I am trying to figure out the solution of the recurrence relation $$T(n) = 5T(n/3) + n \log n$$ using the Master Method.

I am guessing that $f(n) = O(n^{1.46 - \varepsilon})$, but I am confused in the part that $\frac{n \log n}{n^{1.46}}$ must be polynomially smaller. If something is polynomially smaller does it mean it can not be bounded either from below or from above (or both)?

  • $\begingroup$ Is the $-\varepsilon$ in the exponent? If not, then $\mathcal{O}(n^{1,46} - \varepsilon)$ is the same as $\mathcal{O}(n^{1,46})$. $\endgroup$
    – Nathaniel
    Commented Feb 21, 2021 at 12:44
  • $\begingroup$ it is in the exponent $\endgroup$
    – Diana
    Commented Feb 21, 2021 at 12:48
  • $\begingroup$ @Steven when I want to show that something is polynomially larger I find polynomials and bound the expression from below and above. Does it mean that in the case I want to show it is polynomially smaller I can not bound it from below by polynomial? $\endgroup$
    – Diana
    Commented Feb 21, 2021 at 13:48
  • $\begingroup$ If you want to show that $f(n)$ is polynomially larger than $g(n)$ you just need to find a lower bound to $f(n)$ of $\Omega( g(n) n^\varepsilon)$ for some $\varepsilon > 0$. No need to bound $f(n)$ from above. When you need to show that $f(n)$ is polynomially smaller than $g(n)$ you only need to bound $f(n)$ from above with some function in $O(g(n) n^{-\varepsilon})$, for some $\varepsilon > 0$. No need to bound $f(n)$ from below. By the way my previous comment was incorrect (I had calculated the wrong value for $\log_3 5$) but I was just making explicit that $\log_3 5 \neq 1.46$. $\endgroup$
    – Steven
    Commented Feb 21, 2021 at 13:54

3 Answers 3


When $f(n)$ is said to be polynomially smaller than $g(n)$ it just means that there is some constant $\varepsilon > 0$ such that $f(n) \in O(g(n) n^{-\varepsilon})$. This is not about being able to bound $f(n)$ from above and/or below but about the growth rate of $f(n)$ when compared to $g(n)$. In other words you want $\frac{g(n)}{f(n)}$ to grow at least as fast as some polynomial/root of $n$.

In your particular case, $n \log n$ is indeed polynomially smaller than $n^{1.46}$, and this follow from the fact that $n \log n \in O(n^\alpha)$ for any $\alpha >1$.

A a concrete choice of $\varepsilon$, you can pick $\varepsilon=0.1$ yielding $n \log n \in O(n^{1.46-0.1}) = O(n^{1.36})$.

To see this you can consider the limit: $$ \lim_{n \to \infty} \frac{n \log n}{n^{1.36}} = \lim_{n \to \infty} \frac{\log n}{n^{0.36}} = \lim_{n \to \infty} \frac{1}{0.36 \cdot n^{0.36}} = 0, $$ where we used L'Hôpital's rule.

In the above, the choice $\varepsilon = 0.1$ was just a convenient value that works, but any choice of $\varepsilon \in (0, 0.46)$ suffices.


It is well known, that for any fixed number $a>0$ we have $\log(n)=O(n^a)$.

Then, for a small enough epsilon, that is for $\epsilon<0.46$ we can define $a=0.46-\epsilon>0$. Therefore, $log(n)=O(n^{0.46-\epsilon})$. Multiply by $n$ both sides and you get the solution.

In fact, $\log(n)=o(n^a)$, and therefore not only $n\log(n)=O(n^{1.46-\epsilon})$, but also $n\log(n)=o(n^{1.46-\epsilon})$ which means that $n\log(n)$ grows substantually slower than $n^{1.46-\epsilon}$.

Therefore, $n\log(n)$ cannot be bounded from below by $n^{1.46-\epsilon}$ but can be bounded from above.

  • $\begingroup$ What's the difference between this and my answers with the minus given? $\endgroup$
    – zkutch
    Commented Feb 21, 2021 at 13:01

Answering title: $n\lg n = O(n^{1,46 - \varepsilon})$, when $\varepsilon \lt 0.46$.

  • $\begingroup$ Explicit and well-founded criticism is welcome, especially when what is written is true. $\endgroup$
    – zkutch
    Commented Feb 21, 2021 at 12:59
  • $\begingroup$ The OP also wanted to know why. Not only if its true or false. $\endgroup$
    – nir shahar
    Commented Feb 21, 2021 at 13:02
  • $\begingroup$ Transferring a term over an inequality sign is not an explanation, as well as passing one's own opinion as someone else's. $\endgroup$
    – zkutch
    Commented Feb 21, 2021 at 13:06

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