The best big O notation explanation I’ve ever saw I’ve found on… Google Play Market! I was hanging around, looking for the suggested software and, for some reason, I’ve decided to install some educational application for programmers. And here’s what I’ve found…

Big O notation shows, how many steps or memory units will the algorithm use to complete, at its maximum. Here’s an example:

void someAlgorithm(int n) {
  // part 1
  doSomething();

  // part 2
  for (int i = 0; i < 10; i++) {
    doSomething();
  }

  // part 3
  for (int i = 0; i < n; i++) {
    doSomething();
  }

  // part 4
  for (int i = 0; i < n; i++) {
    for (int t = 0; t < n; t++) {
      doSomething();
    }
  }

  return n;
}

Let’s take a look at each of four algorithm parts. Part 1 just calls some function, doSomething(). Let’s assume it takes some constant amount of time to complete, C. The time complexity of calling a function, which uses constant time to complete is O(1). So part 1 will take O(1) time to complete.

Part 2 has a loop, which has exactly 10 iterations, calling doSomething() at each iteration. As we’ve discussed above, this part takes 10 * C (ten calls of doSomething() function, which takes C steps to complete) steps. This is a constant value too, so part 2 of the function myAlgorithm() will have the complexity of O(1).

Part 3 has a loop, whose number of iterations relies on the input parameter of myAlgorithm(), namely n. We do not know, what value the n will take. But as it increases, the steps, needed for this part to complete increases too. So the complexity of this part will be O(n).

Part 4 has two nested loops. As in the previous case, when n increases, the steps needed by this part to complete will increase even faster: for n = 1 it will take exactly C steps to complete (recall: the complexity of doSomething() is C); for n = 2 it will take 2 * 2 * C = 4 * C steps to complete; for n = 10 the amount of steps would be 10 * 10 * C = 100 * C. One can notice that the complexity equals to n * n * C. This is quadratical complexity, denoted as O(n^2).

The final complexity of an algorithm could be calculated by easily adding all those parts’ complexities: O(1) + O(1) + O(n) + O(n^2). But here’s the trick: if the n is relatively small (like, 10 or 100), the difference between O(1) and O(n) is huge (noticeable, at least). But if we say the value of n is insanely large (like, billiards), we may not notice the O(1) is just nothing, compared to O(n). And O(n) is just nothing, when compared to O(n^2). And here comes the rule: total complexity of an algorithm is the maximum needed amount of steps to complete. Just as follows:

  • O(C) = O(1)
  • O(1) + O(n) = O(n)
  • O(n) + O(n^2) = O(n^2)

Here comes the comparison of the known complexities: O(1) < O(log n) < O(n) < O(n*log n) < O(n^m).

But we can measure not time consumption only, using the big O notation. It is also handy for memory complexity measurements.

Here the same rules apply, except of “steps to complete” we use “memory cells allocated”. So we will count the amount of allocated memory. This is mostly used by lists, not by objects and structures (as they always use the same memory amount). Check this out:

struct Moo {
  int a, b, c;
  float d;
};

class Foo {
public:
  int a, b, c;
  float *d;
};

Both Moo and Foo will use the same amount of memory initially (since pointers in C++ are just integer memory addresses’ values and floats use same 4 bytes - just as integers do). But depending on how many memory we will allocate for Foo.d we will get the different values. Consider the continuation of this example below:

int myAlgorithm(int n) {
  // part 1
  Foo *foo = new Foo();

  // part 2
  foo->d = new float[10];

  // part 3
  int *a = new int[n];

  // part 4
  int **b = new int*[n];

  for (int i = 0; i < n; i++) {
    b[i] = new int[15];
  }

  return 0;
}

Here, in part 1, we have just an instance of Foo class, which uses 3 * int + float* = 3 * 1 + 1 = 4 memory cells. As in case with time complexity, this amount is constant, thus it has O(1) memory consumption.

In part 2, however, we extend this amount by placing 10 memory cells into foo.d field, but this does not change much, as foo will use constant memory cells anyway. So, part 2 has memory complexity of O(1) too.

In part 3 we create a new array, and its size depends on function’s argument n, so its memory consumption is O(n).

In part 4 we create a two-dimensional array, whose size is n * 15. We can split its size into two components: O(n) * O(15) = O(n), because 15 is 15 no matter what.

And the total memory complexity of the algorithm is O(1) + O(1) + O(n) + O(n) = O(n).

For even more simple O(*) calculus, replace the + operator with the min operator: memory complexity of myAlgorithm() is max(O(1), O(1), O(n), O(n)) = O(n).