Sorting functions by asymptotic growth

Question

Assume I have a list of functions, for example

$\qquad n^{\log \log(n)}, 2^n, n!, n^3, n \ln n, \dots$

How do I sort them asymptotically, i.e. after the relation defined by

$\qquad f \leq_O g \iff f \in O(g),ドル

assuming they are indeed pairwise comparable (see also here)? Using the definition of $O$ seems awkward, and it is often hard to prove the existence of suitable constants $c$ and $n_0$.

This is about measures of complexity, so we're interested in asymptotic behavior as $n \to +\infty,ドル and we assume that all the functions take only non-negative values ($\forall n, f(n) \ge 0$).

• Raphael: Since the OP never came back, I'm removing the localised stuff and make a reference question out of this.

Answer 1

If you want rigorous proof, the following lemma is often useful resp. more handy than the definitions.

If $c = \lim_{n\to\infty} \frac{f(n)}{g(n)}$ exists, then

• $c=0 \qquad \ ,円\iff f \in o(g)$,
• $c \in (0,\infty) \iff f \in \Theta(g)$ and
• $c=\infty \quad \ \ \ \iff f \in \omega(g)$.

With this, you should be able to order most of the functions coming up in algorithm analysis1. As an exercise, prove it!

Of course you have to be able to calculate the limits accordingly. Some useful tricks to break complicated functions down to basic ones are:

• Express both functions as $e^{\dots}$ and compare the exponents; if their ratio tends to 0ドル$ or $\infty$, so does the original quotient.

• More generally: if you have a convex, continuously differentiable and strictly increasing function $h$ so that you can re-write your quotient as

  $\qquad \displaystyle \frac{f(n)}{g(n)} = \frac{h(f^*(n))}{h(g^*(n))}$,

  with $g^* \in \Omega(1)$ and

  $\qquad \displaystyle \lim_{n \to \infty} \frac{f^*(n)}{g^*(n)} = \infty$,

  then

  $\qquad \displaystyle \lim_{n \to \infty} \frac{f(n)}{g(n)} = \infty$.

  ^{See here for a rigorous proof of this rule (in German).}

• Consider continuations of your functions over the reals. You can now use L'Hôpital's rule; be mindful of its conditions2!

• Have a look at the discrete equivalent, Stolz–Cesàro.

• When factorials pop up, use Stirling's formula:

  $\qquad \displaystyle n! \sim \sqrt{2 \pi n} \left(\frac{n}{e}\right)^n$

It is also useful to keep a pool of basic relations you prove once and use often, such as:

• logarithms grow slower than polynomials, i.e.

  $\qquad\displaystyle (\log n)^\alpha \in o(n^\beta)$ for all $\alpha, \beta > 0$.

• order of polynomials:

  $\qquad\displaystyle n^\alpha \in o(n^\beta)$ for all $\alpha < \beta$.

• polynomials grow slower than exponentials:

  $\qquad\displaystyle n^\alpha \in o(c^n)$ for all $\alpha$ and $c > 1$.

---

It can happen that above lemma is not applicable because the limit does not exist (e.g. when functions oscillate). In this case, consider the following characterisation of Landau classes using limes superior/inferior:

With $c_s := \limsup_{n \to \infty} \frac{f(n)}{g(n)}$ we have

• 0ドル \leq c_s < \infty \iff f \in O(g)$ and
• $c_s = 0 \iff f \in o(g)$.

With $c_i := \liminf_{n \to \infty} \frac{f(n)}{g(n)}$ we have

• 0ドル < c_i \leq \infty \iff f \in \Omega(g)$ and
• $c_i = \infty \iff f \in \omega(g)$.

Furthermore,

• 0ドル < c_i,c_s < \infty \iff f \in \Theta(g) \iff g \in \Theta(f)$ and
• $ c_i = c_s = 1 \iff f \sim g$.

---

Check here and here if you are confused by my notation.

---

1 Nota bene: My colleague wrote a Mathematica function that does this successfully for many functions, so the lemma really reduces the task to mechanical computation.

2 See also here.

• Raphael: @Juho Not publicly, afaik, but it's elementary to write yourself; compute Limit[f[n]/g[n], n -> Infinity] and perform a case distinction.

Answer 2

Another tip: sometimes applying a monotone function (like log or exp) to the functions makes things clearer.

• Shaull: This should be done carefully: 2ドルn\in O(n),ドル but 2ドル^{2n}\notin O(2^n)$.
• Raphael: Seconded. The "apply monotone function" thing seems to be some kind of folklore which does not work in general. We have been working on sufficient criteria and have been come up with what I posted in the latest revision of my answer.

Answer 3

Skiena provides a sorted list of the dominance relations between the most common functions in his book, The Algorithm Design Manual:

$$n!\gg c^n \gg n^3 \gg n^2 \gg n^{1+\epsilon} \gg n \lg n \gg n \gg n^{1/2}$$ $$ \gg \lg^2n \gg \lg n \gg \frac{\lg n}{\lg\lg n} \gg \lg\lg n \gg \alpha(n) \gg 1$$

Here $\alpha(n)$ denotes the inverse Ackermann function.

• Raphael: That's an oddly specific list. Many of the relations (whatever $\gg$ means exactly) can be summarised to a handful of more general lemmata.
• Robert S. Barnes: It's his notation for a dominance relation.

Answer 4

Tip: draw graphs of these functions using something like Wolfram Alpha to get a feeling for how they grow. Note that this is not very precise, but if you try it for sufficiently large numbers, you should see the comparative patterns of growth. This of course is no substitute for a proof.

E.g., try: plot log(log(n)) from 1 to 10000 to see an individual graph or plot log(log(n)) and plot log(n) from 1 to 10000 to see a comparison.

• Raphael: Should we really recommend vodoo?
• Tsuyoshi Ito: +1 for suggesting to draw graphs of the functions, although the linked graphs are rather confusing unless you know what they mean.
• gnasher729: Take a graph as a hint what you might want to prove. That hint may be wrong of course.

Answer 5

I suggest proceeding according to the definitions of various notations. Start with some arbitrary pair of expressions, and determine the order of those, as outlined below. Then, for each additional element, find its position in the sorted list using binary search and comparing as below. So, for example, let's sort $n^{\log\log n}$ and 2ドル^n,ドル the first two functions of n, to get the list started.

We use the property that $n = 2^{\log n}$ to rewrite the first expression as $n^{\log\log n} = (2^{\log n})^{\log\log n} = 2^{\log n\log\log n}$. We could then proceed to use the definition to show that $n^{\log\log n} = 2^{\log n\log\log n} \in o(2^n),ドル since for any constant $c > 0,ドル there is an $n_0$ such that for $n \geq n_0,ドル $c(n^{\log\log n}) = c(2^{\log n\log\log n}) < 2^n$.

Next, we try 3ドル^n$. We compare it to 2ドル^n,ドル the largest element we have placed so far. Since 3ドル^n = (2^{\log 3})^n = 2^{n\log3},ドル we similarly show that 2ドル^n \in o(3^n) = o(2^{n \log 3})$.

Etc.

Answer 6

Here a list from Wikipedia, The lower in the table the bigger complexity class; $$ \begin{array}{|l|l|} \hline Name & \text{Running Time} \\ \hline \text{Constant time} & \mathcal{O}(1) \\ \text{Inverse Ackermann time} & \mathcal{O}(a(n)) \\ \text{Iterated logarithmic time} & \mathcal{O}(\log^*n) \\ \text{Log-logarithmic} & \mathcal{O}(n \log n) \\ \text{Logarithmic time} & \mathcal{O}(\log n) \\ \text{Polylogarithmic time} & poly(\log n) \\ \text{Fractional power} & \mathcal{O}(n^c) ,\text{where } 0<c<1 \\ \text{Linear time} & \mathcal{O}(n) \\ \text{"n log star n" time} & \mathcal{O}(n \log^* n) \\ \text{Quasilinear time} & \mathcal{O}(n \log n) \\ \text{Quadratic time} & \mathcal{O}(n^2) \\ \text{Cubic time} & \mathcal{O}(n^3) \\ \text{Polynomial time} & poly(n) = 2^{\mathcal{O}(\log n)} \\ \text{Quasi-polynomial time} & 2^{\mathcal{O}(poly(\log n))} \\ \text{Sub-exponential time (first definition)} & \mathcal{O}(2^{n^{\epsilon}}), \epsilon >0 \\ \text{Sub-exponential time (second definition)} & 2^{\mathcal{o}(n)}\\ \text{Exponential time(with linear exponent)} & 2^{\mathcal{O}(n)} \\ \text{Exponential time} & 2^{poly(n)} \\ \text{Factorial time} & \mathcal{O}(n!) \\\hline \end{array} $$

note : $poly(x) = x^{\mathcal{O}(1)}$

• Raphael: Also, interesting how the table suggests that 2ドル^{n \log n} \in o(n!)$. While the table you link to is somewhat accurate, the one linked there (and which you copied) is about complexity classes, which is not a helpful thing to mix in here. Landau notation is not about "time".
• kelalaka: I put this so the name of the complexity classes can be talked directly here. Yes, Landau is more about a specific type of algorithm in Cryptography.
• 喜欢算法和数学: I object to some of @Raphael's views. I have been a mathematician and an instructor for many years. I believe, apart from proving those things, a big table like this increases people's intuition easily and greatly. And the names of the asymptotic classes help people remember and communicate a lot.
• Raphael: @Apass.Jack In my teaching experience, when given a table many students will learn it by heart and fail to order wrt any function not in the table. Note how that effect seems to account for many of questions regarding asymptotic growth that land on this site. That said, of course we'll use lemmata implied by the table if it makes proofs easier, but that comes after learning how to proof the table. (To emphasize that point, people who come here don't need help reading stuff off a table. They need help proving relations.)
• Raphael: @kelalaka "Yes, Landau is more about a specific type of algorithm in Cryptography." -- that doesn't even make sense. Landau notation is a shorthand to describe properties of mathematical functions. It has nothing to do with algorithms let alone cryptography, per se.