I am being asked to order functions by growth rate and I didn't make much of this case until I started thinking about it more and more.

Let's postulate that we have some function

public void doStuff(int N) {
    final int k = 1000000;
    int m = k/N;  

    for (int i = 0; i < m; i++) {
        // Do stuff

At first glance, it does seem to me that for larger and larger inputs of $N$, this function will execute faster and faster. However, in regards to growth of functions, are we not concerned with what happens when $$\lim_{N\to\infty} \frac{1}{N}$$

The method I posted above does execute faster for larger inputs of $N$, but only up to a certain point. After that point, the run time would be $0$.

With a function that grows at a rate of $\frac{1}{N}$, wouldn't its running time be limited to be just as fast as a constant time algorithm? It doesn't make sense to me that for large enough values of $N$, a function can have a run time of $0$ when it would still do something.

  • $\begingroup$ I'm not sure to understand. Do you mean for (int i = 0; i < m; i++)? Otherwise the complexity is linear $\endgroup$
    – Mariuslp
    Feb 3 '17 at 0:14
  • $\begingroup$ @Mariuslp Yes, I fixed it. I'm sorry about that. $\endgroup$ Feb 3 '17 at 0:15

The code you gave has a constant running time, which is typically considered the fastest possible for an algorithm. The point is that just starting the algorithm and checking whether you want to terminate immediately takes, say, $c$ time units. The running time of an algorithm is then $c + f(n)$ for some function $f$. Hence, if $f$ is decreasing, the constant $c$ will dominate the running time asymptotically. In particular, you can never have runtime $0$.

On the other hand, if you only consider functions, you can of course have a function with growth $1/N$, and such function "grows" strictly slower than a constant function, i.e., $1/N \in o(1)$. You can check this using the formal definition of the little-o notation.


First remark: when $N > k$, your function isn't doing "real" stuff any more: your loop starts for $i = 0$ and stops when $i \ge m = 0$. So, you never enter the loop.

Second, if you're going to rank using big-Oh complexity, then you can actually use the notation $O(\frac 1N)$. Especially, $O(\frac 1N)$ is a $O(1)$. It's unusual, as most algorithms take actually longer to execute for high N, but it's not mandatory. Let's see why in the following random example.

Assume I'm an evil hacker of unknown nationality who wants to hack US presidential elections: my goal is to make the number of votes for the orange candidate high enough for him to win the state. If the other candidate collected $1\ 000\ 000$ votes in a state then my orange candidate will need as much plus one.

  • If he only has $N = 10$ votes. Then I have to forge $999\ 991$ votes, it's quite long.
  • On the other hand, if the orange candidates has already $N = 10\ 000\ 000$ votes in this state, I don't need to do anything: my algorithm is faster for high $N$.
  • $\begingroup$ I'm sorry, perhaps I am misunderstanding you, but are you saying that $\frac{1}{N}$ and a constant time function, $1$ have the same growth rate? $\endgroup$ Feb 3 '17 at 2:03
  • $\begingroup$ Oh, and I would like to know if what you said about the function not doing anything when $N > k$ invalidates the notion that you can have a function with a growth rate of $\frac{1}{N}$ $\endgroup$ Feb 3 '17 at 2:07
  • 2
    $\begingroup$ @Cherry_Developer In my understanding, $1/N$ and $1$ don't have the same growth rate; $1/N$ is in $O(1)$, but not the other way around. While you can of course have a function with growth rate $1/N$ (e.g, the function $N \mapsto 1/N$), there cannot be an algorithm with that running time. See my answer for an explanation. $\endgroup$ Feb 3 '17 at 10:50
  • $\begingroup$ @ChristianMatt: I agree with the first part of your comment, but I think that on a pure theoretical approach, then a $O(1/N)$ growth rate is valid. For instance, if you consider the complexity being the number of loops, then it's proportional to 1/N; but if you consider the number of basic operations (addition, multiplication, division, comparison), then even for arbitrarily big N we have the two operations: m = k/N and i < m. Then of course the complexity is constant time. $\endgroup$
    – Mariuslp
    Feb 3 '17 at 11:05

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