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If you had an algorithm-related course as I did, you’ve probably heard of the term Big-O notation. It’s one of the most fundamental tools to analyze the cost of an algorithm before implementing it.

Big-O notation describes the complexity of your code using algebraic terms, but let’s take a simpler approach today when analyzing five typical Big-Os.

O(1) - constant complexity

No matter the input data set size, it will always take constant time (or space) to execute.

int constantExample(int x, int y) {     return x + y;}

O(log(n)) - logarithmic complexity

Generally reduces the problem size with each iteration.

int logarithmicExample(int n) {     while (n > 1) {          n = n / 2;     }}

O(n) - linear complexity

It increases linearly and in direct proportion to the size of the input.

boolean linearExample(IList elements, int value) {     foreach (var element in elements) {          if (element == value) return true;     }     return false;}

O(n2) - quadratic complexity

It’s directly proportional to the square of the size of the input.

void quadraticExample(int array[], int size) {     for (int i = 0; i < size; i++) {          for (int j = 0; j < size; j++) {               print(array[i], array[j]);          }     }}

O(2n) - exponential complexity

Its growth doubles with each addition to the input data set. A great example is the Fibonacci recursive function.

int fibonacci(int number) {     if (number <= 1) return number;     return fibonacci(number - 1) + fibonacci(number - 2);}

💡 Do you want a Big-O cheatsheet?Head over here! You’ll find complexities for data structures and algorithms in the best, average, and worst-case scenarios.