You want to prove some statement about some object (which could be a number, a point, a function, a set, etc.). To do so, pick a small , and first prove a weaker statement (which allows for “losses” which go to zero as ) about some perturbed object . Then, take limits . Provided that the dependency and continuity of the weaker conclusion on are sufficiently controlled, and is converging to in an appropriately strong sense, you will recover the original statement.
One can of course play a similar game when proving a statement about some object , by first proving a weaker statement on some approximation to for some large parameter N, and then send at the end.
The first, simplest instance of this trick would be to prove that an object satisfies a property , which we know that conmutes with a limit in some sense, by finding a sequence of objects such that is invariant on it and such that it converges (in the same limit sense) to .♦ The power of the method goes in the difficulty of proving the satisfaction of : should be easy to prove for every by some standard techniques, but difficult for with the same techniques. Note that this instance is somewhat the "reciprocal" of a limiting process (where we replace a sequence by its limit).
But of course, we don't need to be invariant in the sequence: we just need to have a perturbed property for every such that converges to as converges to . Moreover, we don't need the index set to be countable: we can use a net; for example, we can indize with the reals and use the variable .
Here are some typical examples of a target statement , and the approximating statements that would converge to :
|for some independent of|
|is finite||is bounded uniformly in|
|for all (i.e. maximises f)||for all (i.e. nearly maximises f)|
|converges as||fluctuates by at most o(1) for sufficiently large n|
|is a measurable function||is a measurable function converging pointwise to|
|is a continuous function||is an equicontinuous family of functions converging pointwise to OR is continuous and converges (locally) uniformly to|
|The event holds almost surely||The event holds with probability 1-o(1)|
|The statement holds for almost every x||The statement holds for x outside of a set of measure o(1)|
Of course, to justify the convergence of to , it is necessary that converge to (or converge to , etc.) in a suitably strong sense. (But for the purposes of proving just upper bounds, such as , one can often get by with quite weak forms of convergence, thanks to tools such as Fatou's lemma or the weak closure of the unit ball.) Similarly, we need some continuity (or at least semi-continuity) hypotheses on the functions f, g appearing above.
It is also necessary in many cases that the control on the approximating object is somehow "uniform in ", although for "-closed" conclusions, such as measurability, this is not required. [It is important to note that it is only the final conclusion on that needs to have this uniformity in ; one is permitted to have some intermediate stages in the derivation of that depend on in a non-uniform manner, so long as these non-uniformities cancel out or otherwise disappear at the end of the argument.]
By giving oneself an epsilon of room, one can evade a lot of familiar issues in soft analysis. For instance, by replacing "rough", "infinite-complexity", "continuous", "global", or otherwise "infinitary" objects with "smooth", "finite-complexity", "discrete", "local", or otherwise "finitary" approximants , one can finesse most issues regarding the justification of various formal operations (e.g. exchanging limits, sums, derivatives, and integrals). [It is important to be aware, though, that any quantitative measure on how smooth, discrete, finite, etc. should be expected to degrade in the limit , and so one should take extreme caution in using such quantitative measures to derive estimates that are uniform in .] Similarly, issues such as whether the supremum of a function on a set is actually attained by some maximiser become moot if one is willing to settle instead for an almost-maximiser , e.g. one which comes within an epsilon of that supremum M (or which is larger than , if M turns out to be infinite). Last, but not least, one can use the epsilon room to avoid degenerate solutions, for instance by perturbing a non-negative function to be strictly positive, perturbing a non-strictly monotone function to be strictly monotone, and so forth.
In applying the epsilon regularization trick, one often needs to approximate rough functions by smooth ones. One useful way of doing so is to use the trick "To make a function nicer without changing it much, convolve it with an approximate delta function".
To summarise: one can view the epsilon regularisation argument as a "loan" in which one borrows an epsilon here and there in order to be able to ignore soft analysis difficulties, and can temporarily be able to utilise estimates which are non-uniform in epsilon, but at the end of the day one needs to "pay back" the loan by establishing a final "hard analysis" estimate which is uniform in epsilon (or whose error terms decay to zero as epsilon goes to zero).
A variant: It may seem that the epsilon regularisation trick is useless if one is already in "hard analysis" situations when all objects are already "finitary", and all formal computations easily justified. However, there is an important variant of this trick which applies in this case: namely, instead of sending the epsilon parameter to zero, choose epsilon to be a sufficiently small (but not infinitesimally small) quantity, depending on other parameters in the problem, so that one can eventually neglect various error terms and to obtain a useful bound at the end of the day. (For instance, any result proven using the Szemerédi regularity lemma is likely to be of this type.) Since one is not sending epsilon to zero, not every term in the final bound needs to be uniform in epsilon, though for quantitative applications one still would like the dependencies on such parameters to be as favourable as possible.
Graduate real analysis. (Actually, this isn't so much a prerequisite as it is a corequisite: the limiting argument plays a central role in many fundamental results in real analysis.) Some examples also require some exposure to PDE.
Example 2: The Riemann-Lebesgue lemma
Given any absolutely integrable function , the Fourier transform is defined by the formula
The Riemann-Lebesgue lemma asserts that as . It is difficult to prove this estimate for f directly, because this function is too "rough": it is absolutely integrable (which is enough to ensure that exists and is bounded), but need not be continuous, differentiable, compactly supported, bounded, or otherwise "nice". But suppose we give ourselves an epsilon of room. Then, as the space of test functions is dense in , we can approximate to any desired accuracy in the norm by a smooth, compactly supported function , thus
The point is that is much better behaved than f, and it is not difficult to show the analogue of the Riemann-Lebesgue lemma for . Indeed, being smooth and compactly supported, we can now justifiably integrate by parts to obtain
for any non-zero , and it is now clear (since is bounded and compactly supported) that as .
Now we need to take limits as . It will be enough to have converge uniformly to . But from (1) and the basic estimate
(which is the single "hard analysis" ingredient in the proof of the lemma) applied to , we see (by the linearity of the Fourier transform) that
and we obtain the desired uniform convergence.
The limiting argument in the previous example relied on the linearity of the Fourier transform . But, with more effort, it is also possible to extend this type of argument to nonlinear settings. We will sketch (omitting several technical details, which can be found for instance in this PDE book) a very typical instance. Consider a nonlinear PDE, e.g. the nonlinear wave equation
where is some scalar field, and the t and x subscripts denote differentiation of the field . Formally - if u is sufficiently smooth, and sufficiently decaying at spatial infinity, one can show that the energy
is conserved, thus for all t. This can be formally justified by computing the derivative by differentiating under the integral sign, integrating by parts, and then applying the PDE (3); we leave this as an exercise for the reader. (There are also more fancy ways to see why the energy is conserved, using Hamiltonian or Lagrangian mechanics or by the more general theory of stress-energy tensors, but we will not discuss these here.) However, these justifications do require a fair amount of regularity on the solution u; for instance, requiring u to be three-times continuously differentiable in space and time, and compactly supported in space on each bounded time interval, would be sufficient to make the computations rigorous by applying "off the shelf" theorems about differentiation under the integration sign, etc.
But suppose one only has a much rougher solution, for instance an energy class solution which has finite energy (4), but for which higher derivatives of u need not exist in the classical sense. (There is a non-trivial issue regarding how to make sense of the PDE (3) when u is only in the energy class, since the terms and do not then make sense classically, but there are standard ways to deal with this, e.g. using weak derivatives, which we will not discuss further here.) Then it is difficult to justify the energy conservation law directly. However, it is still possible to obtain energy conservation by the limiting argument. Namely, one takes the energy class solution u at some initial time (e.g. t=0) and approximates that initial data (the initial position and initial data ) by a much smoother (and compactly supported) choice of initial data, which converges back to in a suitable "energy topology" related to (4) which we will not define here. It then turns out (from the existence theory of the PDE (3)) that one can extend the smooth initial data to other times t, providing a smooth solution to that data. For this solution, the energy conservation law can be justified.
Now we take limits as (keeping t fixed). Since converges in the energy topology to , and the energy functional E is continuous in this topology, converges to . To conclude the argument, we will also need to converge to , which will be possible if converges in the energy topology to . Thus in turn follows from a fundamental fact (which requires a certain amount of effort to prove) about the PDE to (4), namely that it is well-posed in the energy class. This means that not only do solutions exist and are unique for initial data in the energy class, but they depend continuously on the initial data in the energy topology; small perturbations in the data lead to small perturbations in the solution, or more formally that the map from data to solution (say, at some fixed time t) is continuous in the energy topology. This final fact concludes the limiting argument and gives us the desired conservation law .
Example 4: The maximum principle
The maximum principle is a fundamental tool in elliptic and parabolic PDE (for example, it is used heavily in the proof of the Poincaré conjecture, see e.g. my lecture notes on this topic). Here is a model example of this principle:
A naive attempt to prove proposition 1 comes very close to working, and goes like this: suppose for contradiction that the proposition failed, thus u exceeds M somewhere in the interior of the disk. Since u is continuous, and the disk is compact, there must then be a point in the interior of the disk where the maximum is attained. Undergraduate calculus then tells us that and are non-positive, which almost contradicts the harmonicity hypothesis . However, it is still possible that and both vanish at , so we don't yet get a contradiction.
But we can finish the proof by giving ourselves an epsilon of room. The trick is to work not with the function u directly, but with the modified function , to boost the harmonicity into subharmonicity. Indeed, we have . The preceding argument now shows that cannot attain its maximum in the interior of the disk; since it is bounded by on the boundary of the disk, we conclude that is bounded by on the interior of the disk as well. Sending we obtain the claim.
Example 5: Manipulating generalised functions
In PDE one is primarily interested in smooth (classical) solutions; but for a variety of reasons it is useful to also consider rougher solutions. Sometimes, these solutions are so rough that they are no longer functions, but are measures, distributions, or some other concept of "generalised function" or "generalised solution". For instance, the fundamental solution to a PDE is typically just a distribution or measure, rather than a classical function. A typical example: a (sufficiently smooth) solution to the three-dimensional wave equation with initial position and initial velocity is given by the classical formula
where is the unique rotation-invariant probability measure on the sphere of radius t, or equivalently, the area element dS on that sphere divided by the surface area of that sphere. (The convolution of a smooth function and a (compactly supported) finite measure is defined by .)
For this and many other reasons, it is important to manipulate measures and distributions in various ways. For instance, in addition to convolving functions with measures, it is also useful to convolve measures with measures; the convolution of two finite measures on is defined as the measure which assigns to each measurable set E in , the measure
For sake of concreteness, let's focus on a specific question, namely to compute (or at least estimate) the measure , where is the normalised rotation-invariant measure on the unit circle . It turns out that while is not absolutely continuous with respect to Lebesgue measure , the convolution is: for some absolutely integrable function f on . But what is this function f? It certainly is possible to compute it from the definition (5), or by other methods (e.g. the Fourier transform), but I would like to give one approach to computing these sorts of expressions involving measures (or other generalised functions) based on epsilon regularisation, which requires a certain amount of geometric computation but which I find to be rather visual and conceptual, compared to more algebraic approaches (e.g. based on Fourier transforms). The idea is to approximate a singular object, such as the singular measure , by a smoother object , such as an absolutely continuous measure. For instance, one can approximate by
where . It is clear that converges to in the vague topology, which implies that converges to in the vague topology also. Since
we will be able to understand the limit f by first considering the function
and then taking (weak) limits as to recover f.
Up to constants, one can compute from elementary geometry that is comparable to , and vanishes for , and is comparable to for (and of size in the transition region ) and is comparable to for (and of size about when . (This is a good exercise for anyone who wants practice in quickly computing the orders of magnitude of geometric quantities such as areas; for such order of magnitude calculations, quick and dirty geometric methods tend to work better here than the more algebraic calculus methods you would have learned as an undergraduate.) The bounds here are strong enough to allow one to take limits and conclude what f looks like: it is comparable to . And by being more careful with the computations of area, one can compute the exact formula for f(x) (more detail needed here).