Time complexity of this code

Question

I am working on some homework so I cannot post any code. I am working on some code and at this point I have something like this (instead of functions I wrote time complexity):

while(O(n^2)) {
    O(n^4);
    O(n^2);
}

I estimated O's according to nested for-loops I have in functions. My question is what is actually time complexity of this whole thing? I wouldn't mind short explaination either. Thank you!

Answer 1

EDIT: My previous answer was wrong, because I have made a mistake.

Now I understand, that your code can be refactored to

while(run == true)
{
    run = O(n^2);
    O(n^4);
    O(n^2);
}

so each iteration has O(n^4) complexity, because we have a polynomial n^4 + 2*(n^2) and we ditch the lower degree. Now you have to multiply it by number of iteration. For example if you get n iteration you end up with O(n^5). If you always have 1000000000 iterations, you still have O(n^4).

An excellent explanation of Big-O notation is here: What is a plain English explanation of "Big O" notation?

Answer 2

What are you specifically trying to find out? If you want to know how long your code takes to compute, and break this down into specific loops and nested loops then I'd suggest looking into the stopwatch class:

http://msdn.microsoft.com/en-us/library/system.diagnostics.stopwatch(v=vs.110).aspx

Also you can watch the times live using by writing the elapse time of the stopwatches to labels and then calling me.update()