Trapping a Transcendental: There are reals that are not roots of polynomials
penzba.co.uk
penzba.co.uk
0. The basic idea is to show that an algebraic number -- i.e., one that's a root of a polynomial with integer coefficients -- can't be very well approximated by rational numbers ... and then to construct a number that can be very well approximated by rational numbers.
1. Suppose you've got a polynomial p, of degree d, with integer coefficients. Let x be one of its roots, and let a/b be any rational number. How close can it be to x? Well, let's ask a slightly different question: how close can p(a/b) be to 0? Answer: it's always (some integer)/b^d, and unless x actually equals a/b that integer isn't zero, so is >=1 or <=-1; so abs(poly(rational)) >= 1/denominator^degree.
2. OK, so how close can a/b be to x? Well, roughly speaking the ratio between p(a/b)-0 (which we know isn't too small) and a/b-x (which is what we care about) is close to p'(x), provided a/b is close to x. So (after filling in the details) we find that: if x is a root of an integer polynomial of degree d then there's a constant C such that every rational number has abs(rational-x) >= C denominator^d.
3. Now let's construct a number that is better-approximated by rationals than that allows. Specifically, take a decimal that looks like 0.110001000... where the spacing between the 1s grows very fast. The traditional thing is to take 10^-1! + 10^-2! + 10^-3! + ... so let's do that. Then truncating after 10^-k! leaves an error that's approximately 10^-(k+1)!; in other words we have a rational number with denominator N=10^k! and an error on the order of N^(k+1), so for any integer polynomial of degree d, once k gets somewhat larger than d this approximation is "too good", so this number can't be a root of that polynomial. So it can't be the root of any integer polynomial; in other words, it's transcendental.
The article submitted here easily shows that in any interval on the real line there is at least one transcendental. Your proof here shows that you can create a transcendental "close" to any given rational. Both have a power beyond just "transcendentals exist".
The reason I like the one submitted is that it's more visual and less algebraic, hence easier to explain to those less comfortable with polynomials and the like, but certainly both have their merits.
Thanks for showing us this one.
The submitted item says: take an interval, here's how to construct a transcendental inside your chosen interval.
The proof that gjm11 gives says: Here's a proof that if something is unreasonably close to a rational then it's not algebraic, and here's how to construct something like that.
The concept of "unreasonably close" then has to be expressed in terms of sizes of denominators, and hence is a relative term. That forms a key part of the proof.
Perhaps a hard ask, but do you know of any similarly explicable proofs for transcendental-ness of "well known" numbers?
His mention of the (beautiful but difficult) Gelfond-Schneider theorem reminds me of the following much easier, much less deep but rather pretty observation.
Question: Is it possible to take an irrational number, raise it to an irrational power, and get a rational number?
Answer: Yes. Write s=sqrt(2) -- which is of course irrational -- and consider s^s. If it's rational then we're done. Otherwise, (s^s)^s is an irrational number to an irrational power; but it equals s^(s^2) = s^2 = 2, which is rational!
Thanks to Gelfond and Schneider, we know that actually s^s is not only irrational but transcendental.
(In 1919, the great mathematician David Hilbert mentioned three big unsolved problems in number theory: the Riemann Hypothesis, Fermat's "last theorem", and the transcendence of 2^sqrt(2). He said RH would probably be proved within a few years, FLT maybe in his lifetime, and the transcendence of 2^sqrt(2) probably not in the lifetime of anyone in his audience. In fact he got the order exactly wrong: the transcendence of 2^sqrt(2) was proved in 1930, FLT in 1995, and RH still isn't done despite its great importance.)
http://en.wikipedia.org/wiki/Transcendental_number#Numbers_p...
The main tools are the Gelfond-Schneider theorem and the Lindeman-Weierstrass theorem:
http://en.wikipedia.org/wiki/Gelfond%E2%80%93Schneider_theor...
http://en.wikipedia.org/wiki/Lindemann%E2%80%93Weierstrass_t...
gjm11 will know much more about these than I, and is better placed to offer insight into them.
Thank you. I'm casting about for more things to write about like this. There are lots of things hackers will have read, but not seen the details for, and sometimes the details are very accessible.
If you liked this one, what else would you like? For example:
* Between every two rationals there's an irrational
* Between every two rationals there's a rational
* The rationals can be listed, in full, exactly once each
That kind of stuff. What assertions or claims have you seen made that you'd like explained in more detail?
Feel free to reply here, or email - address in my profile.
Edited for layout.
First, a reminder of what we're going to kinda-sorta-prove: Take any number and compute its continued fraction, giving approximations p1/q1, p2/q2, etc. Then for "almost all" numbers it happens that qn^(1/n) tends to the magical number exp(pi^2 / (12 log 2)).
And a brief summary of the idea: the process of computing continued fractions is basically one of applying a certain function again and again, and the number qn^(1/n) can be related to a certain average involving the numbers you get; a very general and powerful thing called the "ergodic theorem" tells us that when you do this, the values you get look a lot like random numbers chosen with a particular distribution; the corresponding average (over numbers chosen with that probability distribution) is a fairly simple integral, equal to an infinite series whose value is the thing we're looking for.
CONTINUED FRACTIONS
OK. So, how do you find the continued fraction for a number x? You repeatedly subtract off the integer part and then take the reciprocal; the integer parts you subtract off are the c.f. coefficients.
Forget about the integer parts for a moment and just consider the following function: x -> fractional_part(1/x). Call this F. We'll apply it only to numbers between 0 and 1. If you start with any x, compute fractional_part(x) and then repeatedly apply F, the c.f. coefficients are exactly the bits you throw away when computing the fractional parts.
Now suppose you pick a random x and compute F(x), F(F(x)), etc. These points will be scattered over the interval (0,1) somehow. In this sort of setting it often turns out that for almost all x, the density with which these points turn up is independent of x. Let me be more specific.
ERGODIC THEORY
Suppose you've got a probability distribution on (0,1) with the following magical property: picking a number according to this probability distribution and applying F to it gives you a number with the same probability distribution. For some F you can do this, and for others you can't. If you happen to have done anything with Markov chains and run across the notion of an "invariant measure" or "stationary distribution", this is the same idea but in a continuous rather than a discrete setting. "Measure", "distribution", and "probability distribution" all mean more or less the same thing here.
Well, anyway, if you have your function F and an invariant measure, and if another technical condition called "ergodicity" applies (it says that F mixes things up enough that you can't find nontrivial subsets that F maps to themselves), then something wonderful follows: for almost all x, the sequence x, F(x), F(F(x)), ... "samples the space fairly" according to your probability distribution. There are several different "ergodic theorems" that make this rigorous with different meanings of "sample fairly". Here's one: let f be any well-behaved function; then for almost all x, the averages [f(x)+f(F(x))+...+f(F^(n-1)(x))]/n tend to the same value, which is the average value of f(x) when you pick x from that magic probability distribution.
THE INVARIANT MEASURE
If you're still awake after all that, it will not surprise you that the particular F we're talking about -- mapping x to frac(1/x) -- does indeed have an invariant measure. Its probability density function is nice and simple: (1/log2) 1/(1+x). Proving that this is indeed invariant under F is pretty straightforward.
So all we need to do is to set things up so that the thing we're interested in is of the form [f(x)+f(F(x))+...+f(F^(n-1)(x))]/n, and then compute the average of f(x) with respect to our invariant measure. How do we do that?
CONTINUED FRACTIONS AGAIN
(There's a bit of magic here, but it's fairly easy magic.) Suppose the n'th convergent to x is pk(x)/qk(x). Then it's not hard to show that pk(x) = q{k-1}(Fx). So the following identity is trivial because it's just multiplying by a lot of 1s:
1/qn(x) = 1/qn(x) pn(x)/q{n-1}(Fx) p{n-1}(Fx)/q{n-2}(FFx) ... p2(F^{n-2}x)/q1(F^{n-1}x).
Now "shift the numerators left by one space":
1/qn(x) = pn(x)/qn(x) p{n-1}(Fx)/q{n-1}(Fx) ... p1(F^{n-1}x)/q1(F^{n-1}x).
Now remember that pk(y)/qk(y) is a pretty good approximation to y (at this point in an actual proof there'd need to be some computation of what the errors are, but I'll spare you), so
1/qn(x) ~= x F(x) F(F(x)) ... F^n(x)
which is beginning to look a lot like what we need. Take n'th roots and logs:
log qn(x)^(1/n) ~= -[log x + log F(x) + ... + log F^n(x)]/n
NOW IT'S JUST INTEGRATION
Aha. So now (thanks to the ergodic theorem) we just need to know the average of log x when we pick x according to our probability distribution. That's
integral {0..1} of (1/log2) . log x . 1/(1+x)
which, given the form of the answer we're looking out, is crying out to be done by expanding log x as a series. More specifically, first an integration by parts shows that -(that integral) equals
integral {0..1} of log(1+x)/x
which equals
integral {0..1} of (x-x^2/2+x^3/3-x^4/4+...)/x
which, integrating term by term, equals 1 - 1/2^2 + 1/3^2 - 1/4^2 + ..., and the fact that this equals pi^2/12 is basically the same as the slightly more famous fact that 1 + 1/2^2 + 1/3^2 + ... equals pi^2/6. And now we're done.
http://en.wikipedia.org/wiki/Cantor%E2%80%93Bernstein%E2%80%...
...where s3 is root(3), and gg is Euler's gamma/Masceroni's 0.5772... constant:
e^(s3 times gg)/(e times s3 times gg) = 1.00000002718?
Flirting? Fear of patent trolls, proprietary pride, just dumb luck, what?
Note: HN edit coughed when I first used '*' for 'times'
If you compute some more places, you get 1.0000000271816967980437100574683393 so the digits of e don't continue for very long.
The thing that's more surprising on the face of it is that the result is so close to 1. Let's rearrange it a little:
exp(s3 gg) ~= exp(1) s3 gg
which I prefer to write as
exp(s3 gg - 1) ~= 1 + (s3 gg - 1)
because the series for exp looks like exp(x) = 1 + x + ... -- so what gives you the value close to 1 is that s3 gg - 1 is rather close to 0, so that the first couple of terms of the series are close to the actual value of exp(); in other words, that gg is close to 1/s3. So far as I know, this too is just a coincidence. (There are lots of simple-ish constants between, say, 0.1 and 10; it's not really that surprising to find a pair that are approximately reciprocals.)
The e-like digits that follow amount (after a little simple algebra) to the fact that (s3 gg - 1)^2/2 ~= e/10^8 or, if you prefer, 1 - s3 gg ~= sqrt(2e)/10^4. Which smells very, very coincidental to me. It's a basically random smallish number; why shouldn't it be close to sqrt(2e) / a power of 10?
You may enjoy http://en.wikipedia.org/wiki/Mathematical_coincidence .
No sir, you are mistaken. My linux netbook's perl regex's determined this is interesting. I myself, a human, merely scan a move-to-front list that suit's this week's curious fancy.
Also, I think you you put spaces around the * sign you're OK.
(Added in edit: Feels like a coincidence, but strange things do happen, as evidenced in my calculations for the birthday problem, where sqrt(ln(4)) surfaced.)