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Variational Programming

We have coined the term Variational Programming to describe our prefered approach to programming: to follow a path of least action. The name is borrow from physics, where Hamilton's variational principle states that a physical trajectory of a particle is given by a path along which the action is minimal.1Our goal is to be lazy in the optimal sense: to minimize the amount of time and energy to along the path of writing a piece of software, from start to finish, i.e. from the first idea to the completion of a robust and well-tested product, that can be easily and flexibly used in connection with other pieces of software.

As is the case in physics, the least action principle is a global one, not a local one. The challenge is to minimize the total time required to put together a whole software package. If we want to write the code for an individual model, it is by far the easiest to throw something together quickly and make it work. However, after writing a few dozen modules that way, it quickly becomes clear that making them together smoothly requires more and more work, forcing a repeated rewrite of many of the pieces that originally were easy to dash of.

Clearly, there is an optimum approach. If you spend too much time polishing each module to make it really elegant and beautiful, you will spend (almost) forever before you finish your software package. If you rush each module writing too much, and don't think carefully about how they should fit together, you spend (almost) forever chasing bugs all over the place. The question is: where, in between these two extremes, is the optimum approach, which minimizes the total amount of work?

Finding the optimum is an art, not a science. Hence the title of our series The Art of Computational Science. But an art requires particular skills and can be learned, ideally by exposure to many hands-on examples, the more real-life-like the better. And while being exposed to such examples, it does become clear that there are some general rules.

In our experience, which between the two of us includes half a century of frequent scientific simulation code writing, the main rule is: try to make frequent variations. Hence our term Variational Programming, which we will now summarize.

The first improvement over rushing into software writing is to test every step carefully, it to make sure it is correct. While this is much better than a rush job, it still is no guarantee at all that the module one is working on will function optimally in a larger setting, connected to other modules.

The next improvement is to look/feel/grope around a solution, looking at neighboring and slightly different approaches. The goal of variational programming is: pragmatic simplicity, avoiding the rigidity of an approach that is too narrow, as well as the complexity of an approach that is too baroque and general-purpose.

In summary: before writing anything, try to get a sense of the landscape of the problem. Then come up with a tentative solution, a toy model. This most likely will be wrong, the first time. But if you don't try something, you'll never get there. However, if you try something while building it in grandious ways, you will waste a lot of time if it turns out to be ill-directed. So be pragmatic: try something complex enough that it can actually do something interesting, given the landscape of the problem, but not much more than that. And above all, don't be attached to your first (few) attempt(s): be prepared to clean the decks and throw stuff away.

After one or more tries, you will get a hands-on feel for the landscape, and you will get an idea how much work will be required to go from A to B along different paths. Only then can you choose the path that (most likely) will require the least action. And while exploring that path, it remains important to keep making local variations, to continue trying to find a more optimal solution.

This holds on all levels, down to the smallest module. When you write something, first of all write in small chunks. For each chunk, while writing and while testing, think about whether it makes sense to generalize it a bit more or streamline it a bit more. Play around with it, both while writing and testing, rather than just following your first idea like an arrow let loose. Arrows are bad at following corners in the road.

So the principle of least action in programming means: Be comfortable and lazy, but only so in the long run. If you write quickly, you'll have to do a lot of nasty debugging later. For most people, this is less comfortable that writing new clean code. But even for those who like to both create and solve problems: you'll spend more time and therefore you'll have to work harder to get at your goal. If you follow the path of least action, you'll introduce bugs, for sure, but they will be interesting complex bugs, since you've avoid the annoying ones (ever spent half an hour searching for a bug, only to find that it was a matter of misspelling something somewhere in an overly long function spilling out over a few pages?).

Another way to characterize variational programming concerns the way to learn from errors. The key is to let the problem tell you how it wants to be solved. Of course, the problem won't talk to you, so you have to start with an initial attempt. But rather than trying to solve a task in the first attempt like blasting a tunnel through all the problems, it often makes more sense to try to see whether the type of problem that comes up by itself can suggest how you can change your approach a bit. That way, you often find opportunities to wind your way between the hills, rather than blasting tunnels in a straight line, as the crow flies or the mole bores, as the case may be.

Finally, we call our approach variational because making variations is in fact the only way to find the path of least action. If there where only a few paths, you could try all or most of them. The problem with paths is, there are not only infinitely many of them, but the family of paths itself even has infinitely many degrees of freedom, or more strictly speaking, about as many degrees of freedom as there are key strokes in your code. So the number of possibilities is, roughly speaking, infinity to the power infinity. The only way to find a reasonably optimal choice in such a vast sea of possibilities is to: 1) try a few global choices of different paths to see which one looks promising; 2) after settling on one, make very many local variations to explore in much greater detail the space of paths around the one you're beating. Each local variation gives you a choice, and with sufficient experience your final result will be the product of all the choice you have made along the way, selecting your approach from among a very large number of ways to write your code: a factor of a few to the power of the number of choices you have made!


next up previous contents
Next: An Open Source Project Up: Preface Previous: Extreme Research
The Art of Computational Science
2004/01/25