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 a subset of vertices of an undirected graph such that every two distinct vertices in the clique are adjacent; that is, its induced subgraph is complete.
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 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.
In graph theory, a dominating set for a graph G.
In mathematics, a function was originally the idealization of how a varying quantity depends on another quantity.
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, an NP-complete decision problem is one belonging to both the NP and the NP-hard complexity classes.
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".
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 or output.
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 is the computational complexity that describes the amount of time it takes to run an algorithm.
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).