# A Programmer’s Introduction to Mathematics

For the last four years I’ve been working on a book for programmers who want to learn mathematics. It’s finally done, and you can buy it today.

The website for the book is pimbook.org, which has purchase links—paperback and ebook—and a preview of the first pages. You can see more snippets later in the book on the Amazon listing’s “Look Inside” feature.

If you’re a programmer who wants to learn math, this book is written specifically for you! Why? Because programming and math are naturally complementary, and programmers have a leg up in learning math. Many of the underlying modes of thought in mathematics are present in programming, or are otherwise easy to explain by analogies and contrasts to familiar concepts in software. I leverage that in the book so that you can internalize the insights quickly, and appreciate the nuance more deeply than most books can allow. This book is a bridge from the world of programming to the world of math from the mathematician’s perspective. As far as I know, no other book provides this.

Programs make math more interesting and applicable than otherwise. Typical math writers often hold computation and algorithms at a healthy distance. Not us. We embrace computation as a prize and a principle worth fighting for. Each chapter of the book culminates in an exciting program that applies the mathematical insights from the chapter to an interesting application. The applications include cryptographic schemes, machine learning, drawing hyperbolic tessellations, and a Nobel-prize winning algorithm from economics.

The exercises of the book also push you beyond the book itself. There’s so much math out there that you can’t learn it from a single book. Perspectives and elaborations are spread throughout books, papers, blog posts, wikis, lecture notes, math magazines, and your own scratch paper. This book will prepare you to read a variety of sources by introducing you to the standard language of math, and also push you to engage with those resources.

Finally, this book includes a healthy dose of culture. Quotes and passages from the writings of famous mathematicians, contextual explanations of cultural attitudes, and a light dose of history will provide a peek into why mathematics is the way it is today, and why at times it can seem so confounding to an outsider. Through all this, I will show what progress means for math, what attitudes and patterns will help you along the way, and how to stay sane.

Of course, I couldn’t have written the book without the encouragement and support of you, my readers. Thank you for reading, commenting, and supporting me all these years.

Order the book today! I can’t wait to hear what you think 🙂

I am down to the home stretch for publishing my upcoming book, “A Programmer’s Introduction to Mathematics.” I don’t have an exact publication date—I’m self publishing—but after months of editing, I’ve only got two chapters left in which to apply edits that I’ve already marked up in my physical copy. That and some notes from external reviewers, and adding jokes and anecdotes and fun exercises as time allows.

I’m committing to publishing by the end of the year. When that happens I’ll post here and also on the book’s mailing list. Here’s a sneak preview of the table of contents. And a shot of the cover design (still a work in progress)

# Hanabi: a card game for logicians

Mathematics students often hear about the classic “blue-eyed islanders” puzzle early in their career. If you haven’t seen it, read Terry Tao’s excellent writeup linked above. The solution uses induction and the idea of common knowledge—I know X, and you know that I know X, and I know that you know that I know X, and so on—to make a striking inference from a seemingly useless piece of information.

Recreational mathematics is also full of puzzles involving prisoners wearing colored hats, where they can see others hats but not their own, and their goal is to each determine (often with high probability) the color of their own hat. Sometimes they are given the opportunity to convey a limited amount of information.

Over the last eight years I’ve been delighted by the renaissance of independent tabletop board and card games. Games leaning mathematical, like Set, have had a special place in my heart. Sadly, many games of incomplete information often fall to an onslaught of logic. One example is the popular game The Resistance (a.k.a. Avalon), in which players with unknown allegiances must either deduce which players are spies, or remain hidden as a spy while foiling a joint goal. With enough mathematicians playing, it can be easy to dictate a foolproof strategy. If we follow these steps, we can be 100% sure of victory against the spies, so anyone who disagrees with this plan or deviates from it is guaranteed to be a spy. Though we’re clearly digging a grave for our fun, it’s hard to close pandora’s logic box after it’s open. So I’m always on the lookout for games that resist being trivialized.

Enter Hanabi.

A friend recently introduced me to the game, which channels the soul and complexion of the blue-eyed islanders and hat-donning prisoners into a delightful card game.

The game has simple rules: each player gets a hand that they may not see, but they reveal to all other players. The hands come from the following set of cards (with more 1’s than 2’s, and the fewest 5’s), and players work together, aiming to place cards from 1-5 in order in each color. It’s like solitaire, where stacks of different colors may progress independently, but a 2 must be placed before a 3.

Then the players take turns, and on each turn a player may do one of the following:

1. Choose a card from your hand to play. If the chosen card cannot be played (e.g, it’s a red 3 but only a red 1 is on the table), everyone gets a strike. Three strikes ends the game in a loss.
2. Use an information token (limited in supply) to give one piece of information to one other player; the allowed types of information are explained below.
3. Choose a card from your hand to discard, and regain an information token for future use.

The information you can give to a player to choosing a single feature (a specific rank or color), and pointing to all cards in that player’s hand that have that feature. Example: “these two cards are green”, or “this card is a 4”. House rules dictate whether “no cards are blue” is a valid piece of information. Officially—I like to think it’s in the spirit of the blue-eyed islander’s puzzle where “someone has blue eyes”—you must be able to point at something to reveal information about it.

So the game involves some randomness (the draw), and some resource management (the information tokens), but the heart of the game is figuring out how to convey as much information as possible in a single clue.

Just like the blue-eyed islander’s puzzle, giving a public piece of information to one player can indicate much more. Imagine their are 4 players. I can see your hand, but if, after looking at your hand, I decide instead to give Blair a clue, that gives you information that what’s in Blair’s hand is more valuable for me to reveal to her than what’s in your hand would be to reveal to you.

Another trick: say I know I have a 4, and say it’s the beginning of the game where 4’s are not playable, and the board has a blue 1 on it. If you play before me and you tell me that that same 4 and a second card are both blue, what does that tell me? It was certainly somewhat redundant: you told me more information about a card I knew was not playable, and seemingly not super-helpful information about a second card. After some reflection you can often infer that not only is the second card a blue 2, but also that you have at least one more 2 elsewhere in your hand that’s not immediately playable. That’s a lot of information!

The idea of common knowledge takes it down a rabbit hole that I haven’t quite gotten my head around, but which makes the game continually fun. If I know that you know that I can infer the above scenario with the blue 2, then you not giving me that clue tells me that either that situation isn’t present in my hand, or else that whatever information you’re instead giving to Matthieu is a higher priority. The more the group can understand to be commonly inferable (say, discussing strategies before starting the game), the more one can take advantage of common knowledge. The game starts to feel like a logical olympiad, where your worst enemy is your fallible memory, and if people aren’t playing at the same level, relying too much on an inference your teammate didn’t intend can cause grave mistakes!

It’s a guaranteed hit at your next gathering of logic-loving mathemalites!

# For mathematicians, = does not mean equality

Every now and then I hear some ridiculous things about the equals symbol. Some large subset of programmers—perhaps related to functional programmers, perhaps not—seem to think that = should only and ever mean “equality in the mathematical sense.” The argument usually goes,

Functional programming gives us back that inalienable right to analyze things by using mathematics. Never again need we bear the burden of that foul mutant x = x+1! No novice programmer—nay, not even a mathematician!—could comprehend such flabbergastery. Tis a pinnacle of confusion!

It’s ironic that so much of the merits or detriment of the use of = is based on a veiled appeal to the purity of mathematics. Just as often software engineers turn the tables, and any similarity to mathematics is decried as elitist jibber jabber (Such an archaic and abstruse use of symbols! Oh no, big-O!).

In fact, equality is more rigorously defined in a programming language than it will ever be in mathematics. Even in the hottest pits of software hell, where there’s = and == and ===, throwing in ==== just to rub salt in the wound, each operator gets its own coherent definition and documentation. Learn it once and you’ll never go astray.

Not so in mathematics—oh yes, hide your children from the terrors that lurk. In mathematics equality is little more than a stand-in for the word “is,” oftentimes entirely dependent on context. Now gather round and listen to the tale of the true identities of the masquerader known as =.

$\displaystyle \sum_{i=1}^n i^2 + 3$

If = were interpreted literally, $i$ would be “equal” to 1, and “equal” to 2, and I’d facetiously demand $1 = 2$. Aha! Where is your Gauss now?! But seriously, this bit of notation shows that mathematics has both expressions with scope and variables that change their value over time. And the $\sum$ use for notation was established by Euler, long before algorithms jumped from logic to computers to billionaire Senate testimonies.

Likewise, set-builder notation often uses the same kind of equals-as-iterate.

$\displaystyle A = \{ n^2 : n = 1, 2, \dots, 100 \}$

In Python, or interpreting the expression literally, the value of $n$ would be a tuple, producing a type error. (In Javascript, it produces 2. How could it be Javascript if it didn’t?)

Next up we have the sloppiness of functions. Let $f(x) = 2x + 3$. This is a function, and $x$ is a variable. Rather than precisely say, $f(2) = 7$, we say that for $x=2, f(x) = 7$. So $x$ is simultaneously an indeterminate input and a concrete value. The same scoping for programming functions bypass the naive expectation that equality means “now and forever.” Couple that with the question-as-equation $f(x) = 7$, in which one asks what values of $x$ produce this result, if any, and you begin to see how deep the rabbit hole goes. To understand what someone means when they say $f(x) = 7$, you need to know the context.

But this is just the tip of the iceberg, and we’re drilling deep. The point is that = carries with it all kinds of baggage, not just the scope of a particular binding of a variable.

Continuing with functions, we have rational expressions like $f(x) = \frac{(x+1)x}{x}$. One often starts by saying “let’s let $f$ be this function.” Then we want to analyze it, and in-so-doing we simplify to $f(x) = x+1$. To keep ourselves safe, we modify the domain of $f$ to exclude $x=0$ post-hoc. But the flow of the argument is the same: we defer the exclusion of $x=0$ until we need it, meaning the equality at the beginning is a different equality than at the end. In effect, we have an infinitude of different kinds of equality for functions, one for each choice of what to exclude from the domain. And a mathematical proof might switch between them as needed.

“Why not just define a new function $g$ with a different domain,” you ask? You can, but mathematicians don’t. And if you’re arguing in favor or against a particular notation, and using “mathematics” as your impenetrable shield, you’ve got to remember the famous definition of Reuben Hersh, that “mathematics is what mathematicians do.” For us, that means you can’t claim superiority based on an idea of mathematics that disagrees with mathematical practice. And mathematics, dear reader, is messier than programmers and philosophers would have one believe.

And now we turn to the Great Equality Contextualizer, the isomorphism.

You see, all over mathematics there are objects which are not equal, but we want them to be. When you study symmetry, say, you learn that there is an algebraic structure to symmetry called a group. And the same structure—that is, the same true underlying relationships between the symmetries of a thing—can show up in many different guises. As a set, as a picture, as a class of functions, in polynomials and compass constructions and wallpapers, oh my! In each of these things we want to say that two symmetry structures are the same even if they look different. We want to overload equality when four-fold rotational symmetry applies to my table as well as a four-pointed star.

The tool we use for that is called an isomorphism. In brief terms, it’s a function between two objects, with an inverse, that preserves the structure you care about both ways. In fact, there is a special symbol for when two things are isomorphic, and it’s often $\cong$. But $\cong$ is annoying to write, and it really just means “is the same as” the same way equality does. So mathematicians often drop the squiggle and use =.

Plus, there are a million kinds of isomorphism. Groups, graphs, vector spaces, rings, fields, modules, algebras, rational functions, varieties, Lie groups, *breathe* topological spaces, manifolds of all stripes, sheaves, schemes, lattices, knots, the list just keeps going on and on and on! No way are we making up a symbol for each one of these and the hundreds of variations we might come up with. And moreover, when you say two things are isomorphic, that gives you absolutely no indication of how they are isomorphic. It fact, it can be extremely tedious to compute isomorphisms between things, and it’s even known to be uncomputable in extreme cases! What good is equality if you can’t even check it?

But wait! You might ask, having read this blog for a while and knowing better than to not question a claim. All of these uses of equality are still equivalence relations, and x = x + 1 is not an equivalence relation!

Well, you got me there. Mathematicians love to keep equality as an equivalence relation. When mathematicians need to define an algorithm where the value of $x$ changes in a nontrivial way, it’s usually done by setting $x_0$ equal to some starting value and letting $x_{n}$ be defined as some function of $x_{n-1}$ and smaller terms, like the good ol’ Fibonacci sequence $x_0 = x_1 = 1$ and $x_n = x_{n-1} + x_{n-2}$.

If mutation is so great, why do mathematicians use recursion so much? Huh? Huh?

Well, I’ve got two counterpoints. The first is that the goal here is to reason about the sequence, not to describe it in a way that can be efficiently carried out by a computer. When you say x = x + 1, you’re telling the computer that the old value of x need not linger, and you can do away with the space occupied by the previous value of x. To achieve the same result with recursion requires a whole other can of worms: memoization and tail recursive style and compiler optimizations to shed stack frames. It’s a lot more work to understand all that (to get to an equivalent solution) than it is to understand mutation! Simply stated, the goals of mathematics and programming are quite differently aligned. The former is about understanding a thing, and the latter is more often about describing a concrete process under threat of limited resources.

My second point is that mathematical notation is so flexible and adaptable that it doesn’t need mutation the same way programming languages need it. In mathematics we have no stack overflows, no register limits or page swaps, no limitations on variable names or memory allocation, our brains do the continuation passing for us, and we can rewrite history ad hoc and pile on abstractions as needed to achieve a particular goal. Even when you’re describing an algorithm in mathematics, you get the benefits of mathematical abstractions. A mathematician could easily introduce = as mutation in their work. Nothing stops them from doing so! It’s just that they rarely have a genuine need for it.

But of course, none of this changes that languages could use := or “let” instead of = for assignment. If a strict adherence to asymmetry for asymmetric operations helps you sleep at night, so be it. My point is that the case when = means assignment is an extremely simple bit of context. Much simpler than the albatrossian mental burden required to understand what mathematicians really mean when they write $A = B$.

Postscript: I hope everyone reading this realizes I’m embellishing a bit for the sake of entertainment. If you want to fight me, tell me the best tree isn’t aspen. I dare you.

Postpostscript: embarrassingly, I completely forgot about Big-O notation and friends (despite mentioning it in the article!) as a case where = does not mean equality! f(n) = O(log n) is a statement about upper bounds, not equality! Thanks to @lreyzin for keeping me honest.