## My Little 2021 Mathematics A-to-Z: Tangent Space

And now, finally, I resume and hopefully finish what was meant to be a simpler and less stressful A-to-Z for last year. I’m feeling much better about my stress loads now and hope that I can soon enjoy the feeling of having a thing accomplished.

This topic is one of many suggestions that Elkement, one of my longest blog-friendships here, offered. It’s a creation that sent me back to my grad school textbooks, some of those slender paperback volumes with tiny, close-set type that turn out to be far more expensive than you imagine. Though not in this case: my most useful reference here was V I Arnold’s Ordinary Differential Equations, stamped inside as costing \$18.75. The field is full of surprises. Another wonderful reference was this excellent set of notes prepared by Jodin Morey. They would have done much to help me through that class.

# Tangent Space

Stand in midtown Manhattan, holding a map of midtown Manhattan. You have — not a tangent space, not yet. A tangent plane, representing the curved surface of the Earth with the flat surface of your map, though. But the tangent space is near: see how many blocks you must go, along the streets and the avenues, to get somewhere. Four blocks north, three west. Two blocks south, ten east. And so on. Those directions, of where you need to go, are the tangent space around you.

There is the first trick in tangent spaces. We get accustomed, early in learning calculus, to think of tangent lines and then of tangent planes. These are nice, flat approximations to some original curve. But while we’re introduced to the tangent space, and first learn examples of it, as tangent planes, we don’t stay there. There are several ways to define tangent spaces. One recasts tangent spaces in group theory terms, describing them as a ring based on functions that are equal to zero at the tangent point. (To be exact, it’s an ideal, based on a quotient group, based on two sets of such functions.)

That’s a description mathematicians are inclined to like, not only because it’s far harder to imagine than a map of the city is. But this ring definition describes the tangent space in terms of what we can do with it, rather than how to calculate finding it. That tends to appeal to mathematicians. And it offers surprising insights. Cleverer mathematicians than I am notice how this makes tangent spaces very close to Lagrange multipliers. Lagrange multipliers are a technique to find the maximum of a function subject to a constraint from another function. They seem to work by magic, and tangent spaces will echo that.

I’ll step back from the abstraction. There’s relevant observations to make from this map of midtown. The directions “four blocks north, three west” do not represent any part of Manhattan. It describes a way you might move in Manhattan, yes. But you could move in that direction from many places in the city. And you could go four blocks north and three west if you were in any part of any city with a grid of streets. It is a vector space, with elements that are velocities at a tangent point.

The tangent space is less a map showing where things are and more one of how to get to other places, closer to a subway map than a literal one. Still, the topic is steeped in the language of maps. I’ll find it a useful metaphor too. We do not make a map unless we want to know how to find something. So the interesting question is what do we try to find in these tangent spaces?

There are several routes to tangent spaces. The one I’m most familiar with is through dynamical systems. These are typically physics-driven, sometimes biology-driven, problems. They describe things that change in time according to ordinary differential equations. Physics problems particularly are often about things moving in space. Space, in dynamical systems, becomes “phase space”, an abstract universe spanned by all of the possible values of the variables. The variables are, usually, the positions and momentums of the particles (for a physics problem). Sometimes time and energy appear as variables. In biology variables are often things that represent populations. The role the Earth served in my first paragraph is now played by a manifold. The manifold represents whatever constraints are relevant to the problem. That’s likely to be conservation laws or limits on how often arctic hares can breed or such.

The evolution in time of this system, though, is now the tracing out of a path in phase space. An understandable and much-used system is the rigid pendulum. A stick, free to swing around a point. There are two useful coordinates here. There’s the angle the stick makes, relative to the vertical axis, $\theta$. And there’s how fast the stick is changing, $\dot{\theta}$. You can draw these axes; I recommend $\theta$ as the horizontal and $\dot{\theta}$ as the vertical axis but, you know, you do you.

If you give the pendulum a little tap, it’ll swing back and forth. It rises and moves to the right, then falls while moving to the left, then rises and moves to the left, then falls and moves to the right. In phase space, this traces out an ellipse. It’s your choice whether it’s going clockwise or anticlockwise. If you give the pendulum a huge tap, it’ll keep spinning around and around. It’ll spin a little slower as it gets nearly upright, but it speeds back up again. So in phase space that’s a wobbly line, moving either to the right or the left, depending what direction you hit it.

You can even imagine giving the pendulum just the right tap, exactly hard enough that it rises to vertical and balances there, perfectly aligned so it doesn’t fall back down. This is a special path, the dividing line between those ellipses and that wavy line. Or setting it vertically there to start with and trusting no truck driving down the street will rattle it loose. That’s a very precise dot, where $\dot{\theta}$ is exactly zero. These paths, the trajectories, match whatever walking you did in the first paragraph to get to some spot in midtown Manhattan. And now let’s look again at the map, and the tangent space.

Within the tangent space we see what changes would change the system’s behavior. How much of a tap we would need, say, to launch our swinging pendulum into never-ending spinning. Or how much of a tap to stop a spinning pendulum. Every point on a trajectory of a dynamical system has a tangent space. And, for many interesting systems, the tangent space will be separable into two pieces. One of them will be perturbations that don’t go far from the original trajectory. One of them will be perturbations that do wander far from the original.

These regions may have a complicated border, with enclaves and enclaves within enclaves, and so on. This can be where we get (deterministic) chaos from. But what we usually find interesting is whether the perturbation keeps the old behavior intact or destroys it altogether. That is, how we can change where we are going.

That said, in practice, mathematicians don’t use tangent spaces to send pendulums swinging. They tend to come up when one is past studying such petty things as specific problems. They’re more often used in studying the ways that dynamical systems can behave. Tangent spaces themselves often get wrapped up into structures with names like tangent bundles. You’ll see them proving the existence of some properties, describing limit points and limit cycles and invariants and quite a bit of set theory. These can take us surprising places. It’s possible to use a tangent-space approach to prove the fundamental theorem of algebra, that every polynomial has at least one root. This seems to me the long way around to get there. But it is amazing to learn that is a place one can go.

I am so happy to be finally finishing Little 2021 Mathematics A-to-Z. All of this project’s essays should be at this link. And all my glossary essays from every year should be at this link. Thank you for reading.

## My All 2020 Mathematics A to Z: Velocity

I’m happy to be back with long-form pieces. This week’s is another topic suggested by Mr Wu, of the Singapore Maths Tuition blog.

# Velocity.

This is easy. The velocity is the first derivative of the position. First derivative with respect to time, if you must know. That hardly needed an extra week to write.

Yes, there’s more. There is always more. Velocity is important by itself. It’s also important for guiding us into new ideas. There are many. One idea is that it’s often the first good example of vectors. Many things can be vectors, as mathematicians see them. But the ones we think of most often are “some magnitude, in some direction”.

The position of things, in space, we describe with vectors. But somehow velocity, the changes of positions, seems more significant. I suspect we often find static things below our interest. I remember as a physics major that my Intro to Mechanics instructor skipped Statics altogether. There are many important things, like bridges and roofs and roller coaster supports, that we find interesting because they don’t move. But the real Intro to Mechanics is stuff in motion. Balls rolling down inclined planes. Pendulums. Blocks on springs. Also planets. (And bridges and roofs and roller coaster supports wouldn’t work if they didn’t move a bit. It’s not much though.)

So velocity shows us vectors. Anything could, in principle, be moving in any direction, with any speed. We can imagine a thing in motion inside a room that’s in motion, its net velocity being the sum of two vectors.

And they show us derivatives. A compelling answer to “what does differentiation mean?” is “it’s the rate at which something changes”. Properly, we can take the derivative of any quantity with respect to any variable. But there are some that make sense to do, and position with respect to time is one. Anyone who’s tried to catch a ball understands the interest in knowing.

We take derivatives with respect to time so often we have shorthands for it, by putting a ‘ mark after, or a dot above, the variable. So if x is the position (and it often is), then $x'$ is the velocity. If we want to emphasize we think of vectors, $\vec{x}$ is the position and $\vec{x}'$ the velocity.

Velocity has another common shorthand. This is $v$, or if we want to emphasize its vector nature, $\vec{v}$. Why a name besides the good enough $\vec{x}'$? It helps us avoid misplacing a ‘ mark in our work, for one. And giving velocity a separate symbol encourages us to think of the velocity as independent from the position. It’s not — not exactly — independent. But knowing that a thing is in the lawn outside tells us nothing about how it’s moving. Velocity affects position, in a process so familiar we rarely consider how there’s parts we don’t understand about it. But velocity is also somehow also free of the position at an instant.

Velocity also guides us into a first understanding of how to take derivatives. Thinking of the change in position over smaller and smaller time intervals gets us to the “instantaneous” velocity by doing only things we can imagine doing with a ruler and a stopwatch.

Velocity has a velocity. $\vec{v}'$, also known as $\vec{a}$. Or, if we’re sure we won’t lose a ‘ mark, $\vec{x}''$. Once we are comfortable thinking of how position changes in time we can think of other changes. Velocity’s change in time we call acceleration. This is also a vector, more abstract than position or velocity. Multiply the acceleration by the mass of the thing accelerating and we have a vector called the “force”. That, we at least feel we understand, and can work with.

Acceleration has a velocity too, a rate of change in time. It’s called the “jerk” by people telling you the change in acceleration in time is called the “jerk”. (I don’t see the term used in the wild, but admit my experience is limited.) And so on. We could, in principle, keep taking derivatives of the position and keep finding new changes. But most physics problems we find interesting use just a couple of derivatives of the position. We can label them, if we need, $\vec{x}^{(n)}$, where n is some big enough number like 4.

We can bundle them in interesting ways, though. Come back to that mention of treating position and velocity of something as though they were independent coordinates. It’s a useful perspective. Imagine the rules about how particles interacting with one another and with their environment. These usually have explicit roles for position and velocity. (Granting this may reflect a selection bias. But these do cover enough interesting problems to fill a career.)

So we create a new vector. It’s made of the positition and the velocity. We’d write it out as $(x, v)^T$. The superscript-T there, “transposition”, lets us use the tools of matrix algebra. This vector describes a point in phase space. Phase space is the collection of all the physically possible positions and velocities for the system.

What’s the derivative, in time, of this point in phase space? Glad to say we can do this piece by piece. The derivative of a vector is the derivative of each component of a vector. So the derivative of $(x, v)^T$ is $(x', v')^T$, or, $(v, a)^T$. This acceleration itself depends on, normally, the positions and velocities. So we can describe this as $(v, f(x, v))^T$ for some function $f(x, v)$. You are surely impressed with this symbol-shuffling. You are less sure why this bother.

The bother is a trick of ordinary differential equations. All differential equations are about how a function-to-be-determined and its derivatives relate to one another. In ordinary differential equations, the function-to-be-determined depends on a single variable. Usually it’s called x or t. There may be many derivatives of f. This symbol-shuffling rewriting takes away those higher-order derivatives. We rewrite the equation as a vector equation of just one order. There’s some point in phase space, and we know what its velocity is. That we do because in this form many problems can be written as a matrix problem: $\vec{x}' = A\vec{x}$. Or approximate our problem as a matrix problem. This lets us bring in linear algebra tools, and that’s worthwhile.

It also lets us bring in numerical tools. Numerical mathematics has developed many methods to solve the ordinary differential equation $x' = f(x)$. Most of them extend to $\vec{x}' = f(\vec{x})$. The result is a classic mathematician’s trick. We can recast a problem as one we have better tools to solve.

It calls on a more abstract idea of what a “velocity” might be. We can explain what the thing that’s “moving” and what it’s moving through are, given time. But the instincts we develop from watching ordinary things move help us in these new territories. This is also a classic mathematician’s trick. It may seem like all mathematicians do is develop tricks to extend what they already do. I can’t say this is wrong.

Thank you all for reading and for putting up with my gap week. This and all of my 2020 A-to-Z essays should be at this link. All the essays from every A-to-Z series should be at this link.

## My All 2020 Mathematics A to Z: J Willard Gibbs

Charles Merritt sugested a biographical subject for G. (There are often running themes in an A-to-Z and this year’s seems to be “biography”.) I don’t know of a web site or other project that Merritt has that’s worth sharing, but if I learn of it, I’ll pass it along.

# J Willard Gibbs.

My love and I, like many people, tried last week to see the comet NEOWISE. It took several attempts. When finally we had binoculars and dark enough sky we still had the challenge of where to look. Finally determined searching and peripheral vision (which is more sensitive to faint objects) found the comet. But how to guide the other to a thing barely visible except with binoculars? Between the silhouettes of trees and a convenient pair of guide stars we were able to put the comet’s approximate location in words. Soon we were experts at finding it. We could turn a head, hold up the binoculars, and see a blue-ish puff of something.

To perceive a thing is not to see it. Astronomy is full of things seen but not recognized as important. There is a great need for people who can describe to us how to see a thing. And this is part of the significance of J Willard Gibbs.

American science, in the 19th century, had an inferiority complex compared to European science. Fairly, to an extent: what great thinkers did the United States have to compare to William Thompson or Joseph Fourier or James Clerk Maxwell? The United States tried to argue that its thinkers were more practical minded, with Joseph Henry as example. Without downplaying Henry’s work, though? The stories of his meeting the great minds of Europe are about how he could fix gear that Michael Faraday could not. There is a genius in this, yes. But we are more impressed by magnetic fields than by any electromagnet.

Gibbs is the era’s exception, a mathematical physicist of rare insight and creativity. In his ability to understand problems, yes. But also in organizing ways to look at problems so others can understand them better. A good comparison is to Richard Feynman, who understood a great variety of problems, and organized them for other people to understand. No one, then or now, doubted Gibbs compared well to the best European minds.

Gibbs’s life story is almost the type case for a quiet academic life. He was born into an academic/ministerial family. Attended Yale. Earned what appears to be the first PhD in engineering granted in the United States, and only the fifth non-honorary PhD in the country. Went to Europe for three years, then came back home, got a position teaching at Yale, and never left again. He was appointed Professor of Mathematical Physics, the first such in the country, at age 32 and before he had even published anything. This speaks of how well-connected his family was. Also that he was well-off enough not to need a salary. He wouldn’t take one until 1880, when Yale offered him two thousand per year against Johns Hopkins’s three.

Between taking his job and taking his salary, Gibbs took time to remake physics. This was in thermodynamics, possibly the most vibrant field of 19th century physics. The wonder and excitement we see in quantum mechanics resided in thermodynamics back then. Though with the difference that people with a lot of money were quite interested in the field’s results. These were people who owned railroads, or factories, or traction companies. Extremely practical fields.

What Gibbs offered was space, particularly, phase space. Phase space describes the state of a system as a point in … space. The evolution of a system is typically a path winding through space. Constraints, like the conservation of energy, we can usually understand as fixing the system to a surface in phase space. Phase space can be as simple as “the positions and momentums of every particle”, and that often is what we use. It doesn’t need to be, though. Gibbs put out diagrams where the coordinates were things like temperature or pressure or entropy or energy. Looking at these can let one understand a thermodynamic system. They use our geometric sense much the same way that charts of high- and low-pressure fronts let one understand the weather. James Clerk Maxwell, famous for electromagnetism, was so taken by this he created plaster models of the described surface.

This is, you might imagine, pretty serious, heady stuff. So you get why Gibbs published it in the Transactions of the Connecticut Academy: his brother-in-law was the editor. It did not give the journal lasting fame. It gave his brother-in-law a heightened typesetting bill, and Yale faculty and New Haven businessmen donated funds.

Which gets to the less-happy parts of Gibbs’s career. (I started out with ‘less pleasant’ but it’s hard to spot an actually unpleasant part of his career.) This work sank without a trace, despite Maxwell’s enthusiasm. It emerged only in the middle of the 20th century, as physicists came to understand their field as an expression of geometry.

That’s all right. Chemists understood the value of Gibbs’s thermodynamics work. He introduced the enthalpy, an important thing that nobody with less than a Master’s degree in Physics feels they understand. Changes of enthalpy describe how heat transfers. And the Gibbs Free Energy, which measures how much reversible work a system can do if the temperature and pressure stay constant. A chemical reaction where the Gibbs free energy is negative will happen spontaneously. If the system’s in equilibrium, the Gibbs free energy won’t change. (I need to say the Gibbs free energy as there’s a different quantity, the Helmholtz free energy, that’s also important but not the same thing.) And, from this, the phase rule. That describes how many independently-controllable variables you can see in mixing substances.

In the 1880s Gibbs worked on something which exploded through physics and mathematics. This was vectors. He didn’t create them from nothing. Hermann Günter Grassmann — whose fascinating and frustrating career I hadn’t known of before this — laid much of the foundation. Building on Grassman and W K Clifford, though, let Gibbs present vectors as we now use them in physics. How to define dot products and cross products. How to use them to simplify physics problems. How they’re less work than quaternions are. Gibbs was not the only person to recast physics in vector form. Oliver Heaviside is another important mathematical physicist of the time who did. But Gibbs identified the tools extremely well. You can read his Elements of Vector Analysis. It’s not very different from what a modern author would write on the subject. It’s terser than I would write, but terse is also respectful of someone’s time and ability to reason out explanations of small points.

There are more pieces. They don’t all fit in a neat linear timeline; nobody’s life really does. Gibbs’s thermodynamics work, leading into statistical mechanics, foreshadows much of quantum mechanics. He’s famous for the Gibbs Paradox, which concerns the entropy of mixing together two different kinds of gas. Why is this different from mixing together two containers of the same kind of gas? And the answer is that we have to think more carefully about what we mean by entropy, and about the differences between containers.

There is a Gibbs phenomenon, known to anyone studying Fourier series. The Fourier series is a sum of sine and cosine functions. It approximates an arbitrary original function. The series is a continuous function; you could draw it without lifting your pen. If the original function has a jump, though? A spot where you have to lift your pen? The Fourier series for that represents the jump with a region where its quite-good approximation suddenly turns bad. It wobbles around the ‘correct’ values near the jump. Using more terms in the series doesn’t make the wobbling shrink. Gibbs described it, in studying sawtooth waves. As it happens, Henry Wilbraham first noticed and described this in 1848. But Wilbraham’s work went unnoticed until after Gibbs’s rediscovery.

And then there was a bit in which Gibbs was intrigued by a comet that prolific comet-spotter Lewis Swift observed in 1880. Finding the orbit of a thing from a handful of observations is one of the great problems of astronomical mathematics. Karl Friedrich Gauss started the 19th century with his work projecting the orbit of the newly-discovered and rapidly-lost asteroid Ceres. Gibbs put his vector notation to the work of calculating orbits. His technique, I am told by people who seem to know, is less difficult and more numerically stable than was earlier used.

Swift’s comet of 1880, it turns out, was spotted in 1869 by Wilhelm Tempel. It was lost after its 1908 perihelion. Comets have a nasty habit of changing their orbits on us. But it was rediscovered in 2001 by the Lincoln Near-Earth Asteroid Research program. It’s next to reach perihelion the 26th of November, 2020. You might get to see this, another thing touched by J Willard Gibbs.

This and the other other A-to-Z topics for 2020 should be at this link. All my essays for this and past A-to-Z sequences are at this link. I’ll soon be opening f or topics for J, K, and L, essays also. Thanks for reading.

## A Bunch Of Tweets I’d Thought To Save

I’m slow about sharing them is all. It’s a simple dynamic: I want to write enough about each tweet that it’s interesting to share, and then once a little time has passed, I need to do something more impressive to be worth the wait. Eventually, nothing is ever shared. Let me try to fix that.

Just as it says: a link to Leonhard Euler’s Elements of Algebra, as rendered by Google Books. Euler you’ll remember from every field of mathematics ever. This 1770 textbook is one of the earliest that presents algebra that looks like, you know, algebra, the way we study it today. Much of that is because this book presented algebra so well that everyone wanted to imitate it.

An entry in the amusing and novel proofs. This one is John Conway’s candidate for most succinct published mathematics paper. It’s fun, at least as I understand fun to be.

This Theorem of the Day from back in November already is one about elliptic functions. Those came up several times in the Summer 2017 Mathematics A To Z. This day about the Goins-Maddox-Rusin Theorem on Heron Triangles, is dense reading even by the standards of the Theorem of the Day tweet (which fits each day’s theorem into a single slide). Still, it’s worth lounging about in the mathematics.

Elke Stangl, writing about one of those endlessly-to-me interesting subjects: phase space. This is a particular way of representing complicated physical systems. Set it up right and all sorts of physics problems become, if not easy, at least things there’s a standard set of tools for. Thermodynamics really encourages learning about such phase spaces, and about entropy, and here she writes about some of this.

So ‘e’ is an interesting number. At least, it’s a number that’s got a lot of interesting things built around it. Here, John Golden points out a neat, fun, and inefficient way to find the value of ‘e’. It’s kin to that scheme for calculating π inefficiently that I was being all curmudgeonly about a couple of Pi Days ago.

Jo Morgan comes to the rescue of everyone who tries to read old-time mathematics. There were a lot of great and surprisingly readable great minds publishing in the 19th century, but then you get partway through a paragraph and it might as well be Old High Martian with talk about diminishings and consequents and so on. So here’s some help.

As it says on the tin: a textbook on partial differential equations. If you find yourself adrift in the subject, maybe seeing how another author addresses the same subject will help, if nothing else for finding something familiar written in a different fashion.

And this is just fun: creating an ellipse as the locus of points that are never on the fold line when a circle’s folded by a particular rule.

Finally, something whose tweet origin I lost. It was from one of the surprisingly many economists I follow considering I don’t do financial mathematics. But it links to a bit of economic history: Origins of the Sicilian Mafia: The Market for Lemons. It’s 31 pages plus references. And more charts about wheat production in 19th century Sicily than I would have previously expected to see.

By the way, if you’re interested in me on Twitter, that would be @Nebusj. Thanks for stopping in, should you choose to.

## Why Stuff Can Orbit, Part 11: In Search Of Closure

Previously:

And the supplemental reading:

I’m not ready to finish the series off yet. But I am getting closer to wrapping up perturbed orbits. So I want to say something about what I’m looking for.

In some ways I’m done already. I showed how to set up a central force problem, where some mass gets pulled towards the center of the universe. It can be pulled by a force that follows any rule you like. The rule has to follow some rules. The strength of the pull changes with how far the mass is from the center. It can’t depend on what angle the mass makes with respect to some reference meridian. Once we know how much angular momentum the mass has we can find whether it can have a circular orbit. And we can work out whether that orbit is stable. If the orbit is stable, then for a small nudge, the mass wobbles around that equilibrium circle. It spends some time closer to the center of the universe and some time farther away from it.

I want something a little more, else I can’t carry on this series. I mean, we can make central force problems with more things in them. What we have now is a two-body problem. A three-body problem is more interesting. It’s pretty near impossible to give exact, generally true answers about. We can save things by only looking at very specific cases. Fortunately one is a sun, planet, and moon, where each object is much more massive than the next one. We see a lot of things like that. Four bodies is even more impossible. Things start to clear up if we look at, like, a million bodies, because our idea of what “clear” is changes. I don’t want to do that right now.

Instead I’m going to look for closed orbits. Closed orbits are what normal people would call “orbits”. We’re used to thinking of orbits as, like, satellites going around and around the Earth. We know those go in circles, or ellipses, over and over again. They don’t, but the difference between a closed orbit and what they do is small enough we don’t need to care.

Here, “orbit” means something very close to but not exactly what normal people mean by orbits. Maybe I should have said something about that before. But the difference hasn’t counted for much before.

Start off by thinking of what we need to completely describe what a particular mass is doing. You need to know the central force law that the mass obeys. You need to know, for some reference time, where it is. You also need to know, for that same reference time, what its momentum is. Once you have that, you can predict where it should go for all time to come. You can also work out where it must have been before that reference time. (This we call “retrodicting”. Or “predicting the past”. With this kind of physics problem time has an unnerving symmetry. The tools which forecast what the mass will do in the future are exactly the same as those which tell us what the mass has done in the past.)

Now imagine knowing all the sets of positions and momentums that the mass has had. Don’t look just at the reference time. Look at all the time before the reference time, and look at all the time after the reference time. Imagine highlighting all the sets of positions and momentums the mass ever took on or ever takes on. We highlight them against the universe of all the positions and momentums that the mass could have had if this were a different problem.

What we get is this ribbon-y thread that passes through the universe of every possible setup. This universe of every possible setup we call a “phase space”. It’s easy to explain the “space” part of that name. The phase space obeys the rules we’d expect from a vector space. It also acts in a lot of ways like the regular old space that we live in. The “phase” part I’m less sure how to justify. I suspect we get it because this way of looking at physics problems comes from statistical mechanics. And in that field we’re looking, often, at the different ways a system can behave. This mathematics looks a lot like that of different phases of matter. The changes between solids and liquids and gases are some of what we developed this kind of mathematics to understand, in fact. But this is speculation on my part. I’m not sure why “phase” has attached to this name. I can think of other, harder-to-popularize reasons why the name would make sense too. Maybe it’s the convergence of several reasons. I’d love to hear if someone has a good etymology. If one exists; remember that we still haven’t got the story straight about why ‘m’ stands for the slope of a line.

Anyway, this ribbon of all the arrangements of position and momentum that the mass does ever at any point have we call a “trajectory”. We call it a trajectory because it looks like a trajectory. Sometimes mathematics terms aren’t so complicated. We also call it an “orbit” since very often the problems we like involve trajectories that loop around some interesting area. It looks like a planet orbiting a sun.

A “closed orbit” is an orbit that gets back to where it started. This means you can take some reference time, and wait. Eventually the mass comes back to the same position and the same momentum that you saw at that reference time. This might seem unavoidable. Wouldn’t it have to get back there? And it turns out, no, it doesn’t. A trajectory might wander all over phase space. This doesn’t take much imagination. But even if it doesn’t, if it stays within a bounded region, it could still wander forever without repeating itself. If you’re not sure about that, please consider an old sequence I wrote inspired by the Aardman Animation film Arthur Christmas. Also please consider seeing the Aardman Animation film Arthur Christmas. It is one of the best things this decade has offered us. The short version is, though, that there is a lot of room even in the smallest bit of space. A trajectory is, in a way, a one-dimensional thing that might get all coiled up. But phase space has got plenty of room for that.

And sometimes we will get a closed orbit. The mass can wander around the center of the universe and come back to wherever we first noticed it with the same momentum it first had. A that point it’s locked into doing that same thing again, forever. If it could ever break out of the closed orbit it would have had to the first time around, after all.

Closed orbits, I admit, don’t exist in the real world. Well, the real world is complicated. It has more than a single mass and a single force at work. Energy and momentum are conserved. But we effectively lose both to friction. We call the shortage “entropy”. Never mind. No person has ever seen a circle, and no person ever will. They are still useful things to study. So it is with closed orbits.

An equilibrium orbit, the circular orbit of a mass that’s at exactly the right radius for its angular momentum, is closed. A perturbed orbit, wobbling around the equilibrium, might be closed. It might not. I mean next time to discuss what has to be true to close an orbit.

## A Leap Day 2016 Mathematics A To Z: Surjective Map

Gaurish today gives me one more request for the Leap Day Mathematics A To Z. And it lets me step away from abstract algebra again, into the world of analysis and what makes functions work. It also hovers around some of my past talk about functions.

## Surjective Map.

This request echoes one of the first terms from my Summer 2015 Mathematics A To Z. Then I’d spent some time on a bijection, or a bijective map. A surjective map is a less complicated concept. But if you understood bijective maps, you picked up surjective maps along the way.

By “map”, in this context, mathematicians don’t mean those diagrams that tell you where things are and how you might get there. Of course we don’t. By a “map” we mean that we have some rule that matches things in one set to things in another. If this sounds to you like what I’ve claimed a function is then you have a good ear. A mapping and a function are pretty much different names for one another. If there’s a difference in connotation I suppose it’s that a “mapping” makes a weaker suggestion that we’re necessarily talking about numbers.

(In some areas of mathematics, a mapping means a function with some extra properties, often some kind of continuity. Don’t worry about that. Someone will tell you when you’re doing mathematics deep enough to need this care. Mind, that person will tell you by way of a snarky follow-up comment picking on some minor point. It’s nothing personal. They just want you to appreciate that they’re very smart.)

So a function, or a mapping, has three parts. One is a set called the domain. One is a set called the range. And then there’s a rule matching things in the domain to things in the range. With functions we’re so used to the domain and range being the real numbers that we often forget to mention those parts. We go on thinking “the function” is just “the rule”. But the function is all three of these pieces.

A function has to match everything in the domain to something in the range. That’s by definition. There’s no unused scraps in the domain. If it looks like there is, that’s because were being sloppy in defining the domain. Or let’s be charitable. We assumed the reader understands the domain is only the set of things that make sense. And things make sense by being matched to something in the range.

Ah, but now, the range. The range could have unused bits in it. There’s nothing that inherently limits the range to “things matched by the rule to some thing in the domain”.

By now, then, you’ve probably spotted there have to be two kinds of functions. There’s one in which the whole range is used, and there’s ones in which it’s not. Good eye. This is exactly so.

If a function only uses part of the range, if it leaves out anything, even if it’s just a single value out of infinitely many, then the function is called an “into” mapping. If you like, it takes the domain and stuffs it into the range without filling the range.

Ah, but if a function uses every scrap of the range, with nothing left out, then we have an “onto” mapping. The whole of the domain gets sent onto the whole of the range. And this is also known as a “surjective” mapping. We get the term “surjective” from Nicolas Bourbaki. Bourbaki is/was the renowned 20th century mathematics art-collective group which did so much to place rigor and intuition-free bases into mathematics.

The term pairs up with the “injective” mapping. In this, the elements in the range match up with one and only one thing in the domain. So if you know the function’s rule, then if you know a thing in the range, you also know the one and only thing in the domain matched to that. If you don’t feel very French, you might call this sort of function one-to-one. That might be a better name for saying why this kind of function is interesting.

Not every function is injective. But then not every function is surjective either. But if a function is both injective and surjective — if it’s both one-to-one and onto — then we have a bijection. It’s a mapping that can represent the way a system changes and that we know how to undo. That’s pretty comforting stuff.

If we use a mapping to describe how a process changes a system, then knowing it’s a surjective map tells us something about the process. It tells us the process makes the system settle into a subset of all the possible states. That doesn’t mean the thing is stable — that little jolts get worn down. And it doesn’t mean that the thing is settling to a fixed state. But it is a piece of information suggesting that’s possible. This may not seem like a strong conclusion. But considering how little we know about the function it’s impressive to be able to say that much.

## Into.

The definition of “into” will call back to my A to Z piece on “bijections”. It particularly call on what mathematicians mean by a function. When a mathematician talks about a functions she means a combination three components. The first is a set called the domain. The second is a set called the range. The last is a rule that matches up things in the domain to things in the range.

We said the function was “onto” if absolutely everything which was in the range got used. That is, if everything in the range has at least one thing in the domain that the rule matches to it. The function that has domain of -3 to 3, and range of -27 to 27, and the rule that matches a number x in the domain to the number x3 in the range is “onto”.

## Bijection.

To explain this second term in my mathematical A to Z challenge I have to describe yet another term. That’s function. A non-mathematician’s idea a function is something like “a line with a bunch of x’s in it, and maybe also a cosine or something”. That’s fair enough, although it’s a bit like defining chemistry as “mixing together colored, bubbling liquids until something explodes”.

By a function a mathematician means a rule describing how to pair up things found in one set, called the domain, with the things found in another set, called the range. The domain and the range can be collections of anything. They can be counting numbers, real numbers, letters, shoes, even collections of numbers or sets of shoes. They can be the same kinds of thing. They can be different kinds of thing.