4 Hypercontractivity

We begin with an important lemma of Bonami.

Lemma 4.1 (Bonami’s Lemma). Let f:{−1,1}n→ℝ be a function of degree ≤k. Then ∥f∥4≤3k∕2∥f∥2.

This statement is natural: to have ∥f∥4 much larger than ∥f∥2, f must be ‘spiky’, because if it is roughly constant, then these norms are roughly the same; but if f is ‘spiky’, then we expect it to not be low degree polynomial.

Example. Consider f(0)=1, f(x)=0 otherwise. ∥f∥2=2−n∕2, ∥f∥4=2−n∕4. So ∥f∥4 is much larger than ∥f∥2 in this case, and indeed f is not a low degree polynomial.

Proof. Induction on n. n=1 is easy. Let f:{−1,1}n→ℝ. Then f=g+xnh where g(x)=Enf=12(f(xn→1)+f(xn→−1)) and h(x)=Dnx=12(f(xn→1)−f(xn→−1)). Note also that g and xnh are orthogonal.

Recall that

xA={0n∉AxA∖{n}n∈A

Therefore, Dnf has degree at most k−1. First,

∥f∥22=∥g∥22+∥xnh∥22=∥g∥22+∥h∥22.

Also,

∥f∥44=𝔼x(g(x)+xnh(x))4=𝔼x(g(x)4+4xng(x)3h(x)+6xn2g(x)2h(x)2+4xn3g(x)h(x)3+xn4h(x)4)=∥g∥44+6𝔼xg(x)2h(x)2+∥h∥44

The last line uses the fact that g, h don’t depend on xn and that xn=±1 with probability 12. Then

∥f∥44=∥g∥44+6𝔼xg(x)2h(x)2+∥h∥44≤∥g∥44+6∥g∥42∥h∥42+∥h∥44(by Cauchy-Schwarz)≤32k∥g∥24+6⋅3k∥g∥223k−1∥h∥22+32(k−1)∥h∥24(by inductive hypothesis)≤32k(∥g∥24+2∥g∥22∥h∥22+19∥h∥24)≤32k(∥g∥22+∥h∥22)2≤32k∥f∥24□

Corollary 4.2 (Anticoncentration of low-degree functions). Let f:{−1,1}n→ℝ have degree ≤k and let 𝔼xf(x)=μ and Var⁡f=σ2. Then

ℙ[|f(x)−μ|>σ2]≥916⋅3−2k.

Proof. Let g=f−μ. Then 𝔼g=0 and ∥g∥22=σ2. Let p=ℙ[|g(x)|>σ2]. Let A={x:|g(x)|≤σ2}, B=Ac. Then

σ2=∥g∥22≤(1−p)σ24+p𝔼x∈Bg(x)2.

Therefore,

p𝔼x∈Bg(x)2≥34σ2,

so

𝔼x∈Bg(x)2≥3σ24p.

By Cauchy-Schwarz (or Var⁡(g(x)2)≥0) we have

𝔼x∈Bg(x)4≥9σ416p2,

so ∥g∥44≥9σ416p. But Bonami’s Lemma implies that ∥g∥44≤32k∥g∥24=32kσ4. Therefore, p≥916⋅3−2k, as stated. □

In this lecture, we are aiming towards the earlier stated stability version of Arrow’s Theorem.

Lemma 4.3. Let g:{−1,1}n→ℝ be given by the formula g(x)=∑⁡i=1naixi. Then

Var⁡g2=2∑i≠jai2aj2.

Remark. Why should one care about studying Var⁡(g(x)2)? Note that if g is a Boolean function, then g(x)2≡1, so Var⁡(g(x)2). So Var⁡(g(x)2) can be used to measure “how close” a function is to being Boolean (or a multiple of a Boolean function).

Proof. 𝔼g2=∑⁡iai2 by Parseval, so

(𝔼g2)2=∑iai4+∑i≠jai2aj2𝔼g4=∑i,j,k,laiajakal𝔼xxixjxkxl=∑iai4+3∑i≠jai2aj2

Note that 𝔼xxixjxkxl vanishes if any index appears an odd number of times, which is how we deduce the last equality above.

Subtracting the two above equations gives the result. □

Lemma 4.4. Let g:{−1,1}n→ℝ, 1−δ≤∥g∥22≤1, Var⁡g2≤Cδ, g linear with no constant term. Then there exists i such that g^(i)2≥1−(2+C2)δ.

Proof. Let g(x)=∑⁡iaixi. Then 1−δ≤∑⁡iai2≤1, 2∑⁡i≠jai2aj2≤Cδ. Therefore,

∑iai4=(∑iai2)2−∑i≠jai2aj2≥1−2δ−C2δ=1−(2+C2)δ.

Also,

∑iai4≤max⁡iai2(∑iai2)≤max⁡ai2=max⁡g^(i)2.□

Theorem 4.5 (Friedgut, Kalai, Naor). Let f:{−1,1}n→{−1,1} be a function with W1(f)≥1−δ. Then there exists i such that f^(i)2≥1−O(δ).

Proof. Let G be the degree-1 part of f, i.e. g=∑⁡if^(i)xi. Then by hypothesis, 1−δ≤∥g∥22, and by Parseval ∥g∥22≤∥g∥22=1. Let σ=Var⁡g2. Note that g2 has degree at most 2. By the Anticoncentration of low-degree functions,

ℙ[|g2−∥g∥22|>σ2]≥916⋅181=19×16.

Since ∥g∥22∈[1−δ,1], it follows that

ℙ[|g2−1|>σ2−δ]≥19×16.

Also, 1=f2. Therefore,

𝔼x|g(x)2−f(x)2|≥19×16⋅(σ2−δ).

But

𝔼x|g(x)2−f(x)2|=𝔼x|g(x)+f(x)||g(x)−f(x)|≤∥f+g∥2∥f−g∥2≤2δ

by Cauchy-Schwarz. Therefore, σ2−δ≤32×9δ, so σ≤64×9δ+2δ≤600δ, so σ2≤360000δ. By Lemma 4.4, there exists i such that g^(i)2≥1−180002δ=1−O(δ). But g^(i)2=f^(i)2, so we are done. □

Let p=ℙ[f(x)=xi]. Then f^(i)=𝔼f(x)xi=p−(1−p)=2p−1. So if f^(i)=1−O(δ), then p=O(δ) or 1−p=O(δ). So f is well-approximated by xi or by −xi.

This completes the proof of the stability version of Arrow’s Theorem.

Next time: ∥T13f∥4≤∥f∥2.

Notation. Given f:{−1,1}n→ℝ, write f(=k) for the degree-k part of f, i.e. ∑⁡|A|=kf^(A)xA.

Theorem 4.6. Let ρ=13. Then ∥Tρf∥4≤∥f∥2 for every f:{−1,1}n→ℝ.

Proof.

∥Tρf∥4=∥∑k=0nTrhof(=k)∥4=∥∑k=0nρkf(=k)∥4≤∑k=0nρk∥f(=k)∥4≤∑k=0nρk⋅3k∕2∥f(=k)∥2(by Bonami’s Lemma)=∑k=0n∥f(=k)∥2≤n(∑k=0n∥f(=k)∥22)12=n∥f∥2(since the f(=k) are orthogonal)

To get rid of the n we use the tensor-power trick.

Write f⊗m:({−1,1}n)m→ℝ for the function

f⊗m(x1,…,xm)=f(x1)⋯f(xm).

It is easy to check that ∥Tρf⊗m∥4=∥Tρf∥4m and ∥f⊗m∥2=∥f∥2m. Therefore,

∥Tρf∥4m=∥Tρf⊗m∥4≤mn∥f⊗m∥2=mn∥f∥2m,

where the inequality is what we proved earlier.

So ∥Tρf∥4≤(mn)12m∥f∥2 for every m. Letting m→∞, we deduce that ∥Tρf∥4≤∥f∥2. □

Remark. The tensor trick here would have worked even if n was replaced by any subexponential function.

Corollary 4.7. Let ρ=13 and let f:{−1,1}n→ℝ. Then ∥Tρf∥2≤∥f∥4∕3.

Proof. Note that Tρ is self adjoint (see the example sheet, or note that it easily follows from Tρf^(A)=ρ|A|f^(A)). So

∥Tρf∥2=max⁡∥g∥2=1⟨Tρf,g⟩=max⁡∥g∥2=1⟨f,Tρg⟩(self-adjoint)≤max⁡∥h∥4=1⟨f,h⟩(by Theorem 4.6)=∥f∥4∕3(since L4∕3 is the dual of L4)□

Remark. On the last = in the above proof, we could instead just say ≤ by Hölder.

Remark. ∥Tρf∥22=⟨Tρf,Tρf⟩=⟨Tρ2f,f⟩=⟨T13f,f⟩=Stab⁡13f.

So an equivalent formulation of Theorem 4.6 is that Stab⁡13f≤∥f∥432.