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On page 17 on the paper Online Learning with Predictable Sequences, we find a regret of an algorithm equal to

$$ \text{Reg}_T=\frac{R^2}{\eta}+\frac{\eta}{2}\sigma^2(T+logT+1) $$ where $T$ is the entire time, $\eta$ is learning rate, $R$ is constant, and $\sigma^2$ is the variance of data. The above using arithmetic mean-geometric mean inequality can be lower bounded by

$$ \sqrt{2}R\sigma\sqrt{T+logT+1}\leq \frac{R^2}{\eta}+\frac{\eta}{2}\sigma^2(T+logT+1) $$

why the left hand side is $\tilde{{O}}(\sigma\sqrt{T})$?

According to $\tilde{{O}}$ vs $O$, $\tilde{{O}}$ ignores logarithmic factors that means when $f(n) \in \tilde{O}(g(n))$ there exist a $k$ such that $f(n) \in O(g(n)\log^k g(n))$, now I have $f(T)=\sqrt{2}R\sigma\sqrt{T+logT+1}$, why I can write it $O(\sigma\sqrt{T}log^kT)=\tilde{O}(\sigma\sqrt{T})$? and what is my $k$?

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What is $\tilde O$? There are two different definitions.

  • According to exercise 3.5 of Introduction to Algorightms by Corman et al., $\tilde O$ can mean $O$ with logarithmic factors ignored: $$\tilde O(g(n))= \{f(n): \text{there exist nonnegative constant } k \\ \text{ and positive constants } c, k, \text{ and }n_0 \text{ such that }\\ 0\le f(n)\le cg(n)(\log n)^k\text{ for all }n\ge n_0\}$$
  • According to Wikipedia entry, $f(n) = \tilde O(g(n))$ is shorthand for $f(n) = O(g(n)\left(\log g(n)\right)^k)$ for some $k$.

For any value $x>0$, $x^0=1$. If we take $k=0$ simply, we can see that for either definition, $$f(T)=\sqrt{2}R\sigma\sqrt{T+\log T+1}=O(\sigma \sqrt{T}) =O(\sigma \sqrt{T}\,1)=\tilde{O}(\sigma\sqrt{T})$$


Here is the explanation for the second equality in the above equation.

$\log T=o(T) \Rightarrow T+\log T+1=\Theta(T) \Rightarrow \sqrt{T+\log T+1}=\Theta(\sqrt T)$

W can also proceed more rigorously, using L'Hôpital's rule once.

$$\lim_{T\to\infty}\frac{\sqrt{T+\log T+1}}{\sqrt T} = \sqrt{\lim_{T\to\infty}\frac{T+\log T+1}{T}} = \sqrt{\lim_{T\to\infty}\frac{1+\frac1T}{1}}=1$$

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  • $\begingroup$ my question is why $\tilde{\mathcal{O}}$ not $\mathcal{O}$ ? Could you address that? $\endgroup$ – Saeed Dec 27 '18 at 15:56
  • $\begingroup$ I revised the statement to elaborate my question clearly, please take a look. $\endgroup$ – Saeed Dec 27 '18 at 16:20
  • $\begingroup$ $\textbf{Wikipedia entry:}$ 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 $n\log k$ is always $o(n\epsilon)$ for any constant k and any $\epsilon > 0$). $\endgroup$ – Sagnik Dec 29 '18 at 8:18
  • $\begingroup$ @Sagnik: I understand that. What I do not understand is why $O(T+\log T)=O(T\log T)$? $\endgroup$ – Saeed Dec 30 '18 at 18:52
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    $\begingroup$ @Apass.Jack : But it is not in your answer anymore. You have revised your answer, and I want the accepted answer stands alone. $\endgroup$ – Saeed Dec 30 '18 at 20:22

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