Having some more free time than usual, I figured I would read a recent paper that looked interesting. However, I’m something of a noob at math, so I figure it’s worth it to take it slowly and painstakingly think through the details. This will be a sort of stream-of-conciousness post where I do just that.

The paper I’ll be reading will be Terry Tao’s “Sendov’s conjecture for sufficiently high degree polynomials.” This paper looks interesting to me because it applies “cheap nonstandard” methods to prove a result in complex analysis. In addition it uses probability-theoretic methods, which I’m learning a lot of right now. Sendov’s conjecture is the following:

Sendov’s conjectureLet be a polynomial of degree that has all zeroes . If is a zero then has a zero in .

Without loss of generality, we may assume that is monic and that . Indeed, if is a polynomial in the variable we divide by the argument of and then rescale by the top coefficient.

Tao notes that by Tarski’s theorem, in principle it suffices to get an upper bound on the degree of a counterexample and then use a computer-assisted proof to complete the argument. I think that for every you’d get a formula in the theory of real closed fields that could be decided in time, where is an absolute constant (which, unfortunately, is exponential in the number of variables of the formula, and so is probably quite large). Worse, Tao is going to use a compactness argument and so is going to get an astronomical bound . Still, something to keep in mind — computer-assisted proofs seem like the future in analysis.

More precisely, Tao proves the following:

Proposition 1For every in a monotone sequence in , let be a monic polynomial of degree with all zeroes in and let satisfy . If for every , has no zeroes in , then .

It’s now pretty natural to see how “cheap nonstandard” methods apply. One can pass to a subsequence countably many times and still preserve the hypotheses of the proposition, so by diagonalization and compactness, we may assume good (but ineffective) convergence properties. For example, we can assume that , where does not depend on and is with respect to .

Using overspill, one can view the proposition model-theoretically: it says that if is a nonstandard natural number, a monic polynomial of degree with a zero , and there are no zeroes of in , then . Tao never fully takes this POV, but frequently appeals to results like the following:

Proposition 2Let be a first-order predicate. Then:

- (Overspill) If for every sufficiently small standard , , then there is an infinitesimal such that .
- (Underspill) If for every infinitesimal , , then there are arbitrarily small such that .
- (-saturation) If is where is compact, then the implied constant in the statement of is independent of .

Henceforth we will use asymptotic notation in the nonstandard sense; for example, a quantity is if it is infinitesimal. This is equivalent to the cheap nonstandard perspective where a quantity is iff it is with respect to , where is ranging over some monotone sequence of naturals. I think the model-theoretic perspective is helpful here because we are going to pass to subsequences a lot, and at least in the presence of the boolean prime ideal theorem, letting be a fixed nonstandard natural number is equivalent to choosing a nonprincipal ultrafilter on that picks out the subsequences we are going to pass to, in the perspective where ranges over a monotone sequence of standard naturals.

This follows because the Stone-Cech compactification is exactly the space of ultrafilters on . Indeed, if ranges over a monotone sequence of standard naturals, then in , converges to an element of , which then is a nonprincipal ultrafilter. If denotes the ultrapower of with respect to , then I think the equivalence class of the sequence in is exactly the limit of . Conversely, once a nonprincipal ultrafilter has been fixed, we have a canonical way to pass to subsequences: only pass to a subsequence which converges to . This is possible since is compact.

I think it will be at times convenient to go back to the “monotone sequence of standard naturals” perspective, especially when we’re doing computations, so I reserve the right to go between the two. We’ll call the monotone sequence perspective the “cheap perspective” and the model-theoretic perspective the “expensive perspective”.

I’m not familiar with the literature on Sendov’s conjecture, so I’m going to blackbox the reduction that Tao carries out. The reduction says that, due to the Gauss-Lucas theorem and previously existing partial results on Sendov’s conjecture, to prove Proposition 1, it suffices to show:

Proposition 3Let be a nonstandard natural, let be a monic polynomial of degree with all zeroes in and let satisfy . Suppose that has no zeroes in and

- (Theorem 3.1 in Tao) either , or
- (Theorem 5.1 in Tao) there is a standard such that

Then .

In the former case we have and in the latter we have . We’ll call the former “case zero” and the latter “case one.”

Tao gives a probabilistic proof of Proposition 3, and that’s going to be the bulk of this post and its sequels. Let be a random zero of , drawn uniformly from the finite set of zeroes. Le denote a random zero of chosen independently of .

In the cheap perspective, depends on , we are going to study properties of the convergence of as , by using our chosen ultrafilter to repeatedly pass to subsequences to make converge in some suitable topology. The probability spaces that lives in depend on , but as long as we are interested in a deterministic limit that does not depend on , this is no problem. Indeed will converge to uniformly (resp. in probability) provided that for every , almost surely (resp. ), and this makes sense even though the probability space we are studying depends on . The usual definition of convergence in distribution still makes sense even for random variables converges to a random variable that deos not depend on provided that their distributions converge vaguely to .

Okay, it’s pretty obvious what being infinitesimally close to a deterministic standard real is in the uniform or probabilistic sense. Expanding out the definition of the vague topology of measures, a nonstandard measure on a locally compact Hausdorff space is infinitesimally close to a standard measure provided that for every continuous function with compact support,

This induces a definition of being infinitesimally close in distribution.

Okay, no more model-theoretic games, it’s time to start the actual proof.

Definition 4Let be a bounded complex random variable. Thelogarithmic potentialof is

Here denotes expected value. Tao claims but does not prove that this definition makes sense for almost every . To check this, let be a compact set equipped with Lebesgue measure and let be the distribution of . Then

and the integrand is singular along the set , which has real codimension in . The double integral of a logarithm makes sense almost surely provided that the logarithm blows up with real codimension (to see this, check the double integral of log on ) so this looks good.

Definition 5Let be a bounded complex random variable. TheStieltjes transformof is

Then is “less singular” than , so this definition is inoffensive almost everywhere.

Henceforth Tao lets denote the distribution of , where is any bounded complex random variable. Then and where are the complex derivative and Cauchy-Riemann operators respectively. Since we have

This just follows straight from the definitions. Of course might not be an absolutely continuous measure, so this only makes sense if we use the calculus of distributions.

Does this make sense if is deterministic, say almost surely? In that case is a Dirac measure at and . Everything looks good, since .

For the next claims I need the Gauss-Lucas theorem:

Theorem 6 (Gauss-Lucas)If is a polynomial on , then all zeroes of belong to the convex hull of the variety .

Lemma 7 (Lemma 1.6i in Tao)surely lies in and surely lies in .

*Proof:* The first claim is just a tautology. For the second, by assumption on all zeroes of lie in the convex set , so so does their convex hull. In particular lies in almost surely by the Gauss-Lucas theorem. Our contradiction hypothesis says that .

Lemma 8 (Lemma 1.6ii-iv in Tao)One has . For almost every ,

and

Moreover,

and

Moreover,

and

*Proof:* We pass to the cheap perspective, so is a large standard natural. Since is large, in particular , if is the coefficient of in then the roots of sum to . The roots of sum to by calculus. So .

We write

and

Taking of both sides and then dividing by we immediately get and . Then we take the complex derivative of both sides of the and formulae to get the formulae for and .

Now the formula for follows by subtracting the above formulae, as does the formula for .

Since the distributions of and have bounded support (it’s contained in ) by Prokhorov’s theorem we can find standard random variables and such that is infinitesimal in distribution and similarly for . The point is that and give, up to an infinitesimal error, information about the behavior of , , and by the above lemma and the following proposition.

Proposition 9 (Theorem 1.10 in Tao)One has:

- In case zero, and are identically distributed and almost surely lie in , so is infinitesimal in probability. Moreover, for every compact set ,
- In case one, is uniformly distributed on and is almost surely zero. Moreover,

The proposition gives quantitative bounds that force the zeroes to all be in certain locations. Looking ahead in the paper, it follows that:

- In case zero, the Stieltjes transform of is infinitesimally close to , so by a stability-of-zeroes argument to show that has no zeroes near the origin, even though .
- In case one, if is the standard deviation of , then we have control on the zeroes of up to an error of size , which we can then use to deduce a contradiction.

Next time I’ll start the proof of this proposition. Its proof apparently follows from the theory of Newtonian potentials, which is not too surprising since if is the Laplacian of . It needs the following lemma:

Lemma 10 (Lemma 1.6vi in Tao)If is a curve in that misses the zeroes of and then

and

*Proof:* One has

by the previous lemma. Breaking up into finitely many parts one can assume that is a contractible curve in a simply connected set, in which case we have a branch of the logarithm along . Now apply the fundamental theorem. The case for is the same.