When discussing the running time of an algorithm, the three general types of functions that represent asymptotic bounds on the running time are polynomial, logarithmic and exponential. For this problem, a polynomial function is a function of the form $f(x)={ a }_{ n }{ x }^{ n }+{ a }_{ n - 1 }{ x }^{ n - 1 }+...+{ a }_{ 1 }x+{ a }_{ 0 }$, where $n>0$ and ${ a }_{ n }>0$. A logarithmic function is of the form $f(x)=\log _{ b }{ x } $, where $b>1$. An exponential function is of the form $f(x)={ b }^{ x }$, where $b>1$.

Using big O notation, meaning that if $f(x)=O(g(x))$, then $f(x)\le c\times g(x)$ for all $x\ge { x }_{ 0 }$, where $c$ is a constant greater than zero, list the three types of functions in ascending order of their growth rate.

This means if $f(x)$ comes before $g(x)$ in the list, then $f(x)=O(g(x))$.