The Universe of Discourse
           
Thu, 19 Jul 2007

More about fixed points and attractors
A while back I talked about a technique for calculating √2 where you pick a function that has √2 as a fixed point (that is, f(√2) = √2) and then see what happens when you consider the sequence x, f(x), f(f(x)), ..., for various initial values of x. For some such functions the sequence diverges, but often it converges to √2.

I picked a few example functions, some of which worked and some of which didn't.

One glaring omission from the article was that I forgot to mention the so-called "Babylonian method" for calculating square roots. The Babylonian method for calculating √n is simply to iterate the function x → ½(x + n/x). (This is a special case of the Newton-Raphson method for finding the zeroes of a function. In this case the function whose zeroes are being found is is xx2 - n.) The Babylonian method converges quickly for almost all initial values of x. As I was writing the article, at 3 AM, I had the nagging feeling that I was leaving out an important example function, and then later on realized what it was. Oops.

But there's a happy outcome, which is that the Babylonian method points the way to a nice general extension of this general technique. Suppose you've found a function f that has your target value, say √2, as a fixed point, but you find that iterating f doesn't work for some reason. For example, one of the functions I considered in the article was x → 2/x. No matter what initial value you start with (other than √2 and -√2) iterating the function gets you nowhere; the values just hop back and forth between x and 2/x forever.

But as I said in the original article, functions that have √2 as a fixed point are easy to find. Suppose we have such a function, f, which is badly-behaved because the fixed point repels, or because of the hopping-back-and-forth problem. Then we can perturb the function by trying instead x → ½(x + f(x)), which has the same fixed points, but which might be better-behaved. (More generally, x → (ax + bf(x)) / (a + b) has the same fixed points as f for any nonzero a and b, but in this article we'll leave a = b = 1.) Applying this transformation to the function x → 2/x gives us the the Babylonian method.

I tried applying this transform to the other example I used in the original article, which was xx2 + x - 2. This has √2 as a fixed point, but the √2 is a repelling fixed point. √2 ± &epsilon → √2 ± (1 + 2√2)ε, so the error gets bigger instead of smaller. I hoped that perturbing this function might improve its behavior, and at first it seemed that it didn't. The transformed version is x → ½(x + x2 + x - 2) = x2/2 + x - 1. That comes to pretty much the same thing. It takes √2 ± &epsilon → √2 + (1 + √2)ε, which has the same problem. So that didn't work; oh well.

But actually things had improved a bit. The original function also has -√2 as a fixed point, and again it's one that repels from both sides, because -√2 ± ε → -√2 ± (1 - 2√2)ε, and |1 - 2√2| > 1. But the transformed function, unlike the original, has -√2 as an attractor, since it takes -√2 ± ε → -√2 ± (1 - √2)ε and |1 - √2| < 1.

So the perturbed function works for calculating √2, in a slightly backwards way; you pick a value close to -√2 and iterate the function, and the iterated values get increasingly close to -√2. Or you can get rid of the minus signs entirely by transforming the function again, and considering -f(-x) instead of f(x). This turns x2/2 + x - 1 into -x2/2 + x + 1. The fixed points change places, so now √2 is the attractor, and -√2 is the repeller, since √2 ± ε → √2 ± (1 - √2)ε. Starting with x = 1, we get:

1.5
1.375
1.4296875
1.40768433
1.41689675
1.41309855
1.41467479
1.41402241
1.41429272
1.41418077
1.41422714
1.41420794
1.41421589
1.41421260
1.41421396
1.41421340
1.41421363
So that worked out pretty well. One might even make the argument that the method is simpler than the Babylonian method, since the division is a simple x/2 instead of a complex 2/x. I have not yet looked into the convergence properties; I expect it will turn out that the iterated polynomial converges more slowly than the Babylonian method.

I had meant to write about Möbius transformations, but that will have to wait until next week, I think.


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