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Questions tagged [big-o-notation]

Big O Notation is an informal name of the "O(x)" notation used to describe asymptotic behaviour of functions. It is a special case of Landau notation.

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Comparing asymptotic running time of two algorithms $\sqrt n$ and $2^{\sqrt{\log _{2}n}}$

Given two algorithms with their time-complexity $t_a(n)=\sqrt{n}$ and $t_b(n) = 2^{\sqrt{\log _{2}n}}$ and i have to show $t_b(n) = O(t_a(n))$. I´ve made a program to check this statement and it ...
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Why is n log n dominated by n log^2 n?

Does the rule of $n ^ a$ dominate $n ^ b$ if $a > b$ apply here as well? My understanding is that $n \log n$ will be dominated by $n \log ^2 n$ because of $\log$ being raised to the power of $2$.
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Suppose I have something like the following: $f(x) = g(x) + O(x^n)$ And I apply a power $m$ to both sides $f(x)^m = g(x)^m + \cdots + O(x^n)^m$ My question is whether the following is well ...
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Which function grows faster: N Log N or N^(1+ε/√(log N)) [duplicate]

How would you go about solving this problem? I thought about using a limit infinity approach, but got confused and Wolfram Alpha didn't provide any explanation.
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Runtime complexity of a brute force factoring algorithm? (in terms of bits)

Let N be an n bit number. A brute force algorithm factors N by trying to divide N by all of the numbers between 2 and sqrt(N). Given that dividng two n bit integers takes O(n^2) time, what is the ...
159 views

How to prove that ($56n^2+106n+48)(\log(264n^2+200)) = Θ(𝑛^2\log n)$

I understand that essentially we have to prove that $$c_1(n^2\log n)\le (56n^2+106n+48)(\log(264n^2+200)) \le c_2(n^2\log n)\,.$$ I am confused on how to simplify this further? And ...
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How to prove the performance, Big Omega ,of building a binary heap using recursive method is Ω(nlog(n))

We can learn the big-O of building a binary heap using recursive method is O(n log n) from wiki "This approach, called Williams’ method after the inventor of binary heaps, is easily seen to run in O(n ...
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Asymptotic notation and random variables

I have two random variables $X$ and $Y$ and I want to bound the value of one in terms of the other (for now, I don't care about the actual distribution of their values). Suppose that the two ...