# Functional Differential Geometry

February 17, 2014

by Gerald Jay Sussman & Jack Wisdom (with Will Farr)

This book follows very much in the mould of Sussman & Wisdom’s Structure and Interpretation of Classical Mechanics in that it’s an attempt to take a body of mathematics (differential geometry here, classical mechanics in SICM) and to “computationalise” it, i.e. to use computer programs to make the mathematical structures involved manifest in a way that a more traditional approach to these subjects doesn’t.

This computational viewpoint is a very powerful one to adopt, for a number of reasons.

First, by endeavouring to compute things, we’re forced to be extremely rigorous about the meaning of our notation. When things become notationally inconvenient, it’s common to say “by abuse of notation…” but that doesn’t work if you have to translate your notation to computer code.

Second, by endeavouring to compute things, well, we can compute things! If you’ve ever taken a differential geometry course, you’ll probably remember just how tedious some of the calculations can be. Once you start looking at curvature and geodesics, you have geometrical objects with four indices floating around and everything becomes rather annoying. If you do it right (which mostly means functional programming), taking a computational approach allows you to exploit the compositional property of (functional) programs and to build up from simple examples to complex cases without ever losing track of what’s going on1.

## Notation

The example that Sussman & Wisdom use on the very first page of their book to motivate notational rigour is the Euler-Lagrange equations, traditionally written

$\frac{d}{dt} \frac{\partial L}{\partial \dot{q}} - \frac{\partial L}{\partial q} = 0.$

Here, $L(t, q, \dot{q})$ is the Lagrangian, written as a function of time $t$, generalised coordinates $q$ and generalised velocities $\dot{q}$. In the first term on the left hand side, the partial derivative is with respect to $\dot{q}$, treating the $\dot{q}$ as independent variables. But then, what does the outer time derivative mean? Suppose that we have a solution of the Euler-Lagrange equations, represented by a path $q = w(t)$, a function of time. Then $\dot{q} = dw/dt$, and what we really mean by the Euler-Lagrange equations is something like

$\frac{d}{dt} \left( \left. \frac{\partial L(t, q, \dot{q})}{\partial \dot{q}} \right|_{q=w(t), \dot{q}=\frac{dw(t)}{dt}} \right) - \left. \frac{\partial L(t, q, \dot{q})}{\partial q} \right|_{q=w(t), \dot{q}=\frac{dw(t)}{dt}} = 0.$

This is what we really mean by the traditional expression. It’s a bit of a mouthful, but it’s completely explicit and there are no notational gaps that we need to fill in with our imagination.

And this is the level of notational rigour that you need if you’re going to compute with these things. Achieving this level of rigour takes some effort, and much of the book is devoted to ensuring that this is done correctly.

This is one place where the book shines, giving a very good impression of what it feels like to think this way, and follow the steps that are needed to turn a slightly unclear expression of a mathematical idea into something completely explicit with which you can compute. Although the focus here isn’t really on numerical analysis, there’s much the same feeling you have when you’re developing code for a PDE solver, for instance – you start with a more-or-less clear expression of what you want, but then you need to pin down every single little detail to get to something you can write code for.

Sussman & Wisdom use a notation for calculus that attempts to be clearer and “more functional” than the traditional notation. It has some things in common with the sort of coordinate-free notation for differential geometry used, for example, in Misner, Thorne & Wheeler’s Gravitation (a big fat book about general relativity) and although it takes a little getting used to, it works well and makes the gap between mathematical expressions and the code to represent them a little narrower.

## Coverage, length & explicitness

The coverage of topics is more or less what you would expect from the title: most of what would appear in a first undergraduate course in differential geometry, plus some applications – manifolds, vector fields, a bit of exterior calculus, directional derivatives, curvature and geodesics. The presentation of the material (aside from the computational focus) is relatively standard, although there is a nice chapter called “Over a Map” about defining vector fields on manifolds that takes an approach I’ve not seen before.

My one disappointment with the book is its length. I was hoping for a weighty tome like Structure and Interpretation of Computer Programs, another Sussman production, but instead Functional Differential Geometry is only a shade over 200 pages. Frankly, this isn’t enough space to do the subject justice, particularly given the unusual approach that the book takes.

The result of this abbreviated presentation is that there are lots of gaps in derivations, and lots of aspects of the code that’s presented that aren’t described. To really understand what’s going on, you need to look at the scmutils Scheme library on which the code in the book is based. This is the same library as is used in SICM, but I’m not sure that things are really explained in detail there either. The scmutils code is pretty good, but there’s quite a lot of it (SLOCCOUNT says something around 65,000 lines). You can certainly take examples from the book and trace what’s going on through the scmutils functions that they use, but it’s not a small or easy task. The main problem is that scmutils is rather clever and rather powerful, and the generality that it offers means that the code is quite difficult to understand in places. It would certainly be much better to have more explicit descriptions of what the code examples in the book are doing and how they’re doing it.

## Scheme

All the code is in Scheme, as in SICP and SICM. This isn’t bad by any means (I used to be a fairly committed Schemer), but I realised while browsing through the scmutils library that I’ve become a little spoiled with Haskell and its static types. Whatever arguments you want to make about static versus dynamic typing, the plain fact of the matter is that the type of a function in Haskell provides you with some information about what it does. A type signature is a constraint on the behaviour of a function that helps to narrow down the conceptual space you have to explore to understand what a function does. A very simple and obvious example is that, if the return value of a Haskell function doesn’t live in the IO monad, there’s no way that the function can have any side-effects2.

And the fact is that mathematical objects do have types. A connection is a different type of thing from a vector field, which is a different type of thing from a trajectory on a manifold, and so on. It seems that making those types manifest in the code would be of benefit. Indeed, the scmutils code internally uses a sort of tagged structure approach to distinguish these different kinds of objects, but Scheme doesn’t provide language-level support for saying “this is a vector field” in the way that might be possible in a statically typed language.

There are efforts to develop type systems that are more suitable for expressing mathematics (for example, this recent paper about conservation laws and type parametricity; the type system of the Axiom computer algebra system is also worth mentioning), but it seems as though it ought to be possible to have some half-way house between dynamic typing as in Scheme and “all the types for all the things EVAR” which seems to be the goal of some type theorists. Whether it’s possible to arrive at that half-way house with existing tools isn’t clear, but it might be interesting to reimplement scmutils in Haskell or ML or some other statically typed language to see how good the fit is.

## Conclusion

Overall, this is definitely worth a read if you’re interested in any or all of: differential geometry, functional programming, computer algebra, “fancy” numerical methods. There’s much in here that’s very thought-provoking, even if the treatment is frustratingly brief.

1. The compositional nature of functional programming often seems somewhat magical to me – you can have a sort of true encapsulation of ideas with well-insulated boundaries that allow you to take a concept you’ve expressed programmatically and reuse it and compose it in an entirely natural way.

2. Yes, yes, I know about unsafePerformIO. Give me a break, all right?