This is an example in my lecture notes. Is this function with time complexity $O(n \log n)$?. Because the worst case is the funtion goes into else branch, and 2 nested loops with time complexity of $\log n$ and $n$, so it is $O(n \log n)$. Am I right?

int j = 3;
int k = j * n / 345;
if(k > 100){
    System.out.println("k: " + k);
    for(int i=1; i<n; i*=2){
        for(int j=0; j<i; j++){

4 Answers 4


Time complexity of mentioned algorithm is $O(1)$, because for $K>100$ you have a constant operation (println), and you know: $j=3,k = 3 \cdot n / 345 \implies 100 = 3\cdot n / 345 \implies n=11500$, means for $n\ge 11500$ your algorithm has a constant running time (also other part is constant, because just for $n<11500$ will be called).

For being more clear take a look at this question.

  • $\begingroup$ maybe just to complete your answer, you should mention that when talking about time complexity, we only care of what happens when $n\to\infty$. Or you can link to one of the questions dealing with complexities, like this one $\endgroup$
    – Ran G.
    Commented Jun 26, 2012 at 12:01
  • $\begingroup$ @RanG. You are absolutely right, Actually I thought to write a formal definition of Big-Oh, but I thought everyone knows this, actually as you it's enough to find $n_0$ for initial value, $c_0$ for constant coefficient. I determined $n_0$, but I didn't mentioned $c_0$ (seems it's clear), but yes I'll link it to Raphael's answer (in all I'm lazy in writing). $\endgroup$
    – user742
    Commented Jun 26, 2012 at 12:12

EDIT: As pointed out by Saeed Amiri, this is actually $O(1)$, since for asymptotically large $n$, the else branch isn't actually taken; the if part is executed, which is trivially constant time. The rest of this answer, which I leave for reference, would be correct if, for instance, the condition were k < 100. Sorry for the mix-up.

The time-complexity is essentially going to be on the order of the number of times that $k$ is incremented in the nested for loop. There is some extra stuff going on, but if you think about it, that's just playing with constant factors. How many times will $k$ be incremented?

When $i = 1$, $k$ is incremented once. When $i = 2$, $k$ is incremented two additional times. When $i = x$, $k$ is incremented $x$ additional times. Let us now assume that $n = 2^m + 1$. Then the last iteration of the inner loop will cause k to be incremented $2^m$ times.

$k$ is incremented a grand total of $1 + 2 + ... + 2^m$ times, or $2^{(m+1)} - 1$ times. Recall that `$n = 2^m + 1$. So $n - 1 = 2^m$, and we have that $k$ is incremented $2(n - 1) - 1$ times in total.

$k$ is incremented a number of times that is linear in $n$; ergo, this is all $O(n)$.

  • $\begingroup$ -1 Time complexity is independent from the for loop. (See my answer, and try it yourself). $\endgroup$
    – user742
    Commented Jun 26, 2012 at 9:24
  • 1
    $\begingroup$ @SaeedAmiri Oops, you're right. I sort of honed in on the else part without checking the precondition stuff. Editing answer to reflect this. $\endgroup$
    – Patrick87
    Commented Jun 26, 2012 at 15:11
  • $\begingroup$ Yeah, now your answer is correct. $\endgroup$
    – user742
    Commented Jun 27, 2012 at 12:57

Although the comments about the if/else branches are all correct, I would say the answer is O(log n). The reason is that

System.out.println("k: " + k);

involves conversion of an integer to string output, and this will be O(log n) in the general case (just to print out each digit, even if a lookup table were used).

Not sure if that was a trick part of the question or not...


Let's see:

int j = 3; takes constant time O(1).

int k = j * n / 345 takes some logarithm time function of j and n variables

if (k > 100) takes constant time O(1).

System.out.println("k: " + k); takes logarithm time function of k

for (int i=1; i<n; i*=2) takes logarithm time function of n, Θ(log(n)) to be exact, because if t is the number of iterations of this for loop then the value of i can be expressed as: i=2t-1, if t=1 in the first iteration, so the loop continues as long as 2t-1 < n, where n is not changing.

In calculus, if 2t-1 < n then t-1 < log2(n)

And if t-1 < log2(n) then t < log2(n)+1

And if in each iteration, t is incremented by 1, we can see that this for loop really takes Θ(log(n)) time, if the running time complexity of the code inside this for loop is constant, i.e. O(1) of course!

Inside this for loop, there is another for loop:

for (int j=0; j<i; j++) k++;

Let's analysis this:

k++; takes constant time, i.e. O(1) time.

So it's interesting to analysis running time complexity of the inner for loop then.

Let's see:

According to code of this inner for loop, it appears that there are i iterations in this inner for loop, so it's running time is Θ(i), not just O(i), because it doesn't break in the middle, but remember that i < n, because of the outer for loop, so even though in the beginning it takes 1 iteration when i=1, 2 iterations when i=2, 4 iterations when i=4, 8 iterations when i=8... and etc, because i doubles itself in the end of the outer for loop in the line i*=2, so in total the execution is 1+2+4+8+... iterations but until i≥n so the maximum possible number of iterations in this inner for loop is when i=n-1 in terms of worst case, so if in the last execution of the inner for loop, it ran n-1 iterations, so before that it ran (n-1)/2 iterations and before that it ran (n-1)/4 iterations and before that it ran (n-1)/8 iterations... so in total the execution was:

n-1+(n-1)/2+(n-1)/4+(n-1)/8... = (n-1)(1 + 1/2 + 1/4 + 1/8...) = (n-1)(2) = 2n-2 = Θ(n)

Recall that 1 + 1/2 + 1/4 + 1/8...=2 is well known sum of geometric sequence.

Recall that the outer for loop takes Θ(log(n))

And the inner for loop takes Θ(n).

And the running time complexity of for loop inside for loop is the running time complexity of the outer for loop multiplied by the running time complexity of the inner for loop, thus it takes Θ(nlogn) running time.

So in summary, the running time of this function is Θ(nlogn).

Hope that this answers well your question and teaches you well how to analysis running time complexity of algorithms and functions.

  • $\begingroup$ Why my answer was quickly down voted? $\endgroup$ Commented Jul 26, 2017 at 21:26
  • $\begingroup$ Does my analysis is wrong? $\endgroup$ Commented Jul 26, 2017 at 21:34
  • $\begingroup$ "int k = j * n / 345 takes some logarithm time function of j and n variables". You're using the logarithmic time cost per operation on numbers, but usually the constant time cost per operation is used, as was intended by the question also. In addition, your analysis of the 2 loops is wrong, since j only runs until i in the inner loop. O(n), as mentioned by somebody else, is correct. $\endgroup$ Commented Jul 27, 2017 at 20:50
  • $\begingroup$ Okay, I agree with you. $\endgroup$ Commented Jul 27, 2017 at 21:00

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