It seems a good idea to use a static type system to enforce unit correctness – to make sure that we don't add meters to inches or some such. After all, spacecraft has been lost due to such bugs costing hundreds of millions.

Almost any static type system lets us check units in sufficiently simple cases. I'm going to talk about one specific case which seems to me very hard and I'm not sure there's a type system that handles it.

Suppose we have a bunch of points – real-world measurements in meters – that should all lie on a straight line, modulo measurement noise. Our goal is to find that line – A and B in the equation:

A*x + B = y

If we had exactly 2 points, A and B would be unique and we could obtain them by solving the equation system:

A*x1 + B = y1 A*x2 + B = y2

2 equations, 2 unknowns – we're all set. With more than 2 measurements however, we have an overdetermined equation system – that is, the 3rd equation given by the measurement x3, y3 may "disagree" with the first two. So our A and B must be a compromise between all the equations.

The "disagreement" of x,y with A and B is |A*x + B – y|, since if A and B fit a given x,y pair perfectly, A*x+B is exactly y. Minimizing the sum of these distances from each y sounds like what we want.

Unfortunately, we can't get what we want because minimizing functions with absolute values in them cannot be done analytically. The analytical minimum is where the derivative is 0 but you can't take the derivative of abs.

So instead we'll minimize the sum of (A*x + B – y)^2 – not exactly what we want, but at least errors are non-negative (good), they grow when the distance grows (good), and this is what everybody else is doing in these cases (excellent). If the cheating makes us uneasy, we can try RANSAC later, or maybe sticking our head in the sand or something.

What's the minimum of our error function? Math says, let's first write our equations in matrix form:

(x1 1)*(A B) = y1 (x2 1)*(A B) = y2 ... (xN 1)*(A B) = yN

Let's call the matrix of all (xi 1) "X" and let's call the vector of all yi "Y". Then solving the equation system X'*X*(A B) = X'*Y gives us A, B minimizing our error function. Matlab's "" operator – as in XY – even does this automatically for overdetermined systems; that's how you "solve" them.

And now to static type systems. We start out with the following types:

- x,y are measured in meters (
**m**). - A is a number (
**n**)- a scale factor – while B is in meters, so that A*x + B is in meters as y should be.

What about X'*X and X'*Y? X'*X looks like this:

sum(xi*xi) sum(xi) //m^2 msum(xi) sum(1) //m n

….while X'*Y looks like this:

sum(xi*yi) sum(yi) //m^2 m

The good news is that when we solve for A and B using Cramer's rule which involves computing determinants, things work out wonderfully – A indeed comes out in meters and B comes out as a plain number. So if we code this manually – compute X'*X and X'*Y by summing the terms for all our x,y points and then hand-code the determinants computation – that could rather easily be made to work in many static type systems.

The bad news is that thinking about the type of Matlab's operator or some other *generic *least-squares fitting function makes my head bleed.

Imagine that large equation systems will not be solved by Cramer's rule but by iterative methods and all the intermediate results will need to have a type.

Imagine that we may be looking, not for a line, but a parabola. Then our X'*X matrix would have sum(x^4), sum(x^3) etc. in it – and while spelling out Cramer's rule works nicely again, a generic function's input and output types would have to account for this possibility.

Perhaps the sanest approach is we type every column of X and Y separately, and every element of X'*X and X'*Y separately – not meters^something but T1, T2, T3… And then we see what comes out, and whether intermediate results have sensible types. But I just can't imagine the monstrous types of products of the various elements ever eventually cancelling out as nicely as they apparently should – not in a real flesh-and-blood programming language.

Maybe I'm missing something here? I will admit that I sort of gave up on elegance in programming in general and static typing in particular rather long ago. But we have many static type systems claiming to be very powerful and concise these days. Maybe if I did some catching-up I would regain the optimism of years long gone.

Perhaps in some of these systems, there's an elegant solution to the problem of 's type – more elegant than just casting everything to "number" when it goes into and casting the result back into whatever types we declared for the result, forgoing the automatic type checking.

So – can your static type system do linear algebra while checking that measurement units are used consistently?