# College Math Teaching

## June 18, 2018

### And my “clever proof” is dashed

Filed under: complex variables, editorial, knot theory, numerical methods, topology — Tags: , — collegemathteaching @ 6:03 pm

It has been a while since I posted here, though I have been regularly posting in my complex variables class blog last semester.

And for those who like complex variables and numerical analysis, this is an exciting, interesting development.

But as to the title of my post: I was working to finish up a proof that one kind of wild knot is not “equivalent” to a different kind of wild knot and I had developed a proof (so I think) that the complement of one knot contains an infinite collection of inequivalent tori (whose solid tori contain the knot non-trivially) whereas the other kind of knot can only have a finite number of such tori. I still like the proof.

But it turns out that there is already an invariant that does the trick nicely..hence I can shorten and simplify the paper.

But dang it..I liked my (now irrelevant to my intended result) result!

## March 14, 2014

### Approximating the derivative and round off error: class demonstration

In numerical analysis we are covering “approximate differentiation”. One of the formulas we are using: $f'(x_0) = \frac{f(x_0 + h) -f(x_0 -h)}{2h} - \frac{h^2}{6} f^{(3)}(\zeta)$ where $\zeta$ is some number in $[x_0 -h, x_0 + h]$; of course we assume that the third derivative is continuous in this interval.

The derivation can be done in a couple of ways: one can either use the degree 2 Lagrange polynomial through $x_0-h, x_0, x_0 + h$ and differentiate or one can use the degree 2 Taylor polynomial expanded about $x = x_0$ and use $x = x_0 \pm h$ and solve for $f'(x_0)$; of course one runs into some issues with the remainder term if one uses the Taylor method.

But that isn’t the issue that I want to talk about here.

The issue: “what should we use for $h$?” In theory, we should get a better approximation if we make $h$ as small as possible. But if we are using a computer to make a numerical evaluation, we have to concern ourselves with round off error. So what we actually calculate will NOT be $f'(x_0) = \frac{f(x_0 + h) -f(x_0 -h)}{2h}$ but rather $f'(x_0) = \frac{\hat{f}(x_0 + h) -\hat{f}(x_0 -h)}{2h}$ where $\hat{f}(x_0 \pm h) = f(x_0 \pm h) - e(x_0 \pm h)$ where $e(x_0 \pm h)$ is the round off error used in calculating the function at $x = x_0 \pm h$ (respectively).

So, it is an easy algebraic exercise to show that: $f'(x_0) - \frac{f(x_0 + h) -f(x_0 -h)}{2h} = - \frac{h^2}{6} f^{(3)}(\zeta)-\frac{e(x_0 +h) -e(x_0 -h)}{2h}$ and the magnitude of the actual error is bounded by $\frac{h^2 M}{6} + \frac{\epsilon}{2}$ where $M = max\{f^{(3)}(\eta)\}$ on some small neighborhood of $x_0$ and $\epsilon$ is a bound on the round-off error of representing $f(x_0 \pm h)$.

It is an easy calculus exercise (“take the derivative and set equal to zero and check concavity” easy) to see that this error bound is a minimum when $h = (\frac{3\epsilon}{M})^{\frac{1}{3}}$.

Now, of course, it is helpful to get a “ball park” estimate for what $\epsilon$ is. Here is one way to demonstrate this to the students: solve for $\epsilon$ and obtain $\frac{M h^3}{3} = \epsilon$ and then do some experimentation to determine $\epsilon$.

That is: obtain an estimate of $h$ by using this “3 point midpoint” estimate for a known derivative near a value of $x_0$ for which $M$ (a bound for the 3’rd derivative) is easy to obtain, and then obtain an educated guess for $h$.

Here are a couple of examples: one uses Excel and one uses MATLAB. I used $f(x) = e^x$ at $x = 0$; of course $f'(0) = 1$ and $M = 1$ is reasonable here (just a tiny bit off). I did the 3-point estimation calculation for various values of $h$ and saw where the error started to increase again.

Here is the Excel output for $f(x) = e^x$ at $x =0$ and at $x = 1$ respectively. In the first case, use $M = 1$ and in the second $M = e$ In the $x = 0$ case, we see that the error starts to increase again at about $h = 10^{-5}$; the same sort of thing appears to happen for $x = 1$.

So, in the first case, $\epsilon$ is about $\frac{1}{3} \times (10^{-5})^3 = 3.333 \times 10^{-16}$; it is roughly $10^{-15}$ at $x =1$.

Note: one can also approach $h$ by using powers of $\frac{1}{2}$ instead; something interesting happens in the $x = 0$ case; the $x = 1$ case gives results similar to what we’ve shown. Reason (I think): 1 is easy to represent in base 2 and the powers of $\frac{1}{2}$ can be represented exactly.

Now we turn to MATLAB and here we do something slightly different: we graph the error for different values of $h$. Since the values of $h$ are very small, we use a $-log_{10}$ scale by doing the following (approximating $f'(0)$ for $f(x) = e^x$) . By design, $N = -log_{10}(H)$. The graph looks like: Now, the small error scale makes things hard to read, so we turn to using the log scale, this time on the $y$ axis: let $LE = -log_{10}(E)$ and run plot(N, LE): and sure enough, you can see where the peak is: about $10^{-5}$, which is the same as EXCEL.