally, "at least as hard as the hardest problems in NP".
The P versus NP problem is a major unsolved problem in computer science.
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.
In computer science, a polynomial-time approximation scheme (PTAS) is a type of approximation algorithm for optimization problems (most often, NP-hard optimization problems).
In computability theory and computational complexity theory, a reduction is an algorithm for transforming one problem into another problem.
In mathematical logic, satisfiability and validity are elementary concepts of semantics.
In computer science, the time complexity of an algorithm quantifies the amount of time taken by an algorithm to run as a function of the length of the string representing the input.
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.
FPT (complexity class), Fixed-parameter algorithm, Fixed-parameter tractability, Fixed-parameter tractable, Parameterised complexity, Parameterized (Multivariate) Complexity, Parameterized Complexity, Parametrised complexity, Parametrized complexity, W (complexity class), W Hierarchy, W hierarchy, W(1), W(2), W-Hierarchy, W-hierarchy, XP (class), XP (complexity class).