Examples of using Time complexity in English and their translations into Portuguese
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Păun et al. improve the time complexity to On2.
Time complexity: O( log N){\displaystyle O\log N.
There are two kinds of time complexity results.
The time complexity for searching a B-tree is Olog n.
This text presents a study about the time complexity of determining rkg.
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The time complexity of the collision attack, in contrast, is 2n/2.
Then there is a complete algorithm which is μ{\displaystyle\mu}-average time complexity.
The time complexity for searching a balanced ternary search tree is Olog n.
An algorithm is said to take linear time, or O(n)time, if its time complexity is On.
These algorithms have o(mn) time complexity, where m and n are the sizes of the sequences.
To show a lower bound of"T"("n")for a problem requires showing that no algorithm can have time complexity lower than"T""n.
Let us assume that Hf is in this time complexity class, and we will attempt to reach a contradiction.
In addition to performance bounds,computational learning theory studies the time complexity and feasibility of learning.
Here, complexity refers to the time complexity of performing computations on a multitape Turing machine.
An enhanced version of the attack can break 9-round DES with 215.8 chosen plaintexts and has a 229.2 time complexity Biham and others, 2002.
For an n-bit hash,this attack has a time complexity 2n, which is considered too high for a typical output size of n 128 bits.
For dense graphs,it is more efficient to use this simple algorithm which relies on matrix multiplication, since it gets the time complexity down to O("n"2.373), where"n" is the number of vertices.
Relation to time complexity==Say that a certain language, formula_16, belongs to the time-complexity class formula_42 for some function formula_17.
Usually the resource being considered is running time, i.e. time complexity, but it could also be memory or other resource.
There are two kinds of time complexity results: Positive results- Showing that a certain class of functions is learnable in polynomial time. .
Existing methods for recognition of rational quadratic forms have exponential time complexity or use approximation that weaken the result reliability.
In order to avoid exponential time complexity caused by such blow-up, advanced unification algorithms work on directed acyclic graphs(dags) rather than trees.
In this case, the existence of numerous comparison-sorting algorithms having this time complexity, such as mergesort and heapsort, demonstrates that the bound is tight.
The following table gives the time complexity cost of performing various operations on graphs, for each of these representations, with|V| the number of vertices and|E| the number of edges.
For n-state DFAs over a k-letter input alphabet, an algorithm by David Eppstein finds a synchronizing word of length at most 11n3/48+ O(n2),and runs in time complexity On3+kn2.
Suppose A{\displaystyle{\mathcal{A}}} is an algorithm whose time complexity, T: I→ N{\displaystyle T: I\to\mathbb{N}} is polynomial on μ{\displaystyle\mu} average.
Time complexity: O( j+ log N){\displaystyle O(j+\log N)} To report the string Ci,…, Ci+ j- 1, find the node u that contains Ci and weight(u)>= j, and then traverse T starting at node u.
For spanning tree problems, this representation has linear time complexity when applied to network design problems with evolutionary algorithms.
Time complexity is commonly estimated by counting the number of elementary operations performed by the algorithm, supposing that each elementary operation takes a fixed amount of time to perform.
However, when array-based strings are used for longer strings, time complexity and memory use for inserting and deleting characters become unacceptably large.