Reading the Comics, July 29, 2015: Not Entirely Reruns Edition


Zach Weinersmith’s Saturday Morning Breakfast Cereal (July 25) gets its scheduled appearance here with a properly formed Venn Diagram joke. I’m unqualified to speak for rap musicians. When mathematicians speak of something being “for reals” they mean they’re speaking about a variable that might be any of the real numbers. This is as opposed to limiting the variable to being some rational or irrational number, or being a whole number. It’s also as opposed to letting the variable be some complex-valued number, or some more exotic kind of number. It’s a way of saying what kind of thing we want to find true statements about.

I don’t know when the Saturday Morning Breakfast Cereal first ran, but I know I’ve seen it appear in my Twitter feed. I believe all the Gocomics.com postings of this strip are reruns, but I haven’t read the strip long enough to say.

Steve Sicula’s Home And Away (July 26) is built on the joke of kids wise to mathematics during summer vacation. I don’t think this is a rerun, although we’ve seen the joke this summer before.

An angel with a square halo explains he was good^2.

Daniel Beyer’s Offbeat Comics for the 27th of July, 2015.

Daniel Beyer’s Offbeat Comics (July 27) depicts an angel with a square halo because “I was good2.” The association between squaring a number and squares goes back a long time. Well, it’s right there in the name, isn’t it? Florian Cajori’s A History Of Mathematical Notations cites the term “latus” and the abbreviation “l” to represent the side of a square being used by the Roman surveyor Junius Nipsus in the second century; for centuries this would be as good a term as anyone had for the thing to be calculated. (Res, meaning “thing”, was also popular.) Once you’ve taken the idea of calculating based on the length of a square, the jump to “square” for “length times itself” seems like a tiny one. But Cajori doesn’t seem to have examples of that being written until the 16th century.

The square of the quantity you’re interested in might be written q, for quadratus. The cube would be c, for cubus. The fourth power would be b or bq, for biquadratus, and so on. This is tolerable if you only have to work with a single unknown quantity, but the notation turns into gibberish the moment you want two variables in the mix. So it collapsed in the 17th century, replaced by the familiar x2 and x3 and so on. Many authors developed notations close to this: James Hume would write xii or xiii; Pierre Hérigone x2 or x3, all in one line. Rene Descartes would write x2 or x3 or so, and many, many followed him. Still, quite a few people — including Rene Descartes, Isaac Newton, and even as late a figure as Carl Gauss, in the early 19th century — would resist “x2”. They’d prefer “xx”. Gauss defended this on the grounds that “x2” takes up just as much space as “xx” and so fails the biggest point of having notation.

Corey Pandolph’s Toby, Robot Satan (July 27, rerun) uses sudoku as an example of the logic and reasoning problems that one would expect a robot should be able to do. It is weird to encounter one that’s helpless before them.

Cory Thomas’s Watch Your Head (July 27, rerun from 2007) mentions “Chebyshev grids” and “infinite boundaries” as things someone doing mathematics on the computer would do. And it does so correctly. Differential equations describe how things change on some domain over space and time. They can be very hard to solve exactly, but can be put on the computer very well. For this, we pick a representative set of points which we call a mesh. And we find an approximate representation of the original differential question, which we call a discretization or a difference equation. We can then solve this difference equation on the mesh, and if we’ve done our work right, this approximation will let us get a good estimate for the solution to the original problem over the whole original domain.

A Chebyshev grid is a particular arrangement of mesh points. It’s not uniform; it tends to clump up, becoming more common near the ends of the boundary. This is useful if you have reason to expect that the boundaries are more interesting than the middle of the domain. There’s no sense wasting good computing power calculating boring stuff. The mesh is named for Pafnuty Chebyshev, a 19th Century Russian mathematician whose name is all over mathematics. Unfortunately since he was a 19th Century Russian mathematician, his name is transcribed into English all sorts of ways. Chebyshev seems to be most common today, though Tchebychev used to be quite popular, which is why polynomials of his might be abbreviated as T. There are many alternatives.

Ah, but how do you represent infinite boundaries with the finitely many points of any calculatable mesh? There are many approaches. One is to just draw a really wide mesh and trust that all the action is happening near the center so omitting the very farthest things doesn’t hurt too much. Or you might figure what the average of things far away is, and make a finite boundary that has whatever that value is. Another approach is to make the boundaries repeating: go far enough to the right and you loop back around to the left, go far enough up and you loop back around to down. Another approach is to create a mesh that is bundled up tight around the center, but that has points which do represent going off very, very far, maybe in principle infinitely far away. You’re allowed to create meshes that don’t space points uniformly, and that even move points as you compute. That’s harder work, but it’s legitimate numerical mathematics.

So, the mathematical work being described here is — so far as described — legitimate. I’m not competent to speak about the monkey side of the research.

Greg Evans’s Luann Againn (July 29; rerun from July 29, 1987) name-drops the Law of Averages. There are actually multiple Laws of Averages, with slightly different assumptions and implications, but they all come to about the same meaning. You can expect that if some experiment is run repeatedly, the average value of the experiments will be close to the true value of whatever you’re measuring. An important step in proving this law was done by Pafnuty Chebyshev.

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