Skip to main content

How many symbols can $f'(x)$ have if $f$ has $n$ symbols?

10 May 2022

The other day SMBC put up a lovely comic which did a great job nerdsniping me. I knew that I wanted to try to solve it as soon as I saw it, but I didn’t have the time for a little while (it’s midterm season and I had grading to do). It’s a cute problem, and I want to share my solution with all of you! First, here’s the comic that started it all:

Now, my old advisor (Klaus Sutner) used to say that whenever you’re faced with a problem, you can hack or you can think, but you can’t do both. Today Multiple weeks ago I was in more of a hacking mood, so I wrote up some haskell code to just try all the “reasonable” functions I could think of. By this, of course, I mean the elementary functions.

There’s an obvious recursive way to build up the elementary functions (which you should think of as those functions which might show up in a calculus class):

We can formalize this with a datatype1

Obviously this list, while exhausting the elementary functions, is still somewhat arbitrary. For instance, $\sec$ is nowhere on this list, but we can build it using the functions that are on this list2. Conversely, we added a builtin function for $\tan$, even though we can express it using $\sin$ and $\cos$. The decision to add squaring and square roots as primitive, while relegating cubes and cube roots, etc. to a definition using Const and Pow was similarly arbitrary.

I went for this list basically because it’s what the wikipedia article names explicitly. Later on we’ll show that our solution doesn’t depend on the exact list chosen, so we don’t need to worry about this.

Next up, we need to tell haskell how to compute the derivative of a function. Thankfully, derivatives can be computed recursively, so this is quite easy to code up:

This isn’t perfect. For instance, it doesn’t simplify multiplication by $1$, etc. But I wanted a quick and dirty approximation, and importantly, I didn’t want to spend too long on this project3.

As a (fun?) exercise, write a prune function which makes some easy simplifications after differentiating. Does that change which functions grow the most in complexity?

Next, we need a way to figure out how many symbols are in a given expression. This is also easy to implement:

Lastly, we need a way to build up every expression with $n$ symbols. This way we can differentiate them all, and see which gives us the largest output!

Now, my laptop can fully exhaust every function with $\leq 4$ symbols, and we see that our best bets are

(note that the innermost $x$ counts as a symbol).

Moreover, it’s pretty easy to see that we’ll never use a unary function other than $\arccos$. Indeed, if we write $\lvert f \rvert$ for size f, it’s easy to see that

\[\lvert \arccos(f)' \rvert = \lvert f \rvert + \lvert f' \rvert + 7.\]

More generally, $\lvert \text{blah}(f)’ \rvert = \lvert f \rvert + \lvert f’ \rvert + k$ where $k$ is the number of symbols in the derivative of $\text{blah}(x)$4. Since this is biggest for $\arccos$, and we’re trying to maximize the size of $f’$, there’s no reason to use a unary constructor other than $\arccos$.

This is fairly good evidence that repeatedly composing $\arccos(x)$ with itself is the winner, and even though we can’t test every function with $\geq 5$ symbols, we can test a lot of them, and after letting the code run for just over $24$ hours, iterating $\arccos$ was still the winner.

So, in light of our computational evidence, we might conjecture that $\lvert f’ \rvert$ is maximized (among functions with $\lvert f \rvert = n$) for $f = \arccos(\arccos(\ldots(x)\ldots))$.

At this point, it’s time to stop hacking, and start thinking! Let’s try to prove that this is the best option. Notice we can easily compute $\lvert \arccos(\arccos(\ldots(x)\ldots))’ \rvert = \frac{n^2}{2} + \frac{13n}{2} - 6$ (either by solving some recurrence, or by checking oeis), so we should have some simple proof by induction ahead of us.

If $f$ has $n$ symbols in its definition, then $f’$ has at most $\frac{n^2}{2} + \frac{13n}{2} - 6$ symbols, and moreover this maximum is attained for $f = \arccos(\arccos(\ldots(x)\ldots))$.


We’ll induct on $\lvert f \rvert$.

If $\lvert f \rvert = 1,2$ then we’ve already seen that $\lvert f’ \rvert$ satisfied the desired inequality.

If $\lvert f \rvert \geq 3$, then we case on the outermost constructor.

If it’s unary, say $f = g(h)$, where $\lvert h \rvert = n-1$, then we compute

\[\lvert f' \rvert = \lvert g'(h) \cdot h' \rvert = \lvert h' \rvert + (n-1) + k\]

where $k$ is a constant depending on $g$, which is maximized as $k = 7$ when $g = \arccos(-)$. Then

\[\lvert f' \rvert \leq \lvert h' \rvert + n + 6 \overset{IH}{\leq} \frac{(n-1)^2}{2} + \frac{13(n-1)}{2} - 6 + n + 6 = \frac{n^2}{2} + \frac{13n}{2} - 6.\]

If instead the outermost constructor of $f$ is binary, then we have

where $\lvert f \rvert = n = \lvert g \rvert + \lvert h \rvert + 1$.

In each of these cases we compute $\lvert f’ \rvert$, and find

Clearly these are maximized for $g^h$, so let’s put $\lvert g \rvert = k$ and $\lvert h \rvert = n-1-k$. Then we see

\[\begin{align} \lvert (g^h)' \rvert &= 3 \lvert g \rvert + 2 \lvert h \rvert + \lvert g' \rvert + \lvert h' \rvert + 7 \\ &\overset{IH}{\leq} 3k + 2(n-1-k) + \frac{k^2}{2} + \frac{13k}{2} - 6 + \frac{(n-1-k)^2}{2} + \frac{13(n-1-k)}{2} - 6 + 7 \\ &= \frac{n^2}{2} + \frac{(15-2k)n}{2} + k^2 + 2k - 13 \end{align}\]

So we want this to be $\leq \frac{n^2}{2} + \frac{13n}{2} - 6$ for every choice of $1 \leq k \leq n-2$.

Aaaaaand…. ruh roh!

You can see by this desmos graph that this fails in general. Indeed, the earliest failure happens when $n=8$ and $k=6$. Of course, this is outside of the $n \leq 4$ range that I was able to exhaustively test, and even the $n \leq 6$ range that I had tested a lot of5.


This is a perfect example of Klaus’s “Magic Spiral”, which he shows in the first CDM lecture every year.

In this particular situation, there wasn’t a ton to visualize, so we jumped straight from “compute/experiment” to “conjecture”. Indeed, our computations seemed to suggest that iterating $\arccos$ was the right approach, but when we tried to prove it we failed.

This is ok, though! Good, even, because our failure is instructive! We know where our proof failed, and this tells us where we should focus our computational effort on our next trip around the spiral.

Indeed, knowing that we want $k = \lvert g \rvert = 6$ and $n = 8$ says we should try something like

\[f = g^h = \arccos(\arccos(\arccos(\arccos(\arccos(x)))))^x\]

and indeed, haskell tells us that $\lvert f’ \rvert = 79$, which is bigger than the $78$ we would get by iterating $\arccos$.

Now with Pow and ACos to worry about, it’s much less clear what the optimal function will be. After all, we’ll need to balance the two, and I don’t have the processing power to do an exhaustive search of $n=8,9,10$ (say) to try and guess at a pattern6.

Thankfully, this problem still admits an asymptotic solution, and our earlier proof attempt is easily adapted to this setting.

Now, the most important skill a mathematician should learn is how to cover their tracks7. So when presenting a result like this to journals, we should never say that we’re presenting an asymptotic solution because we didn’t have the time to get a closed form.

Instead, we should argue that the choice of constructors for the elementary functions was arbitrary, and any closed form for the maximal size of $\lvert f’ \rvert$ necessarily depends on the choice of constructors! Indeed, there are other conventions one could make, such as deciding to not count multiplication towards the symbol count, since we often denote multiplication by juxtaposition, which doesn’t require a “symbol” at all.

Of course, one can show that the asymptotics of $\lvert f’ \rvert$ are independent of these choices, which makes the asymptotics a better object of study.

… sounds good, doesn’t it8?

Now let’s prove it!

If $f$ has $n$ symbols in its definition, then $f’$ has $O(n^2)$ symbols in its definition, and this is optimal.

Moreover, our proof shows that this is independent of the choice of presentation of the elementary functions.


Again, we induct on $\lvert f \rvert$, the number of symbols in $f$.

Since we’re only interested in asymptotics, there’s nothing interesting to prove about the base case.

For the inductive case, we case on the outermost constructor of $f$.

If it’s unary, say $f = c(g)$, then we see that

\[\lvert f' \rvert = \lvert c'(g) \cdot g'(x) \rvert = \lvert c'(x) \rvert + O \left ( \lvert g \rvert \right ) + O \left ( \lvert g' \rvert \right ) \pm O(1)\]

where the $O(1)$ term is independent of $c$ and keeps track of the symbols involved in representing the multiplication, etc. The big-ohs around $\lvert g \rvert$ and $\lvert g’ \rvert$ account for the fact that we might use each of these a constant number of times9.

Next we see that $\lvert c’(x) \rvert = O(1)$, since we can uniformly bound these by the size of the largest one, as we did with $\arccos$ earlier in this post10. So we see

\[\begin{align} \lvert f' \rvert &= \lvert c(g)' \rvert \\ &\leq O \left ( \lvert g \rvert \right ) + O \left ( \lvert g' \rvert \right ) + O(1) \\ &\overset{IH}{\leq} O \left ( n-1 \right ) + O \left ((n-1)^2 \right ) + O(1) \\ &\leq O(n^2) \end{align}\]

If instead the outermost constructor is binary, say $f = c(g,h)$, where $c(g,h)$ might be $g+h$, $gh$, $g^h$, etc. then we similarly compute

\[\lvert f' \rvert = \lvert c(g,h)' \rvert = O \left ( \lvert g \rvert \right ) + O \left ( \lvert h \rvert \right ) + O \left ( \lvert g' \rvert \right ) + O \left ( \lvert h' \rvert \right ) + O(1)\]

and since $\lvert g \rvert + \lvert h \rvert = n-1$, we see that this is bounded by

\[O(n-1) + O \left ( (n-1)^2 \right ) + O(1) = O(n^2)\]

and the claim follows.

As for the tightness of this bound, any presentation of the elementary functions must have at least one trig function (since we cannot build the trig functions from the others), say $\sin$. Then the $n$-fold composition $\sin(\sin(\cdots(\sin(x) \cdots)))$ is easily seen to have a derivative with quadratically many symbols.


So we see that the precise question posed in the comic has no answer! It asks for the maximal ratio of $\frac{\lvert f’ \rvert}{\lvert f \rvert}$, but we’ve just shown that this ratio is unbounded. Of course, it’s still a fun problem, and a natural variant does admit a nice solution (which we found).

Moreover, this was a good way to showcase the back and forth between computational experimentation and proof. Sometimes you get things wrong, and that’s ok! We learn, and we form new conjectures that are more likely to be correct with every trip around the spiral.

As a cute project idea, while I was writing this one of my friends (Rahul) sent me a blog post where Iago Leal de Freitas built a calculus evaluator in haskell that does simplification properly!

A better hacker than me can probably modify this code to push things a bit further (especially with some parallel computation) to try and find a family of functions $(f_n)$ attaining the maximum ratios $\frac{\lvert f_n’ \rvert}{\lvert f_n \rvert}$.

This should be a pretty approachable problem for an enthusiastic combinatorics student, and I would love to see somebody do it ^_^

This was a lot of fun! It’s been in the works for a while now (since April 28, apparently), but I really only worked on it for a few days. I’m busy working on a lot of other stuff11, and I’ll hopefully share some of it soon.

One of the biggest things I’ve been spending time on (which probably also qualifies as an announcement) has been the HoTTEST Summer 2022, where I’ll be TAing this summer. I’m already pretty active answering questions in the discord, and I’ve been brushing up on my HoTT to get ready12. I can not express how excited I am to be working on this, and if anybody wants to show up, you’re more than welcome! We’re quickly coming up on 1000 participants (of all experience levels), and it’s sure to be a great time!

For now, though, I’m off to bed. Goodnight all, and I’ll see you in the next one ^_^

  1. Incidentally, this is why I chose haskell instead of sage. Python really doesn’t handle algebraic datatypes with any sort of alacrity, and I wanted to exploit the recursive structure of the problem.

    Plus, it’s been a hot second since I got to use haskell, and it’s one of my favorite languages to work in, so I didn’t spend very long on the decision :P. 

  2. Namely as Div (Const 1) (Cos X) 

  3. … and regrettably I failed in that regard. 

  4. Up to an additive constant, at least. If you want to be super precise, then size $ diff $ C e = size e + size (diff e) + size (C X) - 2 is true for every unary constructor C

  5. Of course, we could simply remove Pow as a constructor, since we can simulate it using Exp and Log. It’s not hard to show that the other binary operations will let this proof go through, so we could have “covered our tracks” by acting like we never even considered Pow!

    I thought it would make for a better narrative (and it might be more instructive) to go the asymptotic approach instead. Plus, it really is more hygenic to prove a result that doesn’t depend on a particular choice of “basic” constructors. 

  6. Looking at the formulas, we can tell that eventually Pow will win out over ACos, and it probably wouldn’t take too much work to sort this out…

    Maybe some reader with some free time wants to take this on as a project? 

  7. I’m only half joking 

  8. It helps that this is actually a perfectly reasonable thing to do, and jokes aside my original plan was to get asymptotics for exactly this reason (also because I anticipated that an exact solution might be hard to get).

    I thought we had gotten lucky with the iterated $\arccos$ construction, and if you can get a closed form, you might as well. But with those dreams dashed, it’s back to the asymptotics at the end of the day. 

  9. For example, we might choose to represent the derivative of $g^2$ by $(g + g) g’$, in which case $\lvert (g^2)’$ would refer to $\lvert g \rvert$ twice.

    I haven’t actually thought much about how badly things break if you do something silly like this, but take it to an extreme (can we find a way to make it so that there’s no uniform bound on this constant?), but I’m also ok to leave that particular avenue unexplored.

    Officially I should probably add some hypotheses explicitly forbidding this – for instance, it should be enough to ask that we allow at most finitely many constructors. That said, I think it’s ok to leave this a bit imprecise for the purposes of a blog post. 

  10. You might worry that there is no largest unary constructor. But the only infinite families of constructors (at least that are listed on wikipedia) are the rational powers and the bases for $\exp$ and $\log$.

    It’s clear, though, that the contributions of each of these derivatives can be uniformly bounded as long as we’re counting a constant as a single symbol. 

  11. I’m reading a series of papers on model categories with Sarah Yeakel (who recently got a permanent position at UCR!), as well as continuing my own readings on topos theory (which have filtered into a reading course on locale theory that I’m teaching some undergrads). I’m also in a class on riemann surfaces which has been really enlightening for me. I have a few ideas for blog posts of the “I wish someone had shown me this example sooner” variety, and hopefully I can get to them soon!

    On top of all this, I’ve been talking with Patricio Gallardo about becoming an algebraic geometer, and he wants me to start spending a serious amount of time working through Hartshorne and Vakil’s notes. This makes sense, of course, and I’m having a ton of fun doing it, but it means I have less time to work on silly projects like this. 

  12. Plus trying to gain some serious familiarity with model categories and $\infty$-categories before we start. This lined up quite nicely with my conversations with Sarah about model categories. Sometimes you just get lucky!