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In the course of the ongoing logic reading seminar at UCLA, I learned about the property of countable saturation. A model of a language is countably saturated if, every countable sequence of formulae in (involving countably many constants in ) which is finitely satisfiable in (i.e. any finite collection in the sequence has a solution in ), is automatically satisfiable in (i.e. there is a solution to all simultaneously). Equivalently, a model is countably saturated if the topology generated by the definable sets is countably compact.
Update, Nov 19: I have learned that the above terminology is not quite accurate; countable saturation allows for an uncountable sequence of formulae, as long as the constants used remain finite. So, the discussion here involves a weaker property than countable saturation, which I do not know the official term for. If one chooses a special type of ultrafilter, namely a “countably incomplete” ultrafilter, one can recover the full strength of countable saturation, though it is not needed for the remarks here. Most models are not countably saturated. Consider for instance the standard natural numbers as a model for arithmetic. Then the sequence of formulae “” for is finitely satisfiable in , but not satisfiable.
However, if one takes a model of and passes to an ultrapower , whose elements consist of sequences in , modulo equivalence with respect to some fixed non-principal ultrafilter , then it turns out that such models are automatically countably compact. Indeed, if are finitely satisfiable in , then they are also finitely satisfiable in (either by inspection, or by appeal to Los’s theorem and/or the transfer principle in non-standard analysis), so for each there exists which satisfies . Letting be the ultralimit of the , we see that satisfies all of the at once.
In particular, non-standard models of mathematics, such as the non-standard model of the natural numbers, are automatically countably saturated.
This has some cute consequences. For instance, suppose one has a non-standard metric space (an ultralimit of standard metric spaces), and suppose one has a standard sequence of elements of which are standard-Cauchy, in the sense that for any standard one has for all sufficiently large . Then there exists a non-standard element such that standard-converges to in the sense that for every standard one has for all sufficiently large . Indeed, from the standard-Cauchy hypothesis, one can find a standard for each standard that goes to zero (in the standard sense), such that the formulae “” are finitely satisfiable, and hence satisfiable by countable saturation. Thus we see that non-standard metric spaces are automatically “standardly complete” in some sense.
This leads to a non-standard structure theorem for Hilbert spaces, analogous to the orthogonal decomposition in Hilbert spaces:
Theorem 1 (Non-standard structure theorem for Hilbert spaces) Let be a non-standard Hilbert space, let be a bounded (external) subset of , and let . Then there exists a decomposition , where is “almost standard-generated by ” in the sense that for every standard , there exists a standard finite linear combination of elements of which is within of , and is “standard-orthogonal to ” in the sense that for all .
Proof: Let be the infimum of all the (standard) distances from to a standard linear combination of elements of , then for every standard one can find a standard linear combination of elements of which lie within of . From the parallelogram law we see that is standard-Cauchy, and thus standard-converges to some limit , which is then almost standard-generated by by construction. An application of Pythagoras then shows that is standard-orthogonal to every element of .
This is the non-standard analogue of a combinatorial structure theorem for Hilbert spaces (see e.g. Theorem 2.6 of my FOCS paper). There is an analogous non-standard structure theorem for -algebras (the counterpart of Theorem 3.6 from that paper) which I will not discuss here, but I will give just one sample corollary:
Theorem 2 (Non-standard arithmetic regularity lemma) Let be a non-standardly finite abelian group, and let be a function. Then one can split , where is standard-uniform in the sense that all Fourier coefficients are (uniformly) , and is standard-almost periodic in the sense that for every standard , one can approximate to error in norm by a standard linear combination of characters (which is also bounded).
This can be used for instance to give a non-standard proof of Roth’s theorem (which is not much different from the “finitary ergodic” proof of Roth’s theorem, given for instance in Section 10.5 of my book with Van Vu). There is also a non-standard version of the Szemerédi regularity lemma which can be used, among other things, to prove the hypergraph removal lemma (the proof then becomes rather close to the infinitary proof of this lemma in this paper of mine). More generally, the above structure theorem can be used as a substitute for various “energy increment arguments” in the combinatorial literature, though it does not seem that there is a significant saving in complexity in doing so unless one is performing quite a large number of these arguments.
One can also cast density increment arguments in a nonstandard framework. Here is a typical example. Call a non-standard subset of a non-standard finite set dense if one has for some standard .
Theorem 3 Suppose Szemerédi’s theorem (every set of integers of positive upper density contains an arithmetic progression of length ) fails for some . Then there exists an unbounded non-standard integer , a dense subset of with no progressions of length , and with the additional property that
for any subprogression of of unbounded size (thus there is no sizeable density increment on any large progression).
Proof: Let be a (standard) set of positive upper density which contains no progression of length . Let be the asymptotic maximal density of inside a long progression, thus . For any , one can then find a standard integer and a standard subset of of density at least such that can be embedded (after a linear transformation) inside , so in particular has no progressions of length . Applying the saturation property, one can then find an unbounded and a set of of density at least for every standard (i.e. of density at least ) with no progressions of length . By construction, we also see that for any subprogression of of unbounded size, hs density at most for any standard , thus has density at most , and the claim follows.
This can be used as the starting point for any density-increment based proof of Szemerédi’s theorem for a fixed , e.g. Roth’s proof for , Gowers’ proof for arbitrary , or Szemerédi’s proof for arbitrary . (It is likely that Szemerédi’s proof, in particular, simplifies a little bit when translated to the non-standard setting, though the savings are likely to be modest.)
I’m also hoping that the recent results of Hrushovski on the noncommutative Freiman problem require only countable saturation, as this makes it more likely that they can be translated to a non-standard setting and thence to a purely finitary framework.
Last year on this blog, I sketched out a non-rigorous probabilistic argument justifying the following well-known theorem:
Theorem 1. (Non-measurable sets exist) There exists a subset of the unit interval which is not Lebesgue-measurable.
The idea was to let E be a “random” subset of . If one (non-rigorously) applies the law of large numbers, one expects E to have “density” 1/2 with respect to every subinterval of , which would contradict the Lebesgue differentiation theorem.
I was recently asked whether I could in fact make the above argument rigorous. This turned out to be more difficult than I had anticipated, due to some technicalities in trying to make the concept of a random subset of (which requires an uncountable number of “coin flips” to generate) both rigorous and useful. However, there is a simpler variant of the above argument which can be made rigorous. Instead of letting E be a “random” subset of , one takes E to be an “alternating” set that contains “every other” real number in ; this again should have density 1/2 in every subinterval and thus again contradict the Lebesgue differentiation theorem.
Of course, in the standard model of the real numbers, it makes no sense to talk about “every other” or “every second” real number, as the real numbers are not discrete. If however one employs the language of non-standard analysis, then it is possible to make the above argument rigorous, and this is the purpose of my post today. I will assume some basic familiarity with non-standard analysis, for instance as discussed in this earlier post of mine.
This post is in some ways an antithesis of my previous postings on hard and soft analysis. In those posts, the emphasis was on taking a result in soft analysis and converting it into a hard analysis statement (making it more “quantitative” or “effective”); here we shall be focusing on the reverse procedure, in which one harnesses the power of infinitary mathematics – in particular, ultrafilters and nonstandard analysis – to facilitate the proof of finitary statements.
Arguments in hard analysis are notorious for their profusion of “epsilons and deltas”. In the more sophisticated arguments of this type, one can end up having an entire army of epsilons that one needs to manage, in particular choosing each epsilon carefully to be sufficiently small compared to other parameters (including other epsilons), while of course avoiding an impossibly circular situation in which a parameter is ultimately required to be small with respect to itself, which is absurd. This art of epsilon management, once mastered, is not terribly difficult – it basically requires one to mentally keep track of which quantities are “small”, “very small”, “very very small”, and so forth – but when these arguments get particularly lengthy, then epsilon management can get rather tedious, and also has the effect of making these arguments unpleasant to read. In particular, any given assertion in hard analysis usually comes with a number of unsightly quantifiers (For every there exists an N…) which can require some thought for a reader to parse. This is in contrast with soft analysis, in which most of the quantifiers (and the epsilons) can be cleanly concealed via the deployment of some very useful terminology; consider for instance how many quantifiers and epsilons are hidden within, say, the Heine-Borel theorem: “a subset of a Euclidean space is compact if and only if it is closed and bounded”.
For those who practice hard analysis for a living (such as myself), it is natural to wonder if one can somehow “clean up” or “automate” all the epsilon management which one is required to do, and attain levels of elegance and conceptual clarity comparable to those in soft analysis, hopefully without sacrificing too much of the “elementary” or “finitary” nature of hard analysis in the process.