Computer Science Stack Exchange is a question and answer site for students, researchers and practitioners of computer science. It's 100% free, no registration required.

Sign up
Here's how it works:
  1. Anybody can ask a question
  2. Anybody can answer
  3. The best answers are voted up and rise to the top

I'm reading Cormen's Introduction to Algorithms 3rd edition, and in examples of Master Method recursion solving Cormen gives two examples

  1. $3T( \frac{n}{4} ) + n\log(n)$
  2. $2T( \frac{n}{2} ) + n\log(n)$

For the first example we have $a=3$ and $b=4$ so $n^{\log_4 (3)}=n^{0.793}$ and Cormen says that if we choose $\epsilon = 0.207$ then $f(n) = n\log(n) = \Omega(n^{\log_4(3) + \epsilon})$

How? As I understand it if $\epsilon = 0.207$ then $\Omega(n^{\log_4(3) + \epsilon})= \Omega(n)$ so we have $n\log(n) = \Omega(n)$ but it's not true; But he proves that $n\log(n) = \Omega( n^{\log_4(3) + \epsilon} )$

And then he proves that for the second case $n\log(n)$ does not apply to masters method 3-rd case the same way as I prove above.

So could somebody explain me in detail how the third case of the master's theorem applies to $3T( \frac{n}{4} ) + n \log(n)$ but not to $2T( \frac{n}{2} ) + n\log(n)$.

share|cite|improve this question
Have you checked any of our other questions about the master theorem (cf master-theorem)? Or our reference question? – Raphael Feb 2 '13 at 13:25
Also, you need to brush up your fundamentals: $O(f) \neq \Omega(f)$. – Raphael Feb 2 '13 at 13:28
up vote 1 down vote accepted

I think you used that method wrong! As mentioned in master theorem case 3. In your case you should use case 3 which uses $\Omega$-notation which describes asymptotic lower bound. So the solution in the book is correct!

You used $O$-notations which is and asymptotic upper bound! Be careful about which cases in master theorem you are using!

To satisfy the second case you should also have $af(n/b) \leq cf(n)$ for some $c < 1$ and large $n$. In your case, $f(n) = n \log(n)$. $a=3$, $b=4$ you should show that

$\qquad 3(n/4 \cdot \log(n/4)) \leq c n \log(n)$.

For large $n$ you could select $1 \geq c \geq 0.75$ which solves your equation.

share|cite|improve this answer
thnx,i didn't notice that it is Big_Omega instead of Big_O, but again how we can proove that for nlog(n) we can find such epsilon that $nlog(n) = \Omega(n^{log_4(3) + ϵ})$ cause when we put 0.2 instead of epsilon $\Omega(n^{log_4(3)+\epsilon} ) = \Omega(n^{0.793+0.2} ) = \Omega(n^1)$ so we need to show that $nlog(n) = Omega(n^1)$. => we need to find ssuch $c$ and $k$ that for all $n >= k$ $0 <= c * n <= n * log(n)$ that means $0 <= c <= log(n)$ but for Big_Omega we need also lim( c / log(n) ) != 0 but it istn't right, there is no such $c$. I know that book is right, where i did mistake – Vahagn Babajanyan Feb 1 '13 at 18:59
@VahagnBabajanyan: please see the completed answer. – Reza Feb 1 '13 at 19:44

First of all, please check what $O$ and $\Omega$ and the other Landau symbols mean; that should remove some of the confusion.

Note that $n \log n \in \omega(n)$, that is $n \log n$ grows properly faster than $n$, asymptotically. This can be seen by

$\qquad \lim_{n \to \infty} \frac{n \log n}{n} = \lim_{n \to \infty} \log n = \infty$.

By the same reasonining, though, $n \log n \in o(n^{1 + \varepsilon})$ for every $\varepsilon \in (0,\infty)$.

Therefore, $n \log n \in \Omega(n^\alpha)$ for all $\alpha \in [0,1]$, so in the example you can choose $\varepsilon$ arbitrarily so that $\varepsilon + \log_4(3) \leq 1$. The authors pick one, namely the largest (up to dropped decimal places).

As has been noted, that does not conclude the proof that case three applies; you still have to check that $a f\left( \frac{n}{b} \right) \le c f(n)$ for some $c>1$ and all $n>n_0$, $n_0$ some natural number.

As for the second example, my above explanation shows that there is no $\varepsilon > 0$ so that $n \log n \in \Omega(n^{\log_2(2) + \varepsilon})$, so case three can not apply here. Note that $o(f) \cap \Omega(f) = \emptyset$ for all $f$.

share|cite|improve this answer
Thanks for the answer. I understand all except how you get $n \log n \in \Omega(n^\alpha)$ equation. If it is possible could you explain it? – Vahagn Babajanyan Feb 2 '13 at 19:56
@VahagnBabajanyan: It's a corollary from $n \log n \in \omega(n)$, using only the definitions of $\omega$ and $\Omega$ as well as the (almost trivial) fact that $n^\alpha \in o(n^\beta)$ for all $\alpha < \beta$. – Raphael Feb 2 '13 at 20:12
I mean for $\Omega$, $\lim_{n \to \infty} \frac{n \log n}{n^{\alpha}} \neq \infty$. Because for $\omega$ it should be infinity, but for $\Omega$ it shouldn't, or i'm mistaken? – Vahagn Babajanyan Feb 2 '13 at 20:18
I mean how can we proove that it is tight bound? – Vahagn Babajanyan Feb 2 '13 at 20:45
Yes, you are mistaken. $\omega(f) \subseteq \Omega(f)$. I don't get what you mean by "tight bound", and what "it" refers to. – Raphael Feb 2 '13 at 21:24

Your Answer


By posting your answer, you agree to the privacy policy and terms of service.

Not the answer you're looking for? Browse other questions tagged or ask your own question.