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What does tilde mean, in big-O notation?
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whatmeanbigtildenotationdoes
Problem
I'm reading a paper, and it says in its time complexity description that time complexity is $\tilde{O}(2^{2n})$.
I have searched the internet and wikipedia, but I can't find what this tilde signifies in big-O/Landau notation. In the paper itself I have also found no clue about this. What does $\tilde{O}(\cdot)$ mean?
I have searched the internet and wikipedia, but I can't find what this tilde signifies in big-O/Landau notation. In the paper itself I have also found no clue about this. What does $\tilde{O}(\cdot)$ mean?
Solution
It's a variant of the big-O that “ignores” logarithmic factors:
$$f(n) \in \tilde O(h(n))$$
is equivalent to:
$$ \exists k : f(n) \in O \!\left( h(n)\log^k(h(n)) \right) $$
From Wikipedia:
Essentially, it is big-$O$ notation, ignoring logarithmic factors because the growth-rate effects of some other super-logarithmic function indicate a growth-rate explosion for large-sized input parameters that is more important to predicting bad run-time performance than the finer-point effects contributed by the logarithmic-growth factor(s). This notation is often used to obviate the “nitpicking” within growth-rates that are stated as too tightly bounded for the matters at hand (since $\log^k n$ is always $o(n^\varepsilon)$ for any constant $k$ and any $\varepsilon > 0$).
$$f(n) \in \tilde O(h(n))$$
is equivalent to:
$$ \exists k : f(n) \in O \!\left( h(n)\log^k(h(n)) \right) $$
From Wikipedia:
Essentially, it is big-$O$ notation, ignoring logarithmic factors because the growth-rate effects of some other super-logarithmic function indicate a growth-rate explosion for large-sized input parameters that is more important to predicting bad run-time performance than the finer-point effects contributed by the logarithmic-growth factor(s). This notation is often used to obviate the “nitpicking” within growth-rates that are stated as too tightly bounded for the matters at hand (since $\log^k n$ is always $o(n^\varepsilon)$ for any constant $k$ and any $\varepsilon > 0$).
Context
StackExchange Computer Science Q#63264, answer score: 38
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