# Computing – Big O Complexity when iterating in 3 dimensions

The first thing you should study in detail about the asymptotic analysis and the amortized analysis.

If we consider the asymptotic analysis for its scenario with `bx = 10, for = 1000, bz = 1000` as your friend suggested if you consider an input set `by` Y `bz` then relatively `bx` It has a small value that can be considered constant. However, in your case, all these values ​​are constant and their execution time can be considered as constant. `O (1)`.

When calculating the asymptotic upper limit of Big-O & # 39; O & # 39 ;, we ignore the constants.

However, if you only use values ​​such as:
`bx = 1000000000, for = 100000000000, bz = 100000000000`
where the input size of `bx` Also relatively higher, therefore, by definition of big-o

That is, f (x) = O (g (x)) if and only if there is a positive real number c and a real number x & # 39; such that
f (x) <= c g(x) for all x > X & # 39;

can indicate the complexity for `O (bx * by * bz)`.

To summarize in the first case you used the same value. `north` to calculate all the loops, but in another case, it used three different input sizes, so its complexity will always be `O (bx * by * bz)` and if any or more value of `bx`, `by` or `bz` it's constant so you can skip it