Runtime $$t$$ of an algorithm as a function of the number of input data. Entered are different possible runtime behaviors using the Big O notation. As can be seen, with increasing number of input data an algorithm with runtime in the order $$\mathcal{O}(n! )$$ is the slowest, slightly faster algorithms have the runtime of the order $$\mathcal{O}(2^n)$$, even faster are the algorithms of the order $$\mathcal{O}(n^2)$$ or $$\mathcal{O}(n)$$, even better $$\mathcal{O}(\log(n))$$. The fastest algorithm with a large number of input data has a constant runtime rate $$\mathcal{O}(1)$$.