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Roughly speaking, mathematical analysis can be divided into two major styles, namely *hard analysis* and *soft analysis*. The precise distinction between the two types of analysis is imprecise (and in some cases one may use a blend the two styles), but some key differences can be listed as follows.

- Hard analysis tends to be concerned with
*quantitative*or*effective*properties such as estimates, upper and lower bounds, convergence rates, and growth rates or decay rates. In contrast, soft analysis tends to be concerned with*qualitative*or*ineffective*properties such as existence and uniqueness, finiteness, measurability, continuity, differentiability, connectedness, or compactness. - Hard analysis tends to be focused on
*finitary*,*finite-dimensional*or*discrete*objects, such as finite sets, finitely generated groups, finite Boolean combination of boxes or balls, or “finite-complexity” functions, such as polynomials or functions on a finite set. In contrast, soft analysis tends to be focused on*infinitary*,*infinite-dimensional*, or*continuous*objects, such as arbitrary measurable sets or measurable functions, or abstract locally compact groups. - Hard analysis tends to involve explicit use of many parameters such as , , , etc. In contrast, soft analysis tends to rely instead on properties such as continuity, differentiability, compactness, etc., which
*implicitly*are defined using a similar set of parameters, but whose parameters often do not make an*explicit*appearance in arguments. - In hard analysis, it is often the case that a key lemma in the literature is not quite optimised for the application at hand, and one has to reprove a slight variant of that lemma (using a variant of the proof of the original lemma) in order for it to be suitable for applications. In contrast, in soft analysis, key results can often be used as “black boxes”, without need of further modification or inspection of the proof.
- The properties in soft analysis tend to enjoy precise closure properties; for instance, the composition or linear combination of continuous functions is again continuous, and similarly for measurability, differentiability, etc. In contrast, the closure properties in hard analysis tend to be fuzzier, in that the parameters in the conclusion are often different from the parameters in the hypotheses. For instance, the composition of two Lipschitz functions with Lipschitz constant is still Lipschitz, but now with Lipschitz constant instead of . These changes in parameters mean that hard analysis arguments often require more “bookkeeping” than their soft analysis counterparts, and are less able to utilise algebraic constructions (e.g. quotient space constructions) that rely heavily on precise closure properties.

In the lectures so far, focusing on the theory surrounding Hilbert’s fifth problem, the results and techniques have fallen well inside the category of soft analysis. However, we will now turn to the theory of approximate groups, which is a topic which is traditionally studied using the methods of hard analysis. (Later we will also study groups of polynomial growth, which lies on an intermediate position in the spectrum between hard and soft analysis, and which can be profitably analysed using both styles of analysis.)

Despite the superficial differences between hard and soft analysis, though, there are a number of important *correspondences* between results in hard analysis and results in soft analysis. For instance, if one has some sort of uniform quantitative bound on some expression relating to finitary objects, one can often use limiting arguments to then conclude a qualitative bound on analogous expressions on infinitary objects, by viewing the latter objects as some sort of “limit” of the former objects. Conversely, if one has a qualitative bound on infinitary objects, one can often use *compactness and contradiction arguments* to recover uniform quantitative bounds on finitary objects as a corollary.

Remark 1Another type of correspondence between hard analysis and soft analysis, which is “syntactical” rather than “semantical” in nature, arises by taking theproofsof a soft analysis result, and translating such a qualitative proof somehow (e.g. by carefully manipulating quantifiers) into a quantitative proof of an analogous hard analysis result. This type of technique is sometimes referred to asproof miningin the proof theory literature, and is discussed in this previous blog post (and its comments). We will however not employ systematic proof mining techniques here, although in later posts we will informally borrow arguments from infinitary settings (such as the methods used to construct Gleason metrics) and adapt them to finitary ones.

Let us illustrate the correspondence between hard and soft analysis results with a simple example.

Proposition 1Let be a sequentially compact topological space, let be a dense subset of , and let be a continuous function (giving the extended half-line the usual order topology). Then the following statements are equivalent:

- (i) (Qualitative bound on infinitary objects) For all , one has .
- (ii) (Quantitative bound on finitary objects) There exists such that for all .

In applications, is typically a (non-compact) set of “finitary” (or “finite complexity”) objects of a certain class, and is some sort of “completion” or “compactification” of which admits additional “infinitary” objects that may be viewed as limits of finitary objects.

*Proof:* To see that (ii) implies (i), observe from density that every point in is adherent to , and so given any neighbourhood of , there exists . Since , we conclude from the continuity of that also, and the claim follows.

Conversely, to show that (i) implies (ii), we use the “compactness and contradiction” argument. Suppose for sake of contradiction that (ii) failed. Then for any natural number , there exists such that . (Here we have used the axiom of choice, which we will assume throughout this course.) Using sequential compactness, and passing to a subsequence if necessary, we may assume that the converge to a limit . By continuity of , this implies that , contradicting (i).

Remark 2Note that the above deduction of (ii) from (i) isineffectivein that it gives no explicit bound on the uniform bound in (ii). Without any further information onhowthe qualitative bound (i) is proven, this is the best one can do in general (and this is one of the most significant weaknesses of infinitary methods when used to solve finitary problems); but if one has access to the proof of (i), one can oftenfinitiseorproof minethat argument to extract an effective bound for , although often the bound one obtains in the process is quite poor (particularly if the proof of (i) relied extensively on infinitary tools, such as limits). See this blog post for some related discussion.

The above simple example illustrates that in order to get from an “infinitary” statement such as (i) to a “finitary” statement such as (ii), a key step is to be able to take a sequence (or in some cases, a more general net ) of finitary objects and extract a suitable infinitary limit object . In the literature, there are three main ways in which one can extract such a limit:

- (Topological limit) If the are all elements of some topological space (e.g. an incomplete function space) which has a suitable “compactification” or “completion” (e.g. a Banach space), then (after passing to a subsequence if necessary) one can often ensure the converge in a topological sense (or in a metrical sense) to a limit . The use of this type of limit to pass between quantitative/finitary and qualitative/infinitary results is particularly common in the more analytical areas of mathematics (such as ergodic theory, asymptotic combinatorics, or PDE), due to the abundance of useful compactness results in analysis such as the (sequential) Banach-Alaoglu theorem, Prokhorov’s theorem, the Helly selection theorem, the Arzelá-Ascoli theorem, or even the humble Bolzano-Weierstrass theorem. However, one often has to take care with the nature of convergence, as many compactness theorems only guarantee convergence in a weak sense rather than in a strong one.
- (Categorical limit) If the are all objects in some category (e.g. metric spaces, groups, fields, etc.) with a number of morphisms between the (e.g. morphisms from to , or vice versa), then one can often form a direct limit or inverse limit of these objects to form a limiting object . The use of these types of limits to connect quantitative and qualitative results is common in subjects such as algebraic geometry that are particularly amenable to categorical ways of thinking. (We have seen inverse limits appear in the discussion of Hilbert’s fifth problem, although in that context they were not really used to connect quantitative and qualitative results together.)
- (Logical limit) If the are all distinct spaces (or elements or subsets of distinct spaces), with few morphisms connecting them together, then topological and categorical limits are often unavailable or unhelpful. In such cases, however, one can still tie together such objects using an ultraproduct construction (or similar device) to create a limiting object or limiting space that is a
*logical limit*of the , in the sense that various properties of the (particularly those that can be phrased using the language of first-order logic) are preserved in the limit. As such, logical limits are often very well suited for the task of connecting finitary and infinitary mathematics together. Ultralimit type constructions are of course used extensively in logic (particularly in model theory), but are also popular in metric geometry. They can also be used in many of the previously mentioned areas of mathematics, such as algebraic geometry (as discussed in this previous post).

The three types of limits are analogous in many ways, with a number of connections between them. For instance, in the study of groups of polynomial growth, both topological limits (using the metric notion of Gromov-Hausdorff convergence) and logical limits (using the ultralimit construction) are commonly used, and to some extent the two constructions are at least partially interchangeable in this setting. (See also these previous posts for the use of ultralimits as a substitute for topological limits.) In the theory of approximate groups, though, it was observed by Hrushovski that logical limits (and in particular, *ultraproducts*) are the most useful type of limit to connect finitary approximate groups to their infinitary counterparts. One reason for this is that one is often interested in obtaining results on approximate groups that are uniform in the choice of ambient group . As such, one often seeks to take a limit of approximate groups that lie in completely unrelated ambient groups , with no obvious morphisms or metrics tying the to each other. As such, the topological and categorical limits are not easily usable, whereas the logical limits can still be employed without much difficulty.

Logical limits are closely tied with non-standard analysis. Indeed, by applying an ultraproduct construction to standard number systems such as the natural numbers or the reals , one can obtain nonstandard number systems such as the nonstandard natural numbers or the nonstandard real numbers (or hyperreals) . These nonstandard number systems behave very similarly to their standard counterparts, but also enjoy the advantage of containing the standard number systems as proper subsystems (e.g. is a subring of ), which allows for some convenient algebraic manipulations (such as the quotient space construction to create spaces such as ) which are not easily accessible in the purely standard universe. Nonstandard spaces also enjoy a useful completeness property, known as countable saturation, which is analogous to metric completeness (as discussed in this previous blog post) and which will be particularly useful for us in tying together the theory of approximate groups with the theory of Hilbert’s fifth problem. See this previous post for more discussion on ultrafilters and nonstandard analysis.

In these notes, we lay out the basic theory of ultraproducts and ultralimits (in particular, proving Los’s theorem, which roughly speaking asserts that ultralimits are limits in a logical sense, as well as the countable saturation property alluded to earlier). We also lay out some of the basic foundations of nonstandard analysis, although we will not rely too heavily on nonstandard tools in this course. Finally, we apply this general theory to approximate groups, to connect finite approximate groups to an infinitary type of approximate group which we will call an *ultra approximate group*. We will then study these ultra approximate groups (and models of such groups) in more detail in the next set of notes.

Remark 3Throughout these notes (and in the rest of the course), we will assume the axiom of choice, in order to easily use ultrafilter-based tools. If one really wanted to expend the effort, though, one could eliminate the axiom of choice from the proofs of the final “finitary” results that one is ultimately interested in proving, at the cost of making the proofs significantly lengthier. Indeed, there is a general result of Gödel that any result which can be stated in the language of Peano arithmetic (which, roughly speaking, means that the result is “finitary” in nature), and can be proven in set theory using the axiom of choice (or more precisely, in the ZFC axiom system), can also be proven in set theory without the axiom of choice (i.e. in the ZF system). As this course is not focused on foundations, we shall simply assume the axiom of choice henceforth to avoid further distraction by such issues.

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