77 relations: Abstract machine, AC (complexity), Algorithm, ALL (complexity), Arthur–Merlin protocol, ♯P, Big O notation, Blum axioms, Boolean circuit, BPP (complexity), BQP, Circuit complexity, Co-NP, Cobham's thesis, Complete (complexity), Computability theory, Computational complexity theory, Computational model, Computational problem, Computational resource, Context-free grammar, Context-sensitive grammar, Counting problem (complexity), David S. Johnson, Decision problem, Descriptive complexity theory, DSPACE, DTIME, EXPSPACE, EXPTIME, FP (complexity), Function problem, Interactive proof system, IP (complexity), L (complexity), List of complexity classes, List of undecidable problems, Log-space reduction, Logarithm, Logical conjunction, Logical connective, Logical disjunction, Mathematical logic, Michael Garey, NC (complexity), Negation, Neil Immerman, NEXPTIME, NL (complexity), Non-deterministic Turing machine, ..., NP (complexity), NP-completeness, NP-hardness, NSPACE, NTIME, Optimization problem, P (complexity), Polynomial, Polynomial-time reduction, Probabilistic Turing machine, Promise problem, PSPACE, QMA, Quantum information science, Quantum Turing machine, Recursive language, Recursively enumerable language, Regular grammar, RP (complexity), Savitch's theorem, Space hierarchy theorem, Subset, Time complexity, Time hierarchy theorem, Turing machine, Turing reduction, ZPP (complexity). Expand index (27 more) » « Shrink index
An abstract machine, also called an abstract computer, is a theoretical model of a computer hardware or software system used in automata theory.
In circuit complexity, AC is a complexity class hierarchy.
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In mathematics and computer science, an algorithm is an unambiguous specification of how to solve a class of problems.
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In computability and complexity theory, ALL is the class of all decision problems.
In computational complexity theory, an Arthur–Merlin protocol is an interactive proof system in which the verifier's coin tosses are constrained to be public (i.e. known to the prover too).
In computational complexity theory, the complexity class ♯P (pronounced "number P" or, sometimes "sharp P" or "hash P") is the set of the counting problems associated with the decision problems in the set NP.
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Big O notation is a mathematical notation that describes the limiting behaviour of a function when the argument tends towards a particular value or infinity.
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In computational complexity theory the Blum axioms or Blum complexity axioms are axioms that specify desirable properties of complexity measures on the set of computable functions.
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In computational complexity theory and circuit complexity, a Boolean circuit is a mathematical model for digital logic circuits.
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In computational complexity theory, BPP, which stands for bounded-error probabilistic polynomial time is the class of decision problems solvable by a probabilistic Turing machine in polynomial time with an error probability bounded away from 1/2 for all instances.
In computational complexity theory, BQP (bounded-error quantum polynomial time) is the class of decision problems solvable by a quantum computer in polynomial time, with an error probability of at most 1/3 for all instances.
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In theoretical computer science, circuit complexity is a branch of computational complexity theory in which Boolean functions are classified according to the size or depth of Boolean circuits that compute them.
In computational complexity theory, co-NP is a complexity class.
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Cobham's thesis, also known as Cobham–Edmonds thesis (named after Alan Cobham and Jack Edmonds), asserts that computational problems can be feasibly computed on some computational device only if they can be computed in polynomial time; that is, if they lie in the complexity class P (PTIME).
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In computational complexity theory, a computational problem is complete for a complexity class if it is, in a technical sense, among the "hardest" (or "most expressive") problems in the complexity class.
Computability theory, also known as recursion theory, is a branch of mathematical logic, of computer science, and of the theory of computation that originated in the 1930s with the study of computable functions and Turing degrees.
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.
A computational model is a mathematical model in computational science that requires extensive computational resources to study the behavior of a complex system by computer simulation.
In theoretical computer science, a computational problem is a mathematical object representing a collection of questions that computers might be able to solve.
In computational complexity theory, a computational resource is a resource used by some computational models in the solution of computational problems.
In formal language theory, a context-free grammar (CFG) is a certain type of formal grammar: a set of production rules that describe all possible strings in a given formal language.
A context-sensitive grammar (CSG) is a formal grammar in which the left-hand sides and right-hand sides of any production rules may be surrounded by a context of terminal and nonterminal symbols.
In computational complexity theory and computability theory, a counting problem is a type of computational problem.
David Stifler Johnson (December 9, 1945 – March 8, 2016) was an American computer scientist specializing in algorithms and optimization.
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.
Descriptive complexity is a branch of computational complexity theory and of finite model theory that characterizes complexity classes by the type of logic needed to express the languages in them.
In computational complexity theory, DSPACE or SPACE is the computational resource describing the resource of memory space for a deterministic Turing machine.
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In computational complexity theory, DTIME (or TIME) is the computational resource of computation time for a deterministic Turing machine.
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In complexity theory, '' is the set of all decision problems solvable by a deterministic Turing machine in O(2p(n)) space, where p(n) is a polynomial function of n. (Some authors restrict p(n) to be a linear function, but most authors instead call the resulting class.) If we use a nondeterministic machine instead, we get the class, which is equal to by Savitch's theorem.
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In computational complexity theory, the complexity class EXPTIME (sometimes called EXP or DEXPTIME) is the set of all decision problems that have exponential runtime, i.e., that are solvable by a deterministic Turing machine in O(2p(n)) time, where p(n) is a polynomial function of n. In terms of DTIME, We know and also, by the time hierarchy theorem and the space hierarchy theorem, that so at least one of the first three inclusions and at least one of the last three inclusions must be proper, but it is not known which ones are.
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In computational complexity theory, the complexity class FP is the set of function problems which can be solved by a deterministic Turing machine in polynomial time; it is the function problem version of the decision problem class P. Roughly speaking, it is the class of functions that can be efficiently computed on classical computers without randomization.
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In computational complexity theory, a function problem is a computational problem where a single output (of a total function) is expected for every input, but the output is more complex than that of a decision problem.
In computational complexity theory, an interactive proof system is an abstract machine that models computation as the exchange of messages between two parties.
In computational complexity theory, the class IP (which stands for Interactive Polynomial time) is the class of problems solvable by an interactive proof system.
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In computational complexity theory, L (also known as LSPACE or DLOGSPACE) is the complexity class containing decision problems that can be solved by a deterministic Turing machine using a logarithmic amount of writable memory space.
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This is a list of complexity classes in computational complexity theory.
In computability theory, an undecidable problem is a type of computational problem that requires a yes/no answer, but where there cannot possibly be any computer program that always gives the correct answer; that is, any possible program would sometimes give the wrong answer or run forever without giving any answer.
In computational complexity theory, a log-space reduction is a reduction computable by a deterministic Turing machine using logarithmic space.
In mathematics, the logarithm is the inverse function to exponentiation.
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In logic, mathematics and linguistics, And (∧) is the truth-functional operator of logical conjunction; the and of a set of operands is true if and only if all of its operands are true.
In logic, a logical connective (also called a logical operator, sentential connective, or sentential operator) is a symbol or word used to connect two or more sentences (of either a formal or a natural language) in a grammatically valid way, such that the value of the compound sentence produced depends only on that of the original sentences and on the meaning of the connective.
In logic and mathematics, or is the truth-functional operator of (inclusive) disjunction, also known as alternation; the or of a set of operands is true if and only if one or more of its operands is true.
Mathematical logic is a subfield of mathematics exploring the applications of formal logic to mathematics.
Michael Randolph Garey is a computer science researcher, and co-author (with David S. Johnson) of Computers and Intractability: A Guide to the Theory of NP-completeness.
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In complexity theory, the class NC (for "Nick's Class") is the set of decision problems decidable in polylogarithmic time on a parallel computer with a polynomial number of processors.
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In logic, negation, also called the logical complement, is an operation that takes a proposition P to another proposition "not P", written \neg P (¬P), which is interpreted intuitively as being true when P is false, and false when P is true.
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Neil Immerman (24 November 1953, Manhasset, New York) is an American theoretical computer scientist, a professor of computer science at the University of Massachusetts Amherst.
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In computational complexity theory, the complexity class NEXPTIME (sometimes called NEXP) is the set of decision problems that can be solved by a non-deterministic Turing machine using time 2n O(1).
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In computational complexity theory, NL (Nondeterministic Logarithmic-space) is the complexity class containing decision problems which can be solved by a nondeterministic Turing machine using a logarithmic amount of memory space.
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In theoretical computer science, a Turing machine is a theoretical machine that is used in thought experiments to examine the abilities and limitations of computers.
In computational complexity theory, NP (for nondeterministic polynomial time) is a complexity class used to describe certain types of decision problems.
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In computational complexity theory, an NP-complete decision problem is one belonging to both the NP and the NP-hard complexity classes.
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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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In computational complexity theory, non-deterministic space or NSPACE is the computational resource describing the memory space for a non-deterministic Turing machine.
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In computational complexity theory, the complexity class NTIME(f(n)) is the set of decision problems that can be solved by a non-deterministic Turing machine which runs in time O(f(n)).
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In mathematics and computer science, an optimization problem is the problem of finding the best solution from all feasible solutions.
In computational complexity theory, P, also known as PTIME or DTIME(nO(1)), is a fundamental complexity class.
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In mathematics, a polynomial is an expression consisting of variables (also called indeterminates) and coefficients, that involves only the operations of addition, subtraction, multiplication, and non-negative integer exponents of variables.
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In computational complexity theory, a polynomial-time reduction is a method of solving one problem by means of a hypothetical subroutine for solving a different problem (that is, a reduction), that uses polynomial time excluding the time within the subroutine.
In computability theory, a probabilistic Turing machine is a non-deterministic Turing machine which chooses between the available transitions at each point according to some probability distribution.
In computational complexity theory, a promise problem is a generalization of a decision problem where the input is promised to belong to a particular subset of all possible inputs.
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In computational complexity theory, PSPACE is the set of all decision problems that can be solved by a Turing machine using a polynomial amount of space.
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In computational complexity theory, QMA, which stands for Quantum Merlin Arthur, is the quantum analog of the nonprobabilistic complexity class NP or the probabilistic complexity class MA.
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Quantum information science is an area of study based on the idea that information science depends on quantum effects in physics.
A quantum Turing machine (QTM), also a universal quantum computer, is an abstract machine used to model the effect of a quantum computer.
In mathematics, logic and computer science, a formal language (a set of finite sequences of symbols taken from a fixed alphabet) is called recursive if it is a recursive subset of the set of all possible finite sequences over the alphabet of the language.
In mathematics, logic and computer science, a formal language is called recursively enumerable (also recognizable, partially decidable, semidecidable, Turing-acceptable or Turing-recognizable) if it is a recursively enumerable subset in the set of all possible words over the alphabet of the language, i.e., if there exists a Turing machine which will enumerate all valid strings of the language.
In theoretical computer science and formal language theory, a regular grammar is a formal grammar that is right-regular or left-regular.
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In computational complexity theory, randomized polynomial time (RP) is the complexity class of problems for which a probabilistic Turing machine exists with these properties.
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In computational complexity theory, Savitch's theorem, proved by Walter Savitch in 1970, gives a relationship between deterministic and non-deterministic space complexity.
In computational complexity theory, the space hierarchy theorems are separation results that show that both deterministic and nondeterministic machines can solve more problems in (asymptotically) more space, subject to certain conditions.
In mathematics, a set A is a subset of a set B, or equivalently B is a superset of A, if A is "contained" inside B, that is, all elements of A are also elements of B. A and B may coincide.
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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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In computational complexity theory, the time hierarchy theorems are important statements about time-bounded computation on Turing machines.
A Turing machine is a mathematical model of computation that defines an abstract machine, which manipulates symbols on a strip of tape according to a table of rules.
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In computability theory, a Turing reduction from a problem A to a problem B, is a reduction which solves A, assuming the solution to B is already known (Rogers 1967, Soare 1987).
In complexity theory, ZPP (zero-error probabilistic polynomial time) is the complexity class of problems for which a probabilistic Turing machine exists with these properties.