MathLabs

Arithmetic and number theory

The circle method

An analytic technique using Fourier integrals over the unit circle to count solutions to additive equations.

IntuitionIntuition: counting by walking around a circle

Suppose you want to count the number of ways to write an integer nn as a sum of kk elements from a set AA of integers (for example, primes, or perfect kk-th powers). Encode AA by a "wave" running once around the unit circle: for each real number α\alpha in [0,1)[0,1), form the exponential sum FA(α)=∑a∈Ae(aα)F_A(\alpha) = \sum_{a \in A} e(a\alpha), where e(x):=e2πixe(x) := e^{2\pi i x}. As α\alpha sweeps from 00 to 11, FA(α)kF_A(\alpha)^k oscillates; near a handful of "resonant" points — rationals a/qa/q with small denominator qq — the terms of the sum line up and add constructively (a major arc), while almost everywhere else the phases point in essentially random directions and cancel out (a minor arc). The circle method turns this geometric picture into an exact formula and then estimates it arc by arc.

Interactive unit circle showing a point at angle theta used to build the exponential sum of the circle method.
Dragging θ\theta moves the point e2πiθ/360e^{2\pi i\theta/360} around the unit circle; the circle method integrates such points against FA(α)kF_A(\alpha)^k as θ/360=α\theta/360=\alpha runs over [0,1)[0,1).

UndergraduateDefinition: the generating function on the circle

Definition: Exponential sum and representation count

For a finite set A⊂Z≥0A\subset\mathbb Z_{\ge0} write FA(α)=∑a∈Ae(aα)F_A(\alpha) = \sum_{a \in A} e(a\alpha) with e(x):=e2πixe(x) := e^{2\pi i x}. For n≥0n\ge0 and k≥1k\ge1, let rk(n)=#{(a1,…,ak)∈Ak:a1+⋯+ak=n}r_k(n)=\#\{(a_1,\dots,a_k)\in A^k : a_1+\cdots+a_k=n\} be the number of ordered representations of nn as a sum of kk elements of AA.

FA(α)=∑a∈Ae(aα),e(x):=e2πixF_A(\alpha) = \sum_{a \in A} e(a\alpha), \qquad e(x) := e^{2\pi i x}

Raising FAF_A to the kk-th power and expanding, the coefficient of e(nα)e(n\alpha) "should" be exactly rk(n)r_k(n); the extraction identity below (Theorem 1) makes this precise: rk(n)=∫01FA(α)k e(−nα) dαr_k(n) = \int_0^1 F_A(\alpha)^k\, e(-n\alpha)\, d\alpha. Here FA(α)kF_A(\alpha)^k is the wave from the picture above, and the integral picks out the one frequency, nn, that we care about — this is the entire content of the method: replace a counting problem by an integral estimation problem.

rk(n)=∫01FA(α)k e(−nα) dαr_k(n) = \int_0^1 F_A(\alpha)^k\, e(-n\alpha)\, d\alpha
Major arcs versus minor arcs
FeatureMajor arc (near a/qa/q, qq small)Minor arc (elsewhere)
Where it sitsA short interval around each rational a/qa/q with q≤Qq\le QWhat is left of [0,1)[0,1) after removing all major arcs
Size of FA(α)F_A(\alpha)Close to its trivial maximum ∣A∣|A|: terms reinforceExpected to be much smaller than ∣A∣|A|: terms cancel
Role in the estimateProduces the main term (a "singular series")Must be bounded above and absorbed into the error term

UndergraduateTwo foundational theorems

For every finite A⊂Z≥0A\subset\mathbb Z_{\ge0}, integer k≥1k\ge1 and n≥0n\ge0, rk(n)=∫01FA(α)k e(−nα) dαr_k(n) = \int_0^1 F_A(\alpha)^k\, e(-n\alpha)\, d\alpha, where rk(n)r_k(n) is the number of ordered kk-tuples from AA summing to nn.

Why is it true?

This is what lets us replace a purely combinatorial counting problem by a question about the size of an analytic integral: if we can show the integral is positive, a representation must exist.

Proof

First note the orthogonality relation ∫01e(mα) dα\int_0^1 e(m\alpha)\, d\alpha=1=1 if m=0m=0, and =0=0 if m≠0m\neq 0: writing e(mα)=cos⁡(2πmα)+isin⁡(2πmα)e(m\alpha)=\cos(2\pi m\alpha)+i\sin(2\pi m\alpha), for m≠0m\neq0 this is a full number of periods of a sine/cosine wave over [0,1)[0,1), which integrates to 00; for m=0m=0 the integrand is the constant 11.

Now expand the kk-th power of the generating function: FA(α)k=(∑a∈Ae(aα))k=∑(a1,…,ak)∈Ake((a1+⋯+ak)α)F_A(\alpha)^k=\Big(\sum_{a\in A}e(a\alpha)\Big)^k=\sum_{(a_1,\dots,a_k)\in A^k} e\big((a_1+\cdots+a_k)\alpha\big), a finite sum over all ordered kk-tuples from AA, grouped by the exponent m=a1+⋯+akm=a_1+\cdots+a_k.

Multiply both sides by e(−nα)e(-n\alpha) and integrate term by term over [0,1)[0,1) (legitimate because the sum is finite, so integration and summation commute): ∫01FA(α)ke(−nα) dα=∑(a1,…,ak)∈Ak∫01e((a1+⋯+ak−n)α) dα\int_0^1 F_A(\alpha)^k e(-n\alpha)\,d\alpha=\sum_{(a_1,\dots,a_k)\in A^k}\int_0^1 e\big((a_1+\cdots+a_k-n)\alpha\big)\,d\alpha.

By the orthogonality relation, each summand on the right is 11 exactly when a1+⋯+ak=na_1+\cdots+a_k=n and 00 otherwise. So the whole sum collapses to exactly the count of tuples with a1+⋯+ak=na_1+\cdots+a_k=n, i.e. rk(n)r_k(n). This proves the identity exactly, with no approximation involved: it is an identity, not an asymptotic.

For every real α\alpha and every integer Q≥1Q\ge1, there is a rational a/qa/q with 1≤q≤Q1\le q\le Q, gcd⁡(a,q)=1\gcd(a,q)=1, such that ∣α−aq∣≤1q(Q+1)\left|\alpha - \dfrac{a}{q}\right| \le \dfrac{1}{q(Q+1)}.

Why is it true?

This guarantees that every point on the circle sits close to some low-denominator rational, which is exactly what lets us partition [0,1)[0,1) into major arcs — short intervals around the good approximations a/qa/q with qq small, where FAF_A is large — and minor arcs, the leftover set.

Proof

Consider the Q+2Q+2 numbers 0,{α},{2α},…,{Qα},10,\{\alpha\},\{2\alpha\},\dots,\{Q\alpha\},1, where {x}\{x\} denotes the fractional part of xx; all lie in [0,1][0,1]. Partition [0,1][0,1] into Q+1Q+1 equal subintervals of length 1/(Q+1)1/(Q+1): [0,1Q+1),[1Q+1,2Q+1),…[0,\tfrac1{Q+1}), [\tfrac1{Q+1},\tfrac2{Q+1}),\dots.

We have Q+2Q+2 numbers and only Q+1Q+1 subintervals, so by the pigeonhole principle two of the numbers {jα}\{j\alpha\} and {iα}\{i\alpha\} (with 0≤i<j≤Q0\le i<j\le Q, allowing i=0i=0 so {iα}=0\{i\alpha\}=0) land in the same subinterval, hence differ by less than 1/(Q+1)1/(Q+1): ∣{jα}−{iα}∣<1Q+1|\{j\alpha\}-\{i\alpha\}|<\tfrac{1}{Q+1}.

Set q=j−iq=j-i, so 1≤q≤Q1\le q\le Q. Since {jα}−{iα}=(jα−iα)−(⌊jα⌋−⌊iα⌋)=qα−a\{j\alpha\}-\{i\alpha\} = (j\alpha - i\alpha) - (\lfloor j\alpha\rfloor - \lfloor i\alpha\rfloor) = q\alpha - a for the integer a=⌊jα⌋−⌊iα⌋a=\lfloor j\alpha\rfloor-\lfloor i\alpha\rfloor, we get ∣qα−a∣<1Q+1|q\alpha - a| < \tfrac{1}{Q+1}, i.e. ∣α−aq∣<1q(Q+1)\left|\alpha-\dfrac aq\right| < \dfrac{1}{q(Q+1)}.

Finally, if gcd⁡(a,q)=d>1\gcd(a,q)=d>1, divide both aa and qq by dd: the resulting fraction has an even smaller denominator and the same (or a smaller) distance to α\alpha, so we may take gcd⁡(a,q)=1\gcd(a,q)=1 without loss of generality. This completes the proof; it is a finite, constructive pigeonhole argument, with no appeal to any unproved estimate.

UndergraduateReal-World Applications and Worked Examples

Beyond number theory, the same "add up waves and look for resonance" idea is the working principle of the discrete Fourier transform used throughout signal processing and electrical engineering, and the asymptotic p(n)∼14n3exp⁡ ⁣(π2n3)p(n) \sim \dfrac{1}{4n\sqrt3}\exp\!\left(\pi\sqrt{\dfrac{2n}{3}}\right) that Hardy and Ramanujan extracted with an early version of the circle method (applied to the partition function p(n)p(n)) is used in statistical mechanics to estimate the number of microstates — hence the entropy — of a system of indistinguishable bosonic excitations at fixed total energy nn.

Example: A miniature circle method computation

Let A={1,2,…,8}A=\{1,2,\dots,8\}. Using the extraction identity, compute r2(9)r_2(9), the number of ordered pairs (a,b)∈A2(a,b)\in A^2 with a+b=9a+b=9.

Solution

By Theorem 1, r2(9)=∫01FA(α)2e(−9α) dαr_2(9)=\int_0^1 F_A(\alpha)^2 e(-9\alpha)\,d\alpha with FA(α)=∑a=18e(aα)F_A(\alpha)=\sum_{a=1}^{8}e(a\alpha); there is no need to actually evaluate the integral analytically — the identity guarantees it equals the direct count, so we may compute the count combinatorially instead and trust the identity.

List every ordered pair (a,b)(a,b) with a,b∈{1,…,8}a,b\in\{1,\dots,8\} and a+b=9a+b=9: (1,8),(2,7),(3,6),(4,5),(5,4),(6,3),(7,2),(8,1)(1,8),(2,7),(3,6),(4,5),(5,4),(6,3),(7,2),(8,1). Each first coordinate from 11 to 88 determines b=9−ab=9-a uniquely, and bb always lands back in {1,…,8}\{1,\dots,8\} (since 1≤a≤8⇒1≤9−a≤81\le a\le8 \Rightarrow 1\le 9-a\le8), so all 88 values of aa work.

Hence r2(9)r_2(9) equals 88. This tiny example is exactly the identity of Theorem 1 in action: the analytic integral and the combinatorial count are, by construction, one and the same number — the real content of the method appears only once AA becomes an infinite or growing set (like the primes) and one must estimate rather than enumerate.

Example: Best rational approximation via Dirichlet's theorem

Apply Dirichlet's approximation theorem with α=2\alpha=\sqrt2 and Q=5Q=5 to exhibit a fraction a/qa/q, 1≤q≤51\le q\le5, satisfying the bound of Theorem 2, and check it numerically.

Solution

We want a/qa/q with 1≤q≤51\le q\le5 and ∣2−a/q∣<1/(q⋅6)|\sqrt2-a/q|<1/(q\cdot6) (taking Q+1=6Q+1=6 in the theorem's bound). Try q=5q=5: the nearest integer to 52≈7.07115\sqrt2\approx7.0711 is a=7a=7, giving 7/5=1.47/5=1.4.

Check the bound: ∣2−7/5∣=∣1.41421…−1.4∣≈0.01421|\sqrt2-7/5|=|1.41421\ldots-1.4|\approx0.01421, while the theorem promises 1/(5⋅6)=1/30≈0.03331/(5\cdot6)=1/30\approx0.0333; indeed 0.01421<0.03330.01421<0.0333, so the bound holds comfortably, exactly as guaranteed.

This 7/57/5 is in fact the convergent of the continued fraction of 2=[1;2,2,2,… ]\sqrt2=[1;2,2,2,\dots] obtained after two steps, which is why it approximates so well — Dirichlet's pigeonhole proof does not need continued fractions to work, but it always lands on comparably good approximants. So 75\dfrac{7}{5} is a valid witness.

In the extraction identity rk(n)=∫01FA(α)k e(−nα) dαr_k(n) = \int_0^1 F_A(\alpha)^k\, e(-n\alpha)\, d\alpha, why does the integral equal exactly rk(n)r_k(n)?

For A={1,2,…,8}A=\{1,2,\dots,8\}, what is r2(9)r_2(9), the number of ordered pairs (a,b)∈A2(a,b)\in A^2 with a+b=9a+b=9?

According to Dirichlet's approximation theorem, for every real α\alpha and integer Q≥1Q\ge1, what is guaranteed to exist?

In statistical mechanics, what does the Hardy–Ramanujan asymptotic p(n)∼14n3exp⁡ ⁣(π2n3)p(n) \sim \dfrac{1}{4n\sqrt3}\exp\!\left(\pi\sqrt{\dfrac{2n}{3}}\right) for the partition function help estimate?

References

  1. G. H. Hardy, S. Ramanujan (1918). Asymptotic formulae in combinatory analysis · DOI:10.1112/plms/s2-17.1.75
  2. J. Bourgain, C. Demeter, L. Guth (2016). Proof of the main conjecture in Vinogradov's Mean Value Theorem for degrees higher than three · DOI:10.4007/annals.2016.184.2.7 · arXiv:1512.01565
  3. B. Green, T. Tao (2008). The primes contain arbitrarily long arithmetic progressions · DOI:10.4007/annals.2008.167.481 · arXiv:math/0404188