Exemplos de uso de Complexity measure em Inglês e suas traduções para o Português
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Formula_14 is"not" a complexity measure, since it fails the second axiom.
Importantly, Blum's speedup theorem andthe Gap theorem hold for any complexity measure satisfying these axioms.
Complexity measures are very generally defined by the Blum complexity axioms.
Buhrman, Harry; de Wolf,Ronald(2002), Complexity Measures and Decision Tree Complexity: A Survey.
Somewhat earlier, Boris Trakhtenbrot(1956), a pioneer in the field from the USSR,studied another specific complexity measure.
Overview==Algorithmic information theory principally studies complexity measures on strings or other data structures.
Notes===A Blum complexity measure is defined using computable functions without any reference to a specific model of computation.
The concept, although not the method,is somewhat similar to that of general text complexity measured by the Flesch-Kincaid Readability Test.
Since computational complexity measures difficulty with respect to the length of the(encoded) input, this naive algorithm is actually exponential.
Although time andspace are the most well-known complexity resources, any complexity measure can be viewed as a computational resource.
Average case complexity measures something else: the balance between the frequency of difficult instances and the degree of difficulty.
Similar remarks apply to the second-most commonly used complexity measure, the amount of space or computer memory used by the computation.
These ideas had occurred to me earlier in 1955 when Icoined the term"signalizing function", which is nowadays commonly known as"complexity measure.
Blum's speedup theorem shows that for any complexity measure there are computable functions that are not optimal with respect to that measure. .
There is no restriction on the amount of computation timethat can be used, though there may be restrictions on some other complexity measures like alternation.
The detail D C{\displaystyle\mathrm{D}_{\mathsf{C}}}is a complexity measure of concept classes dual to the VC dimension D V C{\displaystyle\mathrm{D}_{{\mathsf{V}}C.
The theorem can be proved by using the Blum axioms without any reference to a concrete computational model, so it applies to time, space, orany other reasonable complexity measure.
Examples===* formula_1 is a complexity measure, if formula_13 is either the time or the memory(or some suitable combination thereof) required for the computation coded by"i.
The best, worst andaverage case complexity refer to three different ways of measuring the time complexity(or any other complexity measure) of different inputs of the same size.
Several important complexity measures can be defined on Boolean circuits, including circuit depth, circuit size, and number of alternations between AND gates and OR gates.
We proposed a novel method for texture characterization based on complexity measures such as lyapunov exponent, hurst exponent and lempel-ziv complexity. .
Other complexity measures used in complexity theory include communication complexity, circuit complexity, and decision tree complexity. .
In computational complexity theory the Blum axioms orBlum complexity axioms are axioms that specify desirable properties of complexity measures on the set of computable functions.
Definitions==A Blum complexity measure is a tuple formula_1 with formula_2 a Gödel numbering of the partial computable functions formula_3 and a computable function: formula_4which satisfies the following Blum axioms.
This already became obvious 15 years ago with the application of a technical standards information directive which prompted a huge increase in national regulations on products in the Single Market, exceeding by far in number,volume and complexity measures introduced at EU level: COM(98) 345.
Other complexity measures are also used, such as the amount of communication(used in communication complexity), the number of gates in a circuit(used in circuit complexity) and the number of processors used in parallel computing.
In order to make the definition more accessible we rephrase the Blum axioms interms of Turing machines: A Blum complexity measure is a function Φ{\displaystyle\Phi} from pairs(Turing machine M{\displaystyle M}, input x{\displaystyle x} for M{\displaystyle M}) to the natural numbers union infinity.
These numbers measure properties of things of different types: Complexity measures how hard it is to describe an object(such as a bitstring), information is how much the uncertainty about the state of an object is reduced by knowing the state of another object or system, and improbability measures how unlikely an event is given a probability distribution.
These numbers measure properties of things of different types: Complexity measures how hard it is to describe an object(such as a bitstring), information measures how close to uniform a random probability distribution is and improbability measures how unlikely an event is given a probability distribution.
We measure complexity and fidelity of data in terms of polygonal count.