E. Kowalski's blog

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A tricks-wiki article: smooth sums


Here is a first trial at writing a possible article for the mathematical wiki that T. Gowers has been discussing (starting with this post).

For comparison, he has himself written a few sample articles, and so has T. Tao:

    Recognising countable sets (T. Gowers)
    Dimension arguments in combinatorics (T. Gowers)
    The tensor product trick (T. Tao)

I follow the general outline of these articles, but I’ve added, after the general discussion at the beginning, a list of links to the specific examples which are discussed further on. This seems to improve navigability for readers who might feel that one particular example is closer to what they are looking for.

The writing is quite rough in places, as it wasn’t easy to get the right feel for the level and detail of exposition (for instance, the later examples are treated more briefly). Also, there are probably not enough references and links, and so I will certainly make minor adjustments over the next few days to remedy some of this.

Title: Smooth sums

Quick description: It is often difficult to evaluate asymptotically sums of the type


but much simpler to deal with

\sum_{n\geq 1}{a(n)\varphi(n)}

where φ is a smooth function, which vanishes or decays very fast for n larger than N. Such sums are called smoothed sums.

This trick is useful because it often turns out, either that the original problem leading to the first type of sums could be treated using the second type; or that understanding the smoothed sums may be the right path to understanding the first ones.

General discussion: The underlying facts behind this trick are two elementary properties of harmonic analysis: (1) a Fourier (or similar) transform of a product is the convolution of the Fourier transforms of the arguments; (2) in the sums above, one of the arguments in the product is an (implicit) characteristic function of the interval of summation, and Fourier transforms exchange properties of regularity with properties of decay at infinity, so that after applying the Fourier transform to the product, the smoothing function becomes a rapidly decaying factor which simplifies many further analytic manipulations. Thus, this technique may be interpreted also as some form of regularization.

The effect is the trick is thus to eliminate some purely analytic problems of convergence which are otherwise unrelated to the main issue of interest. This trick is often especially useful in situations where the other argument of the product involved is non-negative. In particular, it is very relevant for asymptotic counting problems in analytic number theory and related fields. It may also fruitfully be combined with dyadic partition arguments (see this (unwritten) tricki article).

Prerequisites: Real analysis and integration theory, complex analysis for some applications. Elementary harmonic analysis (such as Fourier transforms, or Mellin transforms, depending on the type of applications). In particular, the two facts indicated above (behavior with respect to product, and exchange of regularity and decay) are important.

Here are links to the examples to be found below:

Example 1. (Summation of Fourier series)

Example 2. (Fourier inversion formula)

Example 3. (Estimating sums of arithmetic functions)

Example 4. (The Prime Number Theorem)

Example 5. (The analytic large-sieve inequality)

Example 6. (The approximate functional equation for ζ(s))

Example 7. (The explicit formula)


Example 1. (Summation of Fourier series) If f is a periodic function with period 1 on the real line, which is integrable (in the Lebesgue sense, say) on the closed interval [0,1], the Fourier coefficients of f are given by

a_n=\int_0^1{f(t)\exp(-2i\pi nt)dt}

for any integer n. A basic problem of Fourier analysis is to determine when the Fourier series

x\mapsto \sum_{n\in\mathbf{Z}}{a_n \exp(2i\pi n x)}

exists and coincides with f.


S_N(x)=\sum_{-N\leq n\leq N}{a_n \exp(2i\pi n x)}

be the partial sum of this series. We can also interpret the formula as

S_N(x)=\sum_{n\in\mathbf{Z}}{a_n\exp(2i\pi nx)\varphi_N(x)}

where the sum is again unbounded, but the Fourier coefficients are “twisted” with the characteristic function of the finite interval of summation

\varphi_N(x)=0\ \text{if}\ |x|>N,\ \varphi_N(x)=1\ \text{if}\ |x|\leq N

The multiplication-convolution relation of harmonic analysis and the definition of the Fourier coefficients lead to



D_N(x)=\sum_{|n|\leq N}{\exp(2i\pi nx)}= \frac{\sin((2N+1)\pi x)}{\sin \pi x}

is the so-called Dirichlet kernel of order N. This convolution expression is very useful to investigate when the partial sums converge to f(x), but it is well-known that some regularity assumptions on f are needed: there exist integrable functions f such that the Fourier series diverges almost everywhere, as first shown by Kolmogorov.

A significantly better behavior, for continuous functions f, is obtained if, instead of the partial sums above, the Fejér sums are considered:

\sigma_N(x)=\sum_{-N\leq n\leq N}{a_n \exp(2i\pi n x)\left(1-\frac{|n|}{N}\right)}=\frac{1}{N}(S_0(x)+\cdots+S_{N-1}(x)).

These are our first examples of smoothed sums, since the first expression shows that they differ from the partial sums by the replacement of the characteristic function φN(x) by the function


which is more regular: it is continuous on R. Intuitively, one may expect a better analytic behavior of those modified sums because of the averaging involved (by the second expression for the Fejér sums), or because the “smoother” cut-off at the end of the interval of summation implies that those sums should be less susceptible to sudden violent oscillations of the size of the Fourier coefficients.

This is visible analytically from the integral expression which is now



F_N(x)=\frac{1}{n}\frac{\sin^2(n\pi x)}{\sin^2\pi x}

is the Fejér kernel. Because this kernel is non-negative, it is not very difficult to prove that

\lim_{N\rightarrow +\infty}{\sigma_N(x)}=f(x)

for all x if f is continuous. (And in fact, if f is of class C1, it is also easy to deduce that the original partial sum do converge, proving Dirichlet’s theorem on the convergence of Fourier series for such functions).

Example 2. (Fourier inversion formula) If we consider a function f which is integrable on R, the Fourier transform of f is the function defined by

g(x)=\int_{\mathbf{R}}{f(t)\exp(-2i\pi xt)dt}

for x in R. The Fourier inversion formula states that if g is itself integrable, then we can recover the function f by the formula

f(t)=\int_{\mathbf{R}}{g(x)\exp(2i\pi xt)dx}.

A first idea to prove this is to replace the values g(x) on the right-hand side of this expression by their definition as an integral, and apply Fubini’s theorem to exchange the two integrals; this leads formally to

\int_{\mathbf{R}}{f(t)\left(\int_{\mathbf{R}}{\exp(2i\pi x(t-u)du}\right)dt}

but the problem is that the inner integral over u does not exist in the Lebesgue sense. However, if g is replaced by a smoother version obtained by multiplying with a function φ which decays fast enough at infinity, then the same computation leads to

\int_{\mathbf{R}}{g(x)\varphi(x)\exp(2i\pi xt)dx}=\int_{\mathbf{R}}{f(t)\left(\int_{\mathbf{R}}{\varphi(x)\exp(2i\pi x(t-u)du}\right)dt},

which is now legitimate. Selecting a sequence of such functions φ which converge (pointwise) to the constant function 1, the left-hand side converges to

\int_{\mathbf{R}}{g(x)\exp(2i\pi xt)dx}

while a standard Dirac-sequence argument shows that

\int_{\mathbf{R}}{\varphi(x)\exp(2i\pi xu)du}

behaves like a Dirac function at the origin, hence the right-hand side of the smoothed formula converges to f(t).

Note. In these two first examples, one can see the “regularization” aspect very clearly, and one could also invoke more abstract ideas of the theory of distributions to express the same arguments. (For instance, for Example 2, one can say that Fourier inversion holds for the Dirac measure with Fourier transform 1, and that the formal properties involving convolution then extend the formula to more general functions). In the next examples, involving analytic number theory, it is often of the greatest importance to have explicitly quantitative estimates at all steps, and this is often more easily done using computations with functions instead of distributions. However, which functions are used for smoothing is often of little importance.

Example 3. (Estimating sums of arithmetic functions) Let a(n) be an arithmetic function, or in other words a complex-valued function defined for positive integers, and let A(x) denote the summatory function

A(x)=\sum_{1\leq n\leq x}{a(n)}.

Many different problems of analytic number theory can be expressed as the problem of understanding the asymptotic behavior of such sums as x goes to infinity. In Example 4, we describe a celebrated example, the Prime Number Theorem where a(n) is the characteristic function of the set of primes, but here we look at a slightly simpler situation where we assume that

a(n)\geq 0

and that we want to have an upper-bound for A(x), instead of an asymptotic formula. (This weakened goal might be natural, either because we do not need an asymptotic formula for further applications, or because some uniformity in parameters is needed which is easier to arrange with upper bounds). Quite often, this is approached by trying to use the properties of the Dirichlet generating series

D(s)=\sum_{n\geq 1}{a(n)n^{-s}}

provided it converges at least in some half-plane where the real part of s is larger than some C.

Then we can replace the implicit characteristic function of the interval [1,x] in A(x) by any smooth(er) function which is pointwise larger. For instance, if we choose a function φ as in the graph below

Smoothing function

so that it is smooth, non-negative, compactly supported on [0,2], and such that

\varphi(x)=1\ for\ 0\leq x\leq 1,

then we have

A(x)\leq A_{\varphi}(x)=\sum_{n\geq 1}{a(n)\varphi(n/x)}.

Using the Mellin transform (the multiplicative counterpart to the Fourier transform), we can write


where we integrate over a vertical line in the complex plane with real part σ>0, and


Because we use a compactly supported function, it is easy to justify the exchange of the sum over n and the integral representing φ(n/x), and obtain

A(x)\leq \frac{1}{2i\pi}\int_{\sigma-i\infty}^{\sigma+i\infty}{\hat{\varphi}(s)x^sD(s)ds}.

Now we are free to use any technique of complex analysis to try to estimate this integral. The usual idea is to move the line of integration (using Cauchy’s theorem) as far to the left as possible (since the modulus of xs diminishes when the real part of s does). For this, clearly, one needs to know how far D(s) can be analytically continued, but it is equally important to have some control of the integrand high along vertical lines to justify the change of line of integration, and this depends on the decay properties of the Mellin transform at infinity, which amount exactly to the regularity properties of φ itself. If φ was the characteristic function of the interval [1,x], then the Mellin transform only decays like 1/s for s high in a vertical strip, and multiplying this with any functions D(s) which does not tend to 0 leads, at best, to conditionally convergent integrals. On the other hand, if φ is compactly supported on [0,+∞[, it is very easy to check that the Mellin transform is not only holomorphic when the real part of s is positive (which allows changing the contour of integration that far), but also it decaysfaster than any polynomial in vertical strips, so that any D(s) which has at most polynomial growth in vertical strips can be involved in this manipulation.

(A prototype of this, though it doesn’t involve a compactly supported function, is φ(x)=e-x, for which we have


the Gamma function, which decays exponentially fast in vertical strips by the complex Stirling formula: we have

|\Gamma(\sigma+it)|\leq C|t|^{\sigma-1/2}\exp(-\pi|t|/2)

for some constant C, uniformly for σ in any bounded interval and |t|>1).

Finally, even if an asymptotic formula for A(x) is desired, it may be much easier to prove them by using upper and lower bounds

A_1(x)\leq A(x)\leq A_2(x)


A_i(x)=\sum_{n\geq 1}{a(n)\varphi_i(n)}

and the smoothing functions, this time, have graphs as described below

Two smoothing functions

with a parameter y<x which is left free to optimize later on. In a concrete case, one may (for instance) prove — using the smoothness of the sums — that

A_1(x)=x-y+O(x^{2/3}),\ \ \ \ \ A_2(x)=x+y+O(x^{2/3})

and then the choice of y=x2/3 and the encadrement above imply that


Example 4. (The Prime Number Theorem) The first proofs of the Prime Number Theorem

|\{p\leq x\ |\ p\text{ is prime}\}|\sim \frac{x}{\log x}

as x goes to infinity, due (independently) to J. Hadamard and C.J. de la Vallée Poussin in 1896, were implementations of the previous example, either with a(n) the characteristic function of the primes, or

a(p)=\log p\ \ \ \text{if } p \text{ is prime},\ \ \ \ a(n)=0\text{ otherwise}

(which leads to slightly simpler formulas, as already discovered by Chebychev when working with elementary methods). Both proofs used smoothing in a somewhat implicit form. Hadamard’s proof, for instance, amounted roughly to considering

B(x)=\sum_{n\leq x}{a(n)\log (x/n)}

(where the smoothing is present because the function which is inserted is zero at the end of the summation), and rewriting it as


where the point is that the smoothing in B(x) is the reason for the appearance of the factor s-2, which itself allows the integral to converge absolutely, even after shifting the integration to a contour close to but to the left of the line where the real part of s is 1. Hadamard adds (see the last page) a remark that, in so doing, he avoids the well-deserved criticisms levelled by de la Vallée Poussin against those arguments which, being based on A(x) itself, involve an integral with merely a factor s-1, which do not converge absolutely.

The last step of the proof is then an easy argument, using the fact that A(x) is increasing, that shows that the asymptotic formula for B(x) implies the expected asymptotic formula for A(x), i.e., the Prime Number Theorem.

There is nothing special about suing this particular form of smoothing; almost any kind would work here.

Note. It is of course the case that the proof of the Prime Number Theorem involves much more than this trick of smoothing! However, it is clearly the case that trying to dispense with it — as can be done — means spending a lot of energy dealing with purely analytic issues of convergence and exchange of sums and integrals, which become completely obvious after smoothing.

Example 5. (The analytic large-sieve inequality) Sometimes smoothing can be used efficiently for estimating sums involving oscillating terms (like exponential sums), although no direct comparison holds as it does in Example 3. As an example, consider the dual analytic large sieve inequality

\sum_{n=1}^N{\left|\sum_{r=1}^R{a(r)\exp(2i\pi n\xi_r)}\right|^2}\leq \Delta \sum_{r=1}^N{|a(r)|^2}

which can be proved to hold with


for all N, alll complex coefficients a(r), provided the real numbers ξr are δ-spaced modulo 1, i.e., we have

\min_{m\in\mathbf{Z}}{|m+\xi_r-\xi_s|}\geq \delta

if r is distinct from s.

To prove this inequality one is tempted to open the square on the left-hand side, and bring sum over n inside to obtain



W(r,s)=\sum_{n=1}^N{\exp(2i\pi (\xi_r-\xi_s)n)}.

This is a geometric sum, and is easily summed, but it is not so easy to get a good grip on it afterwards in summing over r and s. Although Montgomery and Vaughan succeeded, it is instructive to note that a much quicker argument — though with a weaker result — can be obtained by smoothing W(r,s). However, after opening the square, it is not clear how to insert a smoothing factor without (maybe) changing the sums completely — since the coefficients a(r) are arbitrary. So one must insert the smoothing beforehand: the left-hand side of the inequality is non-negative and can be bounded by

\sum_{n=1}^N{\left|\sum_{r=1}^R{a(r)\exp(2i\pi n\xi_r)}\right|^2}\varphi(n/N)

for any smooth function φ which is equal to 1 on [1,N]. This leads to modified sums

W_{\varphi}(r,s)=\sum_{n=1}^N{\exp(2i\pi (\xi_r-\xi_s)n)\varphi(n/N)}

which can be evaluated and transformed, for instance, by Poisson’s formula to exploit the rapid decay of the Fourier transform of φ.

Example 6. (The “approximate functional equation”) This next example shows how to use smoothing to represent an analytic function given by a Dirichlet series outside of the region of absolute convergence, so that it can be analyzed further. As an example, the series

L(s)=1-\frac{1}{3^s}+\frac{1}{5^s}-\cdots=\sum_{n\geq 1}{\frac{(-1)^{n+1}}{(2n-1)^s}}

converges absolutely only when the real part of s is larger than 1 (it converges conditionally when the real part is positive). It is known to have analytic continuation to an entire function, but in order to investigate its properties in the critical strip, say when the real part of s is 1/2, it is necessary to find some convenient alternative expression valid in this region. [Note that what is explained below applies also to the Riemann zeta function, with a small complication in presentation due to the presence of a pole at s=1; this is why we describe the case of L(s).]

So assume s has real part between 0 and 1. A common procedure is to pick a non-negative smooth function φ on [0,+∞[, with compact support or rapide decay, equal to one on a neighborhood of 0, and with


and then consider, for a parameter X>1, the smoothed sum

S(X)=\sum_{n\geq 1}{\frac{(-1)^{n-1}}{(2n-1)^s}\varphi(n/X)}

which makes sense for all s if φ decays fast enough. The role of X is, intuitively, as the effective length of the sum.

Using Mellin inversion as in Example 3, we get


Now shift the vertical line of integration to the left; because L(s) has at most polynomial growth vertical strips (a non-obvious fact, but one that can be proved without requiring much specific information because of the general Phragmen-Lindelöf principle) and because the Mellin transform of the smooth function φ decays faster than any polynomial, this shift is easy to justify.

Now where do we get poles while performing this shift? The recipe for φ (specifically, that it equals 1 close to 0 and has integral 1) easily imply that φ(w) has a simple pole at w=0 with residue 1, and this leads to a contribution equal to


so that


where the remainder R(X), if we shifted the intregration to the line with real part c is


This can be estimated fairly easily if c is very negative, but one can also use the functional equation of L(s) to express R(X) as a sum very similar to S(X), except that s is replaced with 1-s, and the new sum has effective length roughly equal to (|t|+1)/X, where t is the imaginary part of s (the smoothing function is also different). Thus one gets, roughly, an expression for L(s) as the sum of two sums of lengths, the products of which is equal to |t|+1. This is a very general fact in the study of L-functions and of the basic tools for the study of their analytic properties.

Example 7. (The “explicit formula” of prime number theory) In his paper on the Riemann zeta function, Riemann stated that the function π(x) which counts primes up to x is given by

\pi(x)=\mathrm{Li}(x)+\sum_{\rho}{\mathrm{Li}(x^{\rho})}+\text{(other elementary terms)}

where the sum runs over zeros of ζ(s). This “explicit formula” and similar ones can be very useful to understand many aspects of the distribution of primes, but this particular one is hard to justify for convergence reasons. However, provided one looks at a smoothed sum

\sum_{p}{(\log p)\varphi(p/x)}

(which has effective length x), it is a simple exercise in contour integration to obtain an explicit formula which converges very well, involving a sum over the zeros of the zeta function weighted with the Mellin (or Laplace, or Fourier) transform of φ.

Generalizations of it to primes in arithmetic progressions or Dirichlet L-functions are very efficient, for instance, in finding very strong estimates — assuming the Generalized Riemann Hypothesis — for the smallest prime in an arithmetic progression, or similar problems.


References: The technique of smoothing is not usually discussed in detail in textbooks. One exception is in my introductory book on analytic number theory (in French, so smooth sums are called sommes lisses or sommes lissées): see Section 2.3 in particular where the technique is introduced; it is then used throughout the book quite systematically even when — sometimes — it could be dispensed with.

For details of Examples 4, 5, 6, 7, 8, one can look in my book with Henryk Iwaniec (see Chapters 5 and 7 in particular), although the technique is used there without particular comments.

Written by Kowalski

September 7th, 2008 at 4:18 pm

Posted in Mathematics

9 Responses to 'A tricks-wiki article: smooth sums'

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  1. Very nice! I sometimes joke that one of the most important innovations in 20th century analysis was the bump function… it’s hard to see how we could have made nearly as much progress if we were forced to use non-smooth cutoffs. (The modern formulation of Example 1 in harmonic analysis, by the way, is the theory of Littlewood-Paley multipliers, which are particularly indispensable in the analysis of PDE and in associated function spaces, such as Sobolev spaces.)

    Weyl’s bound for the Gauss circle problem, by the way, would be another excellent illustration of this method. The Poisson summation formula is almost useless until one first applies a smooth cutoff.

    I have seen a non-smooth cutoff referred to in French as “une truncature brutale”, which I think is a wonderful term that really brings home the advantage of smoothing. :)

    Terence Tao

    7 Sep 08 at 19:13     Reply

  2. Ah, I guess the Gauss circle problem is secretly a special case of Example 3. But it is worth noting independently (for instance, Weyl’s method also works for non-arithmetic situations, in which the circle is replaced by some other curved body).

    Terence Tao

    7 Sep 08 at 19:15     Reply

  3. Thanks!

    I almost started discussing the circle problem in more detail in Example 3 (it was the concrete situation I had in mind) but I feared I was getting too technical.

    I should also probably mention in Example 6 the statements that are called Approximate functional equation in Titchmarsh’s book, where sharp cutoffs are used, so that the reader can compare the amount of work with/without smoothing. It’s particularly striking since this example is very clearly supposed to be a step into further work, not an end in itself.

    I unfortunately don’t know much (i.e., not as much as I would like) about the more abstract aspects of harmonic analysis that you mention, so the first two examples are very basic for this purpose, but one could add deeper remarks as you suggest to connect with “modern” methods).

    (About bump functions, I’ve heard a story — all the details may be wrong, but the spirit should be right — that once Selberg was giving an advanced course somewhere, and some people found it strange that he started by constructing suitable bump functions in all details, but he — supposedly — said that so much depended on this that it had to be done).


    7 Sep 08 at 19:20     Reply

  4. Hi!

    I found this article useful! Thanks for it, and I hope the Tricki finally gets it when the moment arrives! :)

    I suggested the addition of a multitag for every trick, in order to give the Tricki a powerful searching engine. I’ll repeat it here below and I’ll apply it to your article, just in case you find it appropiate:

    –(Proposed) Tricki Taggin’ Method–

    WHERE (in which branchs of math)
    IN (in which type of structures)
    FROM (hypothesis and elements you need to have for the trick to make sense; i.e. the trick starting point)
    TO (the generic result you accomplish by applying the trick)
    WITH (”the how”, technical terms that describe key parts of the process)
    NAME (informal name)
    CATEGORY (formal name),
    SN (serial number)
    AUTHOR (the creator or best-known user of the trick, if it’s known)
    REFERENCE (well-kown book, paper or source where it’s used in an important manner)
    USES (well-known uses of this trick, theorems where it is important, etc)
    CONTRIBUTED BY (the creator of the article)

    –Application to Kowalski’s “Smooth sums”–

    SN xxxxxxx
    NAME Smoothing Sums Trick
    CATEGORY Sums Convergence Weighting
    WHERE Harmonic Analysis, Number Theory
    IN Series
    FROM Complex or Real Finite Sum, Arithmetic Function
    TO Bound, Sum
    WITH Product by smooth weights, extension to complete series
    AUTHOR Riemann???
    REFERENCE <>???
    USES Prime Number Theorem, Fourier Series Expansion, Fourier Inversion Formula
    CONTRIBUTED BY E. Kowalski


    I hope you like it, the rough idea at the very least! :D If you think of any additions or modifications, feel free to make them and promote them!

    PS I also suggested to open a forum about the Tricki, but no response yet from anyone. I think that starting to compose articles (even sampling ones!) without having a well-thought base for the Wiki is somewhat riskée.

    We should have a subforum in the forum for articles, of course, but we should have many others as well: ·Definition of tricks and related subjects,
    ·Classification and identification of tricks,
    ·Format of the Wiki,
    ·Access (read and write) to the Wiki,
    ·Coding and programming of the Wiki,
    ·Hierarchy and powers of the members (if any),
    ·Right Licenses for the articles (all of them Creative Commons, the contributor decides…?),
    ·Distribution of the work,
    ·etc etc

    What are your thoughts about this? Feel free to write me if you like, to ambroxius AT terra DOT es!

    Regards, Jose Brox

    Jose Brox

    7 Sep 08 at 19:26     Reply

  5. Many thanks for writing this — it looks very nice. Incidentally, an important feature of the site will be that there will be opportunities to comment on individual articles. I hope people will use it as a chance to say if they find things hard to follow. I have a comment of a related but different kind: my article on Zorn’s lemma will I hope be one of a general category of “How to use X” articles. If anyone felt like writing an article entitled “How to use the Poisson summation formula” they would be doing me, and I expect others, a big service. You look as though you would in principle be able to write such an article …


    7 Sep 08 at 23:39     Reply

  6. Sure, I’d be happy to try to write something on the Poisson formula.


    8 Sep 08 at 5:12     Reply

  7. Emmanuel, I agree with you entirely. In fact sometimes I feel one can view smoothing as more than just a tool. For many applications, such as showing that every large odd number is the sum of three primes, or Waring’s problem on representing numbers as the sum of kth powers, one may as well count everything using an incredibly smooth weight, such as a gaussian. Of course one will not get *asymptotics* for the original problem this way, but often one does not care about this (or, some might argue, the more natural asymptotics are the ones with the gaussian weights). My experience in studying the Hardy-Littlewood method is that the traditional treatments involve a lot of ”we verify the following technical lemma” phrases – I think here of such things as the van der Corput bound linking exponential sums with integrals. If one has smooth weights, that particular bound is immediate from the Poisson summation formula.

    By the way Soundararajan once pointed out to me that if you use Gaussian weights the prime number theorem is known with an error term of (as I recall) x exp(-log x/log log x), as opposed to x exp(-(log x)^{1/2}) if you insist on counting on the unsmoothed interval [1,x].

    Ben Green

    8 Sep 08 at 20:37     Reply

  8. Apropos of Selberg’s smooth functions, I had occasion to use these myself recently. They are extremely smooth approximations to the interval [1,N], and in fact their smoothness is nothing short of remarkable in my opinion. You can take a nonnegative function f which is >= 1 on [1,N], whose Fourier transform is smooth and compactly supported on [-t,t], and whose integral is << N + 1/t. This is most powerful when t is about 1/N; of course by the uncertainty principle one cannot hope to do any better. Until my recent reading on this matter, I was sure that gaussians (which lose logs) would be the best functions to take here, but I was wrong.

    Ben Green

    8 Sep 08 at 20:43     Reply

  9. That’s a very nice observation of Soundararajan, which I didn’t know. I will probably add it to the text (do you know a reference?), together with the contrasting remark that smoothing “solves” the circle problem entirely (the difference being of course due to the fact that there are indeed zeros on the critical line, whereas ζ(s)2 has only a double pole at s=1).

    I also absolutely agree that often the “right” result is the one with smooth weights. It’s even sometimes painful to have to use an available result in the literature about non-smooth sums because there is no statement given with smooth cutoff, and yet using those would cut the length of the proof in two; if it’s a two-page proof, it’s just as well to redo the computation, but if it’s 10 pages or more, it’s really a problem…

    (Similarly, and I may write another article on this, I think many technically useful results on primes in arithmetic progressions or Chebotarev density problems are not phrased in the most useful way, which would be with estimates purely in terms of Dirichlet L-functions instead of the counting functions).

    I really like the Selberg functions also — in fact, my very first bachelor-type thesis was about them…


    8 Sep 08 at 20:58     Reply

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