## My 2019 Mathematics A To Z: Fourier series

Today’s A To Z term came to me from two nominators. One was @aajohannas, again offering a great topic. Another was Mr Wu, author of the Singapore Maths Tuition blog. I hope neither’s disappointed here.

Fourier series are named for Jean-Baptiste Joseph Fourier, and are maybe the greatest example of the theory that’s brilliantly wrong. Anyone can be wrong about something. There’s genius in being wrong in a way that gives us good new insights into things. Fourier series were developed to understand how the fluid we call “heat” flows through and between objects. Heat is not a fluid. So what? Pretending it’s a fluid gives us good, accurate results. More, you don’t need to use Fourier series to work with a fluid. Or a thing you’re pretending is a fluid. It works for lots of stuff. The Fourier series method challenged assumptions mathematicians had made about how functions worked, how continuity worked, how differential equations worked. These problems could be sorted out. It took a lot of work. It challenged and expended our ideas of functions.

Fourier also managed to hold political offices in France during the Revolution, the Consulate, the Empire, the Bourbon Restoration, the Hundred Days, and the Second Bourbon Restoration without getting killed for his efforts. If nothing else this shows the depth of his talents.

# Fourier series.

So, how do you solve differential equations? As long as they’re linear? There’s usually something we can do. This is one approach. It works well. It has a bit of a weird setup.

The weirdness of the setup: you want to think of functions as points in space. The allegory is rather close. Think of the common association between a point in space and the coordinates that describe that point. Pretend those are the same thing. Then you can do stuff like add points together. That is, take the coordinates of both points. Add the corresponding coordinates together. Match that sum-of-coordinates to a point. This gives us the “sum” of two points. You can subtract points from one another, again by going through their coordinates. Multiply a point by a constant and get a new point. Find the angle between two points. (This is the angle formed by the line segments connecting the origin and both points.)

Functions can work like this. You can add functions together and get a new function. Subtract one function from another. Multiply a function by a constant. It’s even possible to describe an “angle” between two functions. Mathematicians usually call that the dot product or the inner product. But we will sometimes call two functions “orthogonal”. That means the ordinary everyday meaning of “orthogonal”, if anyone said “orthogonal” in ordinary everyday life.

We can take equations of a bunch of variables and solve them. Call the values of that solution the coordinates of a point. Then we talk about finding the point where something interesting happens. Or the points where something interesting happens. We can do the same with differential equations. This is finding a point in the space of functions that makes the equation true. Maybe a set of points. So we can find a function or a family of functions solving the differential equation.

You have reasons for skepticism, even if you’ll grant me treating functions as being like points in space. You might remember solving systems of equations. You need as many equations as there are dimensions of space; a two-dimensional space needs two equations. A three-dimensional space needs three equations. You might have worked four equations in four variables. You were threatened with five equations in five variables if you didn’t all settle down. You’re not sure how many dimensions of space “all the possible functions” are. It’s got to be more than the one differential equation we started with.

This is fair. The approach I’m talking about uses the original differential equation, yes. But it breaks it up into a bunch of linear equations. Enough linear equations to match the space of functions. We turn a differential equation into a set of linear equations, a matrix problem, like we know how to solve. So that settles that.

So suppose $f(x)$ solves the differential equation. Here I’m going to pretend that the function has one independent variable. Many functions have more than this. Doesn’t matter. Everything I say here extends into two or three or more independent variables. It takes longer and uses more symbols and we don’t need that. The thing about $f(x)$ is that we don’t know what it is, but would quite like to.

What we’re going to do is choose a reference set of functions that we do know. Let me call them $g_0(x), g_1(x), g_2(x), g_3(x), \cdots$ going on to however many we need. It can be infinitely many. It certainly is at least up to some $g_N(x)$ for some big enough whole number N. These are a set of “basis functions”. For any function we want to represent we can find a bunch of constants, called coefficients. Let me use $a_0, a_1, a_2, a_3, \cdots$ to represent them. Any function we want is the sum of the coefficient times the matching basis function. That is, there’s some coefficients so that

$f(x) = a_0\cdot g_0(x) + a_1\cdot g_1(x) + a_2\cdot g_2(x) + a_3\cdot g_3(x) + \cdots$

is true. That summation goes on until we run out of basis functions. Or it runs on forever. This is a great way to solve linear differential equations. This is because we know the basis functions. We know everything we care to know about them. We know their derivatives. We know everything on the right-hand side except the coefficients. The coefficients matching any particular function are constants. So the derivatives of $f(x)$, written as the sum of coefficients times basis functions, are easy to work with. If we need second or third or more derivatives? That’s no harder to work with.

You may know something about matrix equations. That is that solving them takes freaking forever. The bigger the equation, the more forever. If you have to solve eight equations in eight unknowns? If you start now, you might finish in your lifetime. For this function space? We need dozens, hundreds, maybe thousands of equations and as many unknowns. Maybe infinitely many. So we seem to have a solution that’s great apart from how we can’t use it.

Except. What if the equations we have to solve are all easy? If we have to solve a bunch that looks like, oh, $2a_0 = 4$ and $3a_1 = -9$ and $2a_2 = 10$ … well, that’ll take some time, yes. But not forever. Great idea. Is there any way to guarantee that?

It’s in the basis functions. If we pick functions that are orthogonal, or are almost orthogonal, to each other? Then we can turn the differential equation into an easy matrix problem. Not as easy as in the last paragraph. But still, not hard.

So what’s a good set of basis functions?

And here, about 800 words later than everyone was expecting, let me introduce the sine and cosine functions. Sines and cosines make great basis functions. They don’t grow without bounds. They don’t dwindle to nothing. They’re easy to differentiate. They’re easy to integrate, which is really special. Most functions are hard to integrate. We even know what they look like. They’re waves. Some have long wavelengths, some short wavelengths. But waves. And … well, it’s easy to make sets of them orthogonal.

We have to set some rules. The first is that each of these sine and cosine basis functions have a period. That is, after some time (or distance), they repeat. They might repeat before that. Most of them do, in fact. But we’re guaranteed a repeat after no longer than some period. Call that period ‘L’.

Each of these sine and cosine basis functions has to have a whole number of complete oscillations within the period L. So we can say something about the sine and cosine functions. They have to look like these:

$s_j(x) = \sin\left(\frac{2\pi j}{L} x\right)$

$c_k(x) = \cos\left(\frac{2\pi k}{L} x\right)$

Here ‘j’ and ‘k’ are some whole numbers. I have two sets of basis functions at work here. Don’t let that throw you. We could have labelled them all as $g_k(x)$, with some clever scheme that told us for a given k whether it represents a sine or a cosine. It’s less hard work if we have s’s and c’s. And if we have coefficients of both a’s and b’s. That is, we suppose the function $f(x)$ is:

$f(x) = \frac{1}{2}a_0 + b_1 s_1(x) + a_1 c_1(x) + b_2 s_2(x) + a_2 s_2(x) + b_3 s_3(x) + a_3 c_3(x) + \cdots$

This, at last, is the Fourier series. Each function has its own series. A “series” is a summation. It can be of finitely many terms. It can be of infinitely many. Often infinitely many terms give more interesting stuff. Like this, for example. Oh, and there’s a bare $\frac{1}{2}a_0$ there, not multiplied by anything more complicated. It makes life easier. It lets us see that the Fourier series for, like, 3 + f(x) is the same as the Fourier series for f(x), except for the leading term. The ½ before that makes easier some work that’s outside the scope of this essay. Accept it as one of the merry, wondrous appearances of ‘2’ in mathematics expressions.

It’s great for solving differential equations. It’s also great for encryption. The sines and the cosines are standard functions, after all. We can send all the information we need to reconstruct a function by sending the coefficients for it. This can also help us pick out signal from noise. Noise has a Fourier series that looks a particular way. If you take the coefficients for a noisy signal and remove that? You can get a good approximation of the original, noiseless, signal.

This all seems great. That’s a good time to feel skeptical. First, like, not everything we want to work with looks like waves. Suppose we need a function that looks like a parabola. It’s silly to think we can add a bunch of sines and cosines and get a parabola. Like, a parabola isn’t periodic, to start with.

So it’s not. To use Fourier series methods on something that’s not periodic, we use a clever technique: we tell a fib. We declare that the period is something bigger than we care about. Say the period is, oh, ten million years long. A hundred light-years wide. Whatever. We trust that the difference between the function we do want, and the function that we calculate, will be small. We trust that if someone ten million years from now and a hundred light-years away wishes to complain about our work, we will be out of the office that day. Letting the period L be big enough is a good reliable tool.

The other thing? Can we approximate any function as a Fourier series? Like, at least chunks of parabolas? Polynomials? Chunks of exponential growths or decays? What about sawtooth functions, that rise and fall? What about step functions, that are constant for a while and then jump up or down?

The answer to all these questions is “yes,” although drawing out the word and raising a finger to say there are some issues we have to deal with. One issue is that most of the time, we need an infinitely long series to represent a function perfectly. TThis is fine if we’re trying to prove things about functions in general rather than solve some specific problem. It’s no harder to write the sum of infinitely many terms than the sum of finitely many terms. You write an &infty; symbol instead of an N in some important places. But if we want to solve specific problems? We probably want to deal with finitely many terms. (I hedge that statement on purpose. Sometimes it turns out we can find a formula for all the infinitely many coefficients.) This will usually give us an approximation of the $f(x)$ we want. The approximation can be as good as we want, but to get a better approximation we need more terms. Fair enough. This kind of tradeoff doesn’t seem too weird.

Another issue is in discontinuities. If $f(x)$ jumps around? If it has some point where it’s undefined? If it has corners? Then the Fourier series has problems. Summing up sines and cosines can’t give us a sudden jump or a gap or anything. Near a discontinuity, the Fourier series will get this high-frequency wobble. A bigger jump, a bigger wobble. You may not blame the series for not representing a discontinuity. But it does mean that what is, otherwise, a pretty good match for the $f(x)$ you want gets this region where it stops being so good a match.

That’s all right. These issues aren’t bad enough, or unpredictable enough, to keep Fourier series from being powerful tools. Even when we find problems for which sines and cosines are poor fits, we use this same approach. Describe a function we would like to know as the sums of functions we choose to work with. Fourier series are one of those ideas that helps us solve problems, and guides us to new ways to solve problems.

This is my last big essay for the week. All of Fall 2019 A To Z posts should be at this link. The letter G should get its chance on Tuesday and H next Thursday. I intend to have A To Z essays should be available at this link. If you’d like to nominate topics for essays, I’m asking for the letters I through N at this link. Thank you.

## My 2019 Mathematics A To Z: Encryption schemes

Today’s A To Z term is encryption schemes. It’s another suggested by aajohannas. It’s a chance to dip into information theory.

Mr Wu, author of the Mathtuition88 blog, suggested the Extreme Value Theorem. I was tempted and then realized that I had written this in the 2018 A-to-Z, as the “X” letter. The end of the alphabet has a shortage of good mathematics words. Sometimes we have to work around problems.

# Encryption schemes.

Why encrypt anything?

The oldest reason to hide a message, at least from all but select recipients. Ancient encryption methods will substitute one letter for another, or will mix up the order of letters in a message. This won’t hide a message forever. But it will slow down a person trying to decrypt the message until they decide they don’t need to know what it says. Or decide to bludgeon the message-writer into revealing the secret.

Substituting one letter for another won’t stop an eavesdropper from working out the message. Not indefinitely, anyway. There are patterns in the language. Any language, but take English as an example. A single-letter word is either ‘I’ or ‘A’. A two-letter word has a great chance of being ‘in’, ‘on’, ‘by’, ‘of’, ‘an’, or a couple other choices. Solving this is a fun pastime, for people who like this. If you need it done fast, let a computer work it out.

To hide the message better requires being cleverer. For example, you could substitue letters according to a slightly different scheme for each letter in the original message. The Vignère cipher is an example of this. I remember some books from my childhood, written in the second person. They had programs that you-the-reader could type in to live the thrill of being a child secret agent computer programmer. This encryption scheme was one of the programs used for passing on messages. We can make the plans more complicated yet, but that won’t give us better insight yet.

The objective is to turn the message into something less predictable. An encryption which turns, say, ‘the’ into ‘rgw’ will slow the reader down. But if they pay attention and notice, oh, the text also has the words ‘rgwm’, ‘rgey’, and rgwb’ turn up a lot? It’s hard not to suspect these are ‘the’, ‘them’, ‘they’, and ‘then’. If a different three-letter code is used for every appearance of ‘the’, good. If there’s a way to conceal the spaces as something else, that’s even better, if we want it harder to decrypt the message.

So the messages hardest to decrypt should be the most random. We can give randomness a precise definition. We owe it to information theory, which is the study of how to encode and successfully transmit and decode messages. In this, the information content of a message is its entropy. Yes, the same word as used to describe broken eggs and cream stirred into coffee. The entropy measures how likely each possible message is. Encryption matches the message you really want with a message of higher entropy. That is, one that’s harder to predict. Decrypting reverses that matching.

So what goes into a message? We call them words, or codewords, so we have a clear noun to use. A codeword is a string of letters from an agreed-on alphabet. The terminology draws from common ordinary language. Cryptography grew out of sending sentences.

But anything can be the letters of the alphabet. Any string of them can be a codeword. An unavoidable song from my childhood told the story of a man asking his former lover to tie a yellow ribbon around an oak tree. This is a tiny alphabet, but it only had to convey two words, signalling whether she was open to resuming their relationship. Digital computers use an alphabet of two memory states. We label them ‘0’ and ‘1’, although we could as well label them +5 and -5, or A and B, or whatever. It’s not like actual symbols are scrawled very tight into the chips. Morse code uses dots and dashes and short and long pauses. Naval signal flags have a set of shapes and patterns to represent the letters of the alphabet, as well as common or urgent messages. There is not a single universally correct number of letters or length of words for encryption. It depends on what the code will be used for, and how.

Naval signal flags help me to my next point. There’s a single pattern which, if shown, communicates the message “I require a pilot”. Another, “I am on fire and have dangerous cargo”. Still another, “All persons should report on board as the vessel is about to set to sea”. These are whole sentences; they’re encrypted into a single letter.

And this is the second great use of encryption. English — any human language — has redundancy to it. Think of the sentence “No, I’d rather not go out this evening”. It’s polite, but is there anything in it not communicated by texting back “N”? An encrypted message is, often, shorter than the original. To send a message costs something. Time, if nothing else. To send it more briefly is typically better.

There are dangers to this. Strike out any word from “No, I’d rather not go out this evening”. Ask someone to guess what belongs there. Only the extroverts will have trouble. I guess if you strike out “evening” people might guess “today” or “tomorrow” or something. The sentiment of the sentence remains.

But strike out a letter from “N” and ask someone to guess what was meant. And this is a danger of encryption. The encrypted message has a higher entropy, a higher unpredictability. If some mistake happens in transmission, we’re lost.

We can fight this. It’s possible to build checks into an encryption. To carry a bit of extra information that lets one know that the message was garbled. These are “error-detecting codes”. It’s even possible to carry enough extra information to correct some errors. These are “error-correcting codes”. There are limits, of course. This kind of error-correcting takes calculation time and message space. We lose some economy but gain reliability. There is a general lesson in this.

And not everything can compress. There are (if I’m reading this right) 26 letter, 10 numeral, and four repeater flags used under the International Code of Symbols. So there are at most 40 signals that could be reduced to a single flag. If we need to communicate “I am on fire but have no dangerous cargo” we’re at a loss. We have to spell things out more. It’s a quick proof, by way of the pigeonhole principle, which tells us that not every message can compress. But this is all right. There are many messages we will never need to send. (“I am on fire and my cargo needs updates on Funky Winkerbean.”) If it’s mostly those that have no compressed version, who cares?

Encryption schemes are almost as flexible as language itself. There are families of kinds of schemes. This lets us fit schemes to needs: how many different messages do we need to be able to send? How sure do we need to be that errors are corrected? Or that errors are detected? How hard do we want it to be for eavesdroppers to decode the message? Are we able to set up information with the intended recipients separately? What we need, and what we are willing to do without, guide the scheme we use.

Thank you again for reading. All of Fall 2019 A To Z posts should be at this link. I hope to have a letter F piece on Thursday. All of the A To Z essays should be at this link and if I can sort out some trouble with the first two, they will be soon. And if you’d like to nominate topics for essays, I’m asking for the letters I through N at this link.

## I Ask For The Second Topics For My Fall 2019 Mathematics A-to-Z

We’re only in the third week of the Fall 2019 Mathematics A-to-Z, but this is when I should be nailing down topics for the next several letters. So again, I ask you kind readers for suggestions. I’ve done five A-to-Z sequences before, from 2015 through 2018, and am listing the essays I’ve already written for the middle part of the alphabet. I’m open to revisiting topics, if I think I can improve on what I already wrote. But I reserve the right to use whatever topic feels most interesting to me.

To suggest anything for the letters I through N please leave the comment here. Also do please let me know if you have a mathematics blog, a Twitter or Mathstodon account, a YouTube channel, or anything else that you’d like to share.

### N.

I thank you again you for any thoughts you have. Please ask if there are any questions. I hope to be open to topics in any field of mathematics, including ones I don’t really know. The fun and terror of writing about a thing I’m only learning about is part of what I get from this kind of project.

## My 2019 Mathematics A To Z: Differential Equations

The thing most important to know about differential equations is that for short, we call it “diff eq”. This is pronounced “diffy q”. It’s a fun name. People who aren’t taking mathematics smile when they hear someone has to get to “diffy q”.

Sometimes we need to be more exact. Then the less exciting names “ODE” and “PDE” get used. The meaning of the “DE” part is an easy guess. The meaning of “O” or “P” will be clear by the time this essay’s finished. We can find approximate answers to differential equations by computer. This is known generally as “numerical solutions”. So you will encounter talk about, say, “NSPDE”. There’s an implied “of” between the S and the P there. I don’t often see “NSODE”. For some reason, probably a quite arbitrary historical choice, this is just called “numerical integration” instead.

To write about “differential equations” was suggested by aajohannas, who is on Twitter as @aajohannas.

# Differential Equations.

One of algebra’s unsettling things is the idea that we can work with numbers without knowing their values. We can give them names, like ‘x’ or ‘a’ or ‘t’. We can know things about them. Often it’s equations telling us these things. We can make collections of numbers based on them all sharing some property. Often these things are solutions to equations. We can even describe changing those collections according to some rule, even before we know whether any of the numbers is 2. Often these things are functions, here matching one set of numbers to another.

One of analysis’s unsettling things is the idea that most things we can do with numbers we can also do with functions. We can give them names, like ‘f’ and ‘g’ and … ‘F’. That’s easy enough. We can add and subtract them. Multiply and divide. This is unsurprising. We can measure their sizes. This is odd but, all right. We can know things about functions even without knowing exactly what they are. We can group together collections of functions based on some properties they share. This is getting wild. We can even describe changing these collections according to some rule. This change is itself a function, but it is usually called an “operator”, saving us some confusion.

So we can describe a function in an equation. We may not know what f is, but suppose we know $\sqrt{f(x) - 2} = x$ is true. We can suppose that if we cared we could find what function, or functions, f made that equation true. There is shorthand here. A function has a domain, a range, and a rule. The equation part helps us find the rule. The domain and range we get from the problem. Or we take the implicit rule that both are the biggest sets of real-valued numbers for which the rule parses. Sometimes biggest sets of complex-valued numbers. We get so used to saying “the function” to mean “the rule for the function” that we’ll forget to say that’s what we’re doing.

There are things we can do with functions that we can’t do with numbers. Or at least that are too boring to do with numbers. The most important here is taking derivatives. The derivative of a function is another function. One good way to think of a derivative is that it describes how a function changes when its variables change. (The derivative of a number is zero, which is boring except when it’s also useful.) Derivatives are great. You learn them in Intro Calculus, and there are a bunch of rules to follow. But follow them and you can pretty much take the derivative of any function even if it’s complicated. Yes, you might have to look up what the derivative of the arc-hyperbolic-secant is. Nobody has ever used the arc-hyperbolic-secant, except to tease a student.

And the derivative of a function is itself a function. So you can take a derivative again. Mathematicians call this the “second derivative”, because we didn’t expect someone would ask what to call it and we had to say something. We can take the derivative of the second derivative. This is the “third derivative” because by then changing the scheme would be awkward. If you need to talk about taking the derivative some large but unspecified number of times, this is the n-th derivative. Or m-th, if you’ve already used ‘n’ to mean something else.

And now we get to differential equations. These are equations in which we describe a function using at least one of its derivatives. The original function, that is, f, usually appears in the equation. It doesn’t have to, though.

We divide the earth naturally (we think) into two pairs of hemispheres, northern and southern, eastern and western. We divide differential equations naturally (we think) into two pairs of two kinds of differential equations.

The first division is into linear and nonlinear equations. I’ll describe the two kinds of problem loosely. Linear equations are the kind you don’t need a mathematician to solve. If the equation has solutions, we can write out procedures that find them, like, all the time. A well-programmed computer can solve them exactly. Nonlinear equations, meanwhile, are the kind no mathematician can solve. They’re just too hard. There’s no processes that are sure to find an answer.

You may ask. We don’t need mathematicians to solve linear equations. Mathematicians can’t solve nonlinear ones. So what do we need mathematicians for? The answer is that I exaggerate. Linear equations aren’t quite that simple. Nonlinear equations aren’t quite that hopeless. There are nonlinear equations we can solve exactly, for example. This usually involves some ingenious transformation. We find a linear equation whose solution guides us to the function we do want.

And that is what mathematicians do in such a field. A nonlinear differential equation may, generally, be hopeless. But we can often find a linear differential equation which gives us insight to what we want. Finding that equation, and showing that its answers are relevant, is the work.

The other hemispheres we call ordinary differential equations and partial differential equations. In form, the difference between them is the kind of derivative that’s taken. If the function’s domain is more than one dimension, then there are different kinds of derivative. Or as normal people put it, if the function has more than one independent variable, then there are different kinds of derivatives. These are partial derivatives and ordinary (or “full”) derivatives. Partial derivatives give us partial differential equations. Ordinary derivatives give us ordinary differential equations. I think it’s easier to understand a partial derivative.

Suppose a function depends on three variables, imaginatively named x, y, and z. There are three partial first derivatives. One describes how the function changes if we pretend y and z are constants, but let x change. This is the “partial derivative with respect to x”. Another describes how the function changes if we pretend x and z are constants, but let y change. This is the “partial derivative with respect to y”. The third describes how the function changes if we pretend x and y are constants, but let z change. You can guess what we call this.

In an ordinary differential equation we would still like to know how the function changes when x changes. But we have to admit that a change in x might cause a change in y and z. So we have to account for that. If you don’t see how such a thing is possible don’t worry. The differential equations textbook has an example in which you wish to measure something on the surface of a hill. Temperature, usually. Maybe rainfall or wind speed. To move from one spot to another a bit east of it is also to move up or down. The change in (let’s say) x, how far east you are, demands a change in z, how far above sea level you are.

That’s structure, though. What’s more interesting is the meaning. What kinds of problems do ordinary and partial differential equations usually represent? Partial differential equations are great for describing surfaces and flows and great bulk masses of things. If you see an equation about how heat transmits through a room? That’s a partial differential equation. About how sound passes through a forest? Partial differential equation. About the climate? Partial differential equations again.

Ordinary differential equations are great for describing a ball rolling on a lumpy hill. It’s given an initial push. There are some directions (downhill) that it’s easier to roll in. There’s some directions (uphill) that it’s harder to roll in, but it can roll if the push was hard enough. There’s maybe friction that makes it roll to a stop.

Put that way it’s clear all the interesting stuff is partial differential equations. Balls on lumpy hills are nice but who cares? Miniature golf course designers and that’s all. This is because I’ve presented it to look silly. I’ve got you thinking of a “ball” and a “hill” as if I meant balls and hills. Nah. It’s usually possible to bundle a lot of information about a physical problem into something that looks like a ball. And then we can bundle the ways things interact into something that looks like a hill.

Like, suppose we have two blocks on a shared track, like in a high school physics class. We can describe their positions as one point in a two-dimensional space. One axis is where on the track the first block is, and the other axis is where on the track the second block is. Physics problems like this also usually depend on momentum. We can toss these in too, an axis that describes the momentum of the first block, and another axis that describes the momentum of the second block.

We’re already up to four dimensions, and we only have two things, both confined to one track. That’s all right. We don’t have to draw it. If we do, we draw something that looks like a two- or three-dimensional sketch, maybe with a note that says “D = 4” to remind us. There’s some point in this four-dimensional space that describes these blocks on the track. That’s the “ball” for this differential equation.

The things that the blocks can do? Like, they can collide? They maybe have rubber tips so they bounce off each other? Maybe someone’s put magnets on them so they’ll draw together or repel? Maybe there’s a spring connecting them? These possible interactions are the shape of the hills that the ball representing the system “rolls” over. An impenetrable barrier, like, two things colliding, is a vertical wall. Two things being attracted is a little divot. Two things being repulsed is a little hill. Things like that.

Now you see why an ordinary differential equation might be interesting. It can capture what happens when many separate things interact.

I write this as though ordinary and partial differential equations are different continents of thought. They’re not. When you model something you make choices and they can guide you to ordinary or to partial differential equations. My own research work, for example, was on planetary atmospheres. Atmospheres are fluids. Representing how fluids move usually calls for partial differential equations. But my own interest was in vortices, swirls like hurricanes or Jupiter’s Great Red Spot. Since I was acting as if the atmosphere was a bunch of storms pushing each other around, this implied ordinary differential equations.

There are more hemispheres of differential equations. They have names like homogenous and non-homogenous. Coupled and decoupled. Separable and nonseparable. Exact and non-exact. Elliptic, parabolic, and hyperbolic partial differential equations. Don’t worry about those labels. They relate to how difficult the equations are to solve. What ways they’re difficult. In what ways they break computers trying to approximate their solutions.

What’s interesting about these, besides that they represent many physical problems, is that they capture the idea of feedback. Of control. If a system’s current state affects how it’s going to change, then it probably has a differential equation describing it. Many systems change based on their current state. So differential equations have long been near the center of professional mathematics. They offer great and exciting pure questions while still staying urgent and relevant to real-world problems. They’re great things.

Thanks again for reading. Al Fall 2019 A To Z posts should be at this link. I should get to the letter E for Tuesday. All of the A To Z essays should be at this link. If you have thoughts about other topics I might cover, please offer suggestions for the letters G and H.

## My 2019 Mathematics A To Z: Category Theory

Today’s A To Z term is category theory. It was suggested by aajohannas, on Twitter as @aajohannas. It’s a topic I have long wanted to know better, and that every year or so I make a new attempt to try learning without ever feeling like I’ve made progress.

The language of it is beautiful, though. Much of its work is attractive just to see, too, as the field’s developed notation that could be presented as visual art. Much of mathematics could be visual art, yes, but these are art you can almost create in ASCII. It’s amazing.

# Category Theory.

What is the most important part of mathematics? Well, the part you wish you understood, yes. But what’s the fundamental part? The piece of mathematics that we could feel most sure an alien intelligence would agree is mathematics?

There’s idle curiosity behind this, yes. It’s a question implicit in some ideals of the Enlightenment. The notion that we should be able to find truths that all beings capable of reason would agree upon, and find themselves. Mathematics seems particularly good for this. If we have something proven by deductive logic from clearly stated axioms and definitions, then we know something true.

There’s practicality too. In the late 19th and early 20th century (western) mathematics tried to find logically rigorous foundations. You might have thought we always had that and, uh, not so much. It turns out a complete rigorous logical proof of even simple stuff takes forever. Mathematicians compose enough of an argument to convince other mathematicians that we could fill in the details. But we still trusted there was a rigorous foundation. The question is, what is it?

A great candidate for this was set theory. This was a great breakthrough. The basic modern idea of set theory builds on bunches of things, called elements. And collections of those things, called sets. And we build rigorous ideas of what it means for elements to be members of sets. This doesn’t sound like much. Powerful ideas never do.

I don’t know that everyone’s intuition is like this. But my gut wants to think a “powerful” result is, like, a great rocket. Some enormous and prominent and mighty thing that blasts through a problem like gravity or an atmosphere. This is almost the opposite of what mathematics means by “powerful”. A rocket is a fiddly, delicate thing. It has millions of components made to tight specifications. It can only launch when lots of conditions are exactly right. A theorem that gives a great result, but has a long list of prerequisites and lemmas that feed into it resembles this. A powerful mathematical result is more like the gravity that the rocket overcomes. It tends to suppose little about the situation, and so it provides results that are applicable over the whole field. Or over a wide field, or a surprising breadth of topics. And, really, mighty as a rocket might be, the gravity it fights is moreso.

So set theory is powerful. It can explain many things. Most amazing is that we can represent arithmetic with it. At least we can get to integers, and all that we do with integers, and that in not too much work. It makes sense that mathematicians latched onto this as critical. It fueled much of the thinking behind the New Math, the infamous attempted United States educational reform of the 1960s and 70s. I grew up in the tail end of this, learning unions and intersections and complements along with times tables and delighted in it.

But even before New Math became a coherent idea there was a better idea. Emmy Noether, mentioned yesterday, is not a part of it. But a part of her insight into physics, and into group theory, was an understanding of structure. That important mathematics results from considering what we can do with sets of things. And what things we can do that produce invariants, things that don’t change. Saunders Mac Lane, one of Noether’s students, and Samuel Eilenberg in the 1940s used what looks to me like this principle. They organized category theory.

Category Theory looks at first like set theory only made terrifying. I’m not very comfortable with it myself. It’s an abstract field, and I’m more at home with stuff I can write a quick Octave program to double-check. Many results in category theory are described, or even proved, with beautiful directed-graph lattices. They show how things relate to one another. This is definitely the field to study if you like drawing arrows.

Just as set theory does, category theory starts with things, called objects. And these objects get piled together into collections. And then there’s another collection of relationships between these collections. These relationships you call maps or morphisms or arrows, based on whatever the first book you kind of understood called them. I’m partial to “maps”. And then we have rules by which these maps compose, that is, where two maps reduce to a single map. This bundle of things — the objects, the collections, and the maps — is a category.

These objects can start out looking like elements, and the collections like sets, and the maps like functions. This gives me, at least, a patch of ground where I feel like I know what I’m doing. But what we need of things to be objects and collections and maps is very little. The result is great power. We can describe set theory in the language of categories. So we can describe arithmetic in category theory. There’s a bit of a hike from the start of category theory to, like, knowing what 18 plus 7 is.

But we’re not bound to anything that concrete. We can describe, for example, groups as categories. This gives us results like when we can factor polynomials. Or whether compass and straightedge can trisect an arbitrary angle. (There’s some work behind this too.) We can describe vector spaces as categories. Heady results like the idea that one function might be orthogonal to another lurk within this field. Manifolds, spaces that work like normal space, are part of the field. So are topological spaces, which tell us about shapes.

If you aren’t yet dizzy then consider this. A category is itself an object. So we can define maps between categories. These we call functors. Which themselves have use in computer science, as a way some kinds of software can be programmed well. More, maps themselves are objects. We can define mappings between maps. These we call natural transformations. Which are the things that Eilenberg and Mac Lane were particularly interested in, to start with. Category theory grew in part out of needing a better understanding of natural transformations.

I do not know what to recommend for people who want to really learn category theory. I haven’t found the textbook or the blog that makes me feel like I am mastering the subject. Writing this essay has introduced me to Dr Tom Leinster’s Basic Category Theory, which I’ve enjoyed skimming. Exercise 3.3.1, for example, seems like exactly the sort of problem I would pose if I knew category theory well enough to write a book on it.

Is this, finally, the mathematics we could be sure an alien would recognize? I’m skeptical, but I always am. It seems to me we build mathematics on arithmetic and geometry. Category theory, seeming to offer explanations of both, is a natural foundation for that. But we are evolved to see the world in terms of number and shape. Of course we see arithmetic and geometry as mathematics. Can we count on every being capable of reason seeing the same things as important? … I admit I can’t imagine a being we might communicate with not recognizing both. But this may say more about the limits of my imagination than about the limits of what could be mathematics.

Thanks for reading. All the Fall 2019 A To Z posts should be at this link. I hope to have the second essay of the week posted Thursday. This year’s and all past A To Z sequences should be at this link. And if you have thoughts about other topics I might cover, please offer suggestions for the letters F through H.

## My 2019 Mathematics A To Z: Buffon’s Needle

Today’s A To Z term was suggested by Peter Mander. Mander authors CarnotCycle, which when I first joined WordPress was one of the few blogs discussing thermodynamics in any detail. When I last checked it still was, which is a shame. Thermodynamics is a fascinating field. It’s as deeply weird and counter-intuitive and important as quantum mechanics. Yet its principles are as familiar as a mug of warm tea on a chilly day. Mander writes at a more technical level than I usually do. But if you’re comfortable with calculus, or if you’re comfortable nodding at a line and agreeing that he wouldn’t fib to you about a thing like calculus, it’s worth reading.

# Buffon’s Needle.

I’ve written of my fondness for boredom. A bored mind is not one lacking stimulation. It is one stimulated by anything, however petty. And in petty things we can find great surprises.

I do not know what caused Georges-Louis Leclerc, Comte de Buffon, to discover the needle problem named for him. It seems like something born of a bored but active mind. Buffon had an active mind: he was one of Europe’s most important naturalists of the 1700s. He also worked in mathematics, and astronomy, and optics. It shows what one can do with an engaged mind and a large inheritance from one’s childless uncle who’s the tax farmer for all Sicily.

The problem, though. Imagine dropping a needle on a floor that has equally spaced parallel lines. What is the probability that the needle will land on any of the lines? It could occur to anyone with a wood floor who’s dropped a thing. (There is a similar problem which would occur to anyone with a tile floor.) They have only to be ready to ask the question. Buffon did this in 1733. He had it solved by 1777. We, with several centuries’ insight into probability and calculus, need less than 44 years to solve the question.

Let me use L as the length of the needle. And d as the spacing of the parallel lines. If the needle’s length is less than the spacing then this is an easy formula to write, and not too hard to calculate. The probability, P, of the needle crossing some line is:

$P = \frac{2}{\pi}\frac{L}{d}$

I won’t derive it rigorously. You don’t need me for that. The interesting question is whether this formula makes sense. That L and d are in it? Yes, that makes sense. The length of the needle and the gap between lines have to be in there. More, the probability has to have the ratio between the two. There’s different ways to argue this. Dimensional analysis convinces me, at least. Probability is a pure number. L is a measurement of length; d is a measurement of length. To get a pure number starting with L and d means one of them has to divide into the other. That L is in the numerator and d the denominator makes sense. A tiny needle has a tiny chance of crossing a line. A large needle has a large chance. That $\frac{L}{d}$ is raised to the first power, rather than the second or third or such … well, that’s fair. A needle twice as long having twice the chance of crossing a line? That sounds more likely than a needle twice as long having four times the chance, or eight times the chance.

Does the 2 belong there? Hard to say. 2 seems like a harmless enough number. It appears in many respectable formulas. That π, though …

That π …

π comes to us from circles. We see it in calculations about circles and spheres all the time. We’re doing a problem with lines and line segments. What business does π have showing up?

We can find reasons. One way is to look at a similar problem. Imagine dropping a disc on these lines. What’s the chance the disc falls across some line? That’s the chance that the center of the disc is less than one radius from any of the lines. What if the disc has an equal chance of landing anywhere on the floor? Then it has a probability of $\frac{L}{d}$ of crossing a line. If the radius is smaller than the distance between lines, anyway. If the radius is larger than that, the probability is 1.

Now draw a diameter line on this disc. What’s the chance that this diameter line crosses this floor line? That depends on a couple things. Whether the center of the disc is near enough a floor line. And what angle the diameter line makes with respect to the floor lines. If the diameter line is parallel the floor line there’s almost no chance. If the diameter line is perpendicular to the floor line there’s the best possible chance. But that angle might be anything.

Let me call that angle θ. The diameter line crosses the floor line if the diameter times the sine of θ is less than half the distance between floor lines. … Oh. Sine. Sine and cosine and all the trigonometry functions we get from studying circles, and how to draw triangles within circles. And this diameter-line problem looks the same as the needle problem. So that’s where π comes from.

I’m being figurative. I don’t think one can make a rigorous declaration that the π in the probability formula “comes from” this sine, any more than you can declare that the square-ness of a shape comes from any one side. But it gives a reason to believe that π belongs in the probability.

If the needle’s longer than the gap between floor lines, if $L > d$, there’s still a probability that the needle crosses at least one line. It never becomes certain. No matter how long the needle is it could fall parallel to all the floor lines and miss them all. The probability is instead:

$P = \frac{2}{\pi}\left(\frac{L}{d} - \sqrt{\left(\frac{L}{d}\right)^2 - 1} + \sec^{-1}\left(\frac{L}{d}\right)\right)$

Here $\sec^{-1}$ is the world-famous arcsecant function. That is, it’s whatever angle has as its secant the number $\frac{L}{d}$. I don’t mean to insult you. I’m being kind to the person reading this first thing in the morning. I’m not going to try justifying this formula. You can play with numbers, though. You’ll see that if $\frac{L}{d}$ is a little bit bigger than 1, the probability is a little more than what you get if $\frac{L}{d}$ is a little smaller than 1. This is reassuring.

The exciting thing is arithmetic, though. Use the probability of a needle crossing a line, for short needles. You can re-write it as this:

$\pi = 2\frac{L}{d}\frac{1}{P}$

L and d you can find by measuring needles and the lines. P you can estimate. Drop a needle many times over. Count how many times you drop it, and how many times it crosses a line. P is roughly the number of crossings divided by the number of needle drops. Doing this gives you a way to estimate π. This gives you something to talk about on Pi Day.

It’s a rubbish way to find π. It’s a lot of work, plus you have to sweep needles off the floor. Well, you can do it in simulation and avoid the risk of stepping on an overlooked needle. But it takes a lot of needle-drops to get good results. To be certain you’ve calculated the first two decimal points correctly requires 3,380,000 needle-drops. Yes, yes. You could get lucky and happen to hit on an estimate of 3.14 for π with fewer needle-drops. But if you were sincerely trying to calculate the digits of π this way? If you did not know what they were? You would need the three and a third million tries to be confident you had the number correct.

So this result is, as a practical matter, useless. It’s a heady concept, though. We think casually of randomness as … randomness. Unpredictability. Sometimes we will speak of the Law of Large Numbers. This is several theorems in probability. They all point to the same result. That if some event has (say) a probability of one-third of happening, then given 30 million chances, it will happen quite close to 10 million times.

This π result is another casting of the Law of Large Numbers, and of the apparent paradox that true unpredictability is itself predictable. There is no way to predict whether any one dropped needle will cross any line. It doesn’t even matter whether any one needle crosses any line. An enormous number of needles, tossed without fear or favor, will fall in ways that embed π. The same π you get from comparing the circumference of a circle to its diameter. The same π you get from looking at the arc-cosine of a negative one.

I suppose we could use this also to calculate the value of 2, but that somehow seems to touch lesser majesties.

Thank you again for reading. All of the Fall 2019 A To Z posts should be at this link. This year’s and all past A To Z sequences should be at this link. I’ve made my picks for next week’s topics, and am fooling myself into thinking I have a rough outline for them already. But I’m still open for suggestions for the letters E through H and appreciate suggestions.

## My 2019 Mathematics A To Z: Abacus

Today’s A To Z term is the Abacus. It was suggested by aajohannas, on Twitter as @aajohannas. Particularly asked for was how to use an abacus. The abacus has been used by a great many cultures over thousands of years. So it’s hard to say that there is any one right way to use it. I’m going to get into a way to use it to compute, any more than there is a right way to use a hammer. There are many hammers, and many things to hammer. But there are similarities between all hammers, and the ways to use them as hammers are similar. So learning one kind, and one way to use that kind, can be a useful start.

# Abacus.

I taught at the National University of Singapore in the first half of the 2000s. At the student union was this sheltered overhang formed by a stairwell. Underneath it, partly exposed to the elements (a common building style there) was a convenience store. Up front were the things with high turnover, snacks and pop and daily newspapers, that sort of thing. In the back, beyond the register, in the areas that the rain, the only non-gentle element, couldn’t reach even whipped by wind, were other things. Miscellaneous things. Exam bluebooks faded with age and dust. Good-luck cat statues colonized by spiderwebs. Unlabelled power cables for obsolete electronics. Once when browsing through this I encountered two things that I bought as badges of office.

One was a slide rule, a proper twelve-inch one. I’d had one already, a \$2 six-inch-long one I’d gotten as an undergraduate from a convenience store the university had already decided to evict. The NUS one was a slide rule you could do actual work on. Another was a soroban, a compact Japanese abacus, in a patterned cardboard box a half-inch too short to hold it. I got both. For the novelty, yes. Also, I taught Computational Science. I felt it appropriate to have these iconic human computing devices.

But do I use them? Other than for decoration? … No, not really. I have too many calculators to need them. Also I am annoyed that while I can lay my hands on the slide rule I have put the soroban somewhere so logical and safe I can’t find it. A couple photographs would improve this essay. Too bad.

Do I know how to use them? If I find them? The slide rule, sure. If you know that a slide rule works via logarithms, and you play with it a little? You know how to use a slide rule. At least a little, after a bit of experimentation and playing with the three times table.

The abacus, though? How do you use that?

In childhood I heard about abacuses. That there’s a series of parallel rods, each with beads on them. Four placed below a center beam, one placed above. Sometimes two placed above. That the lower beads on a rod represent one each. That the upper bead represents five. That some people can do arithmetic on that faster than others can an electric calculator. And that was about all I got, or at least retained. How to do this arithmetic never penetrated my brain. I imagined, well, addition must be easy. Say you wanted to do three plus six, well, move three lower beads up to the center bar. Then slide one lower and one upper bead, for six, to the center bar, and read that off. Right?

The bizarre thing is my naive childhood idea is right. At least in the big picture. Let each rod represent one of the numbers in base-ten style. It’s anachronistic to the abacus’s origins to speak of a ones rod, a tens rod, a hundreds rod, and so on. So what? We’re using this tool today. We can use the ideas of base ten to make our understanding easier.

Pick a row of beads that you want to represent the ones. The row to the left of that represents tens. To the left of that, hundreds. To the right of the ones is the one-tenths, and the one-hundredths, and so on. This goes on to however far your need and however big your abacus is.

Move beads to the center to represent numbers you want. If you want ’21’, slide two lower beads up in the tens column and one lower bead in the ones column. If you want ’38’, slide three lower beads up in the tends column and one upper and three lower beads in the ones column.

To add two numbers, slide more beads representing those numbers toward the center bar. To subtract, slide beads away. Multiplication and division were beyond my young imagination. I’ll let them wait a bit.

There are complications. The complications are for good reason. When you master them, they make computation swifter. But you pay for that later speed with more time spent learning. This is a trade we make, and keep making, in computational mathematics. We make a process more reliable, more speedy, by making it less obvious.

Some of this isn’t too difficult. Like, work in one direction so far as possible. It’s easy to suppose this is better than jumping around from, say, the thousands digit to the tens to the hundreds to the ones. The advice I’ve read says work from the left to the right, that is, from the highest place to the lowest. Arithmetic as I learned it works from the ones to the tens to the hundreds, but this seems wiser. The most significant digits get calculated first this way. It’s usually more important to know the answer is closer to 2,000 than to 3,000 than to know that the answer ends in an 8 rather than a 6.

Some of this is subtle. This is to cope with practical problems. Suppose you want to add 5 to 6? There aren’t that many beads on any row. A Chinese abacus, which has two beads on the upper part, could cope with this particular problem. It’s going to be in trouble when you want to add 8 to 9, though. That’s not unique to an abacus. Any numerical computing technique can be broken by some problem. This is why it’s never enough to calculate; we still have to think. For example, thinking will let us handle this five plus six difficulty.

Consider this: four plus one is five. So four and one are “complementary numbers”, with respect to five. Similarly, three and two are five’s complementary numbers. So if we need to add four to a number, that’s equivalent to adding five and subtracting one. If we need to add two, that’s equivalent to adding five and subtracting three. This will get us through some shortages in bead count.

And consider this: four plus six is ten. So four and six are ten-complementary numbers. Similarly, three and seven are ten’s complementary numbers. Two and eight. One and nine. This gets us through much of the rest of the shortage.

So here’s how this works. Suppose we have 35, and wish to add 6 to it. There aren’t the beads to add six to the ones column. So? Instead subtract the complement of six. That is, add ten and subtract four. In moving across the rows, when you reach the tens, slide one lower bead up, making the abacus represent 45. Then from the ones column subtract four. that is, slide the upper bead away from the center bar, and slide the complement to four, one bead, up to the center. And now the abacus represents 41, just like it ought.

If you’re experienced enough you can reduce some of these operations, sliding the beads above and below the center bar at once. Or sliding a bead in the tens and another in the ones column at once. Don’t fret doing this. Worry about making correct steps. You’ll speed up with practice. I remember advice from a typesetting manual I collected once: “strive for consistent, regular keystrokes. Speed comes with practice. Errors are time-consuming to correct”. This is, mutatis mutandis, always good advice.

Subtraction works like addition. This should surprise few. If you have the beads in place, just remove them: four minus two takes no particular insight. If there aren’t enough beads? Fall back on complements. If you wish to do 35 minus 6? Set up 35, and calculate 35 minus 10 plus 4. When you get to the tens rod, slide one bead down; this leaves you with 25. Then in the ones column, slide four beads up. This leaves you with 29. I’m so glad these seem to be working out.

Doing longer additions and subtractions takes more rows, but not actually more work. 35.2 plus 6.4 is the same work as 35 plus 6 and 2 plus 4, each of which you, in principle, know how to do. 35.2 minus 6.4 is a bit more fuss. When you get to the 2 minus 4 bit you have to do that addition-of-complements stuff. But that’s not any new work.

Where the decimal point goes is something you have to keep track of. As with the slide rule, the magnitude of these numbers is notional. Your fingers move the same way to add 352 and 64 as they will 0.352 and 0.064. That’s convenient.

Multiplication gets more tedious. It demands paying attention to where the decimal point is. Just like the slide rule demands, come to think of it. You’ll need columns on the abacus for both the multiplicands and the product. And you’ll do a lot of adding up. But at heart? 2038 times 3.7 amounts to doing eight multiplications. 8 times 7, 3 times 7, 0 times 7 (OK, that one’s easy), 2 times 7, 3 times 7, 3 times 3, 0 times 3 (again, easy), and 2 times 3. And then add up these results in the correct columns. This may be tedious, but it’s not hard. It does mean the abacus doesn’t spare you having to know some times tables. I mean, you could use the abacus to work out 8 times 7 by adding seven to itself over and over, but that’s time-consuming. You can save time, and calculation steps, by memorization. By knowing some answers ahead of time.

Totton Heffelfinger and Gary Flom’s page, from which I’m drawing almost all my practical advice, offers a good notation of lettering the rods of the abacus, A, B, C, D, and so on. To multiply, say, 352 by 64 start by putting the 64 on rods BC. Set the 352 on rods EFG. We’ll get the answer with rod K as the ones column. 2 times 4 is 8; put that on rod K. 5 times 4 is 20; add that to rods IJ. 3 times 4 is 12; add that to rods HI. 2 times 6 is 12; add that to rods IJ. 5 times 6 is 30; add that to rods HI. 3 times 6 is 18; add that to rods GH. All going well this should add up to 22,528, spread out along rods GHIJK. I can see right away at least the 8 is correct.

You would do the same physical steps to multiply, oh, 3.52 by 0.0064. You have to take care of the decimal place yourself, though.

I see you, in the back there, growing suspicious. I’ll come around to this. Don’t worry.

Division is … oh, I have to fess up. Division is not something I feel comfortable with. I can read the instructions and repeat the examples given. I haven’t done it enough to have that flash where I understand the point of things. I recognize what’s happening. It’s the work of division as done by hand. You know, 821 divided by 56 worked out by, well, 56 goes into 82 once with a remainder of 26. Then drop down the 1 to make this 261. 56 goes into 261 … oh, it would be so nice if it went five times, but it doesn’t. It goes in four times, with a remainder of 37. I can walk you through the steps but all I am truly doing is trying to keep up with Totton Heffelfinger and Gary Flom’s instructions here.

There are, I read, also schemes to calculate square roots on the abacus. I don’t know that there are cube-root schemes also. I would bet on there being such, though.

Never mind, though. The suspicious thing I expect you’ve noticed is the steps being done. They’re represented as sliding beads along rods, yes. But the meaning of these steps? They’re the same steps you would do by doing arithmetic on paper. Sliding two beads and then two more beads up to the center bar isn’t any different from looking at 2 + 2 and representing that as 4. All this ten’s-complement stuff to subtract one number from another is just … well, I learned it as subtraction by “borrowing”. I don’t know the present techniques but I’m sure they’re at heart the same. But the work is eerily like what you would do on paper, using Arabic numerals.

The slide rule uses a logarithm-based ruler. This makes the addition of distances along the slides match the multiplication of the values of the rulers. What does the abacus do to help us compute?

Why use an abacus?

What an abacus gives us is memory. It stores numbers. It lets us break a big problem into a series of small problems. It lets us keep a partial computation while we work through those steps. We don’t add 35.2 to 6.4. We add 3 to 0 and 5 to 6 and 2 to 4. We don’t multiply 2038 by 3.7. We multiply 8 by 7, and 8 by 3, and 3 by 7, and 3 by 3, and so on.

And this is most of numerical computing, even today. We describe what we want to do as these high-level operations. But the computation is a lot of calculations, each one of them simple. We use some memory to hold partially completed results. Memory, the ability to store results, lets us change hard problems into long strings of simple ones.

We do more things the way the abacus encourages. We even use those complementary numbers. Not five’s or ten’s complements, not with binary arithmetic computers. Two’s complement arithmetic makes it possible to subtract, or write negative numbers, in ways that are easy to calculate. That there are a set number of rods even has its parallel in modern computing. When representing a real number on the computer we have only so many decimal places. (Yes, yes, binary digit places.) At least unless we use a weird data structure. This affects our calculations. There are numbers we can’t represent perfectly, such as one-third. We need to think about whether this affects what we are using our calculation for.

There are major differences between a digital computer and a person using the abacus. But the processes are similar. This may help us to understand why computational science works the way it does. It may at least help us understand those contests in the 1950s where the abacus user was faster than the calculator user.

But no, I confess, I only use mine for decoration, or will when I find it again.

Thank you for reading. All the Fall 2019 A To Z posts should be at this link. Furthermore, both this year’s and all past A To Z sequences should be at this link. And I am still soliciting subjects for the first third of the alphabet.

## I Ask For The First Topics For My Fall 2019 Mathematics A-To-Z

And a good late August to all my readers. I’m as ready as can be for my Fall 2019 Mathematics A-To-Z. For this I hope to explore one word or concept for each letter in the alphabet, one essay for each. I’m trying, as I did last year, to publish just two essays per week. I like to think this will keep my writing load from being too much. I’m fooling only myself.

For topics, though, I like to ask readers for suggestions. And I’ll be asking just for parts of the alphabet at a time. I’ve found this makes it easier for me to track suggestions. It also makes it easier for me to think about which subjects I feel I can write the most interesting essay about. This is in case I get more than one nomination for a particular letter. That’s hardly guaranteed, but I do like thinking this might happen.

If you do leave a suggestion, please also mention whether you host your own blog or YouTube channel or Twitter or Mathstodon account. Anything that you’d like people to know about.

I’ve done five of these A To Z sequences before, from 2015 through to last year. I won’t necessarily refuse to re-explore something I’ve already written up. There are certainly ones I could improve, given another chance. But I’d probably look to write about a fresh topic.

I hope to start the first week of September, mostly so that I end by (United States) Thanksgiving. The letters that I would like to finish by September are the first eight, A through H. Covered in past years from this have been:

### H.

Thank you for any thoughts you have. Please ask if there are any questions. And I intend for this to be open to topics in any field of mathematics, including the ones I don’t really know. Writing about something I’m just learning about is terrifying and fun. It’s a large part of why I do these things every year, and also why I don’t do them more than once a year.

## The Fall 2019 Mathematics A To Z Is Looking Good

I’m happy to say the most important part of preparing an A To Z sequence has come in. I have my banner art. It’s by Thomas K Dye, creator of the Projection Edge and Newshounds web comics. He keeps up a Patreon at this account.

The A To Z is about my most successful tradition. In it I write essays that are never as short as I think they should be, one for a concept from each letter of the alphabet. And I take nominations from readers for these concepts. If there’s more than one nomination I’ll go for whatever I think I can write the most interesting piece about. But if several things seem interesting I might try rephrasing them, which is how I got into the whole continued fractions trouble.

Before I take nominations I’ll post indexes to the past A to Z’s. I’m willing to revisit a topic I’ve already written about, since I hope I’m getting better at both mathematics and writing. But I’m inclined more to try new stuff where I can. Also the end of the alphabet, the X’s and Y’s and such, tend to be pretty dire. It’s not too soon to start thinking up possibilities.

## I Plan To Do A Fall 2019 Mathematics A To Z

Long-time readers may have felt like something’s missing from my 2019 writing around here. It is an A To Z. This is one of my traditions, to write essays explaining some mathematical concept or related term, one for each letter of the alphabet. I’ve traditionally taken nominations from readers for this, and I plan to do so again.

I’m not taking nominations just yet. I’d like people to have the chance to think about stuff they’d like to see explained. This is how we get things like people asking me what the Ricci tensor is and why we need it. I’m still not perfectly sure I know why we need it, but the essay is out there, so that’s something.

Anyway I have banner art commissioned, again from Thomas K Dye of the Projection Edge and Newshounds web comics, and whose Patreon is here. When I have that I’ll open up at least part of the alphabet to subject nominations.

Also I continue to have the problem where Twitter won’t load on my web browser, Safari. I’m getting close to desperate measures, such as restarting my computer or trying to load it on a different web browser. If you wonder why I am even more quiet on Twitter than usual, this is one reason why.