So I can picture what an algorithm is that has a complexity of n^c, just the number of nested for loops.
for (var i = 0; i < dataset.len; i++ {
for (var
Algorithms with running time O(2^N) are often recursive algorithms that solve a problem of size N by recursively solving two smaller problems of size N-1.
This program, for instance prints out all the moves necessary to solve the famous "Towers of Hanoi" problem for N disks in pseudo-code
void solve_hanoi(int N, string from_peg, string to_peg, string spare_peg)
{
if (N<1) {
return;
}
if (N>1) {
solve_hanoi(N-1, from_peg, spare_peg, to_peg);
}
print "move from " + from_peg + " to " + to_peg;
if (N>1) {
solve_hanoi(N-1, spare_peg, to_peg, from_peg);
}
}
Let T(N) be the time it takes for N disks.
We have:
T(1) = O(1)
and
T(N) = O(1) + 2*T(N-1) when N>1
If you repeatedly expand the last term, you get:
T(N) = 3*O(1) + 4*T(N-2)
T(N) = 7*O(1) + 8*T(N-3)
...
T(N) = (2^(N-1)-1)*O(1) + (2^(N-1))*T(1)
T(N) = (2^N - 1)*O(1)
T(N) = O(2^N)
To actually figure this out, you just have to know that certain patterns in the recurrence relation lead to exponential results. Generally T(N) = ... + C*T(N-1)
with C > 1
means O(x^N). See:
https://en.wikipedia.org/wiki/Recurrence_relation