The Bernstein and Nikolsky inequalities for trigonometric polynomials

Jordan Bell
January 28, 2015

1 Introduction

Let 𝕋=ℝ/2⁢π⁢ℤ. For a function f:𝕋→ℂ and τ∈𝕋, we define fτ:𝕋→ℂ by fτ⁢(t)=f⁢(t-τ). For measurable f:𝕋→ℂ and 0<r<∞, write

∥f∥r=(12⁢π⁢∫𝕋|f⁢(t)|r⁢𝑑t)1/r.

For f,g∈L1⁢(𝕋), write

(f*g)⁢(x)=12⁢π⁢∫𝕋f⁢(t)⁢g⁢(x-t)⁢𝑑t,x∈𝕋,

and for f∈L1⁢(𝕋), write

f^⁢(k)=12⁢π⁢∫𝕋f⁢(t)⁢e-i⁢k⁢t⁢𝑑t,k∈ℤ.

This note works out proofs of some inequalities involving the support of f^ for f∈L1⁢(𝕋).

Let 𝒯n be the set of trigonometric polynomials of degree ≤n. We define the Dirichlet kernel Dn:𝕋→ℂ by

Dn⁢(t)=∑|j|≤nei⁢j⁢t,t∈𝕋.

It is straightforward to check that if T∈𝒯n then

Dn*T=T.

2 Bernstein’s inequality for trigonometric polynomials

DeVore and Lorentz attribute the following inequality to Szegö.11 1 Ronald A. DeVore and George G. Lorentz, Constructive Approximation, p. 97, Theorem 1.1.

Theorem 1.

If T∈𝒯n and T is real valued, then for all x∈𝕋,

T′⁢(x)2+n2⁢T⁢(x)2≤n2⁢∥T∥∞2.
Proof.

If T=0 the result is immediate. Otherwise, take x∈𝕋, and for real c>1 define

Pc⁢(t)=T⁢(t+x)⁢sgn⁢T′⁢(x)c⁢∥T∥∞,t∈𝕋.

Pc∈𝒯n, and satisfies

Pc′⁢(0)=T′⁢(x)⁢sgn⁢T′⁢(x)c⁢∥T∥∞≥0

and ∥Pc∥∞≤1c<1. Since ∥Pc∥∞<1, in particular |Pc⁢(0)|<1 and so there is some α, |α|<π2⁢n, such that sin⁡n⁢α=Pc⁢(0). We define S∈𝒯n by

S⁢(t)=sin⁡n⁢(t+α)-Pc⁢(t),t∈𝕋,

which satisfies S⁢(0)=sin⁡n⁢α-Pc⁢(0)=0. For k=-n,…,n, let tk=-α+(2⁢k-1)⁢π2⁢n, for which we have

sin⁡n⁢(tk+α)=sin⁡(2⁢k-1)⁢π2=(-1)k+1.

Because ∥Pc∥∞<1,

sgn⁢S⁢(tk)=(-1)k+1,

so by the intermediate value theorem, for each k=-n,…,n-1 there is some ck∈(tk,tk+1) such that S⁢(ck)=0. Because

tn-t-n=(2⁢n-1)⁢π2⁢n-(-2⁢n-1)⁢π2⁢n=2⁢π,

it follows that if j≠k then cj and ck are distinct in 𝕋. It is a fact that a trigonometric polynomial of degree n has ≤2⁢n distinct roots in 𝕋, so if t∈(tk,tk+1) and S⁢(t)=0, then t=ck. It is the case that t1=-α+π2⁢n>0 and t0=-α-π2<0, so 0∈(t0,t1). But S⁢(0)=0, so c0=0. Using S⁢(t1)=1>0 and the fact that S has no zeros in (0,t1) we get a contradiction from S′⁢(0)<0, so S′⁢(0)≥0. This gives

0≤Pc′⁢(0)=n⁢cos⁡n⁢α-S′⁢(0)≤n⁢cos⁡n⁢α=n⁢1-sin2⁡n⁢α=n⁢1-Pc⁢(0)2.

Thus

Pc′⁢(0)≤n⁢1-Pc⁢(0)2,

or

n2⁢Pc⁢(0)+Pc′⁢(0)2≤n2.

Because

Pc⁢(0)2=T⁢(x)2c2⁢∥T∥∞2,Pc′⁢(0)2=T′⁢(x)2c2⁢∥T∥∞2

we get

n2⁢T⁢(x)2+T′⁢(x)2≤c2⁢n2⁢∥T∥∞2.

Because this is true for all c>1,

n2⁢T⁢(x)2+T′⁢(x)2≤n2⁢∥T∥∞2,

completing the proof. ∎

Using the above we now prove Bernstein’s inequality.22 2 Ronald A. DeVore and George G. Lorentz, Constructive Approximation, p. 98.

Theorem 2 (Bernstein’s inequality).

If T∈𝒯n, then

∥T′∥∞≤n⁢∥T∥∞.
Proof.

There is some x0∈𝕋 such that |T′⁢(x0)|=∥T′∥∞. Let α∈ℝ be such that ei⁢α⁢T′⁢(x0)=∥T′∥∞. Define S⁢(x)=Re⁢(ei⁢α⁢T⁢(x)) for x∈𝕋, which satisfies S′⁢(x)=Re⁢(ei⁢α⁢T′⁢(x)) and in particular

S′⁢(x0)=Re⁢(ei⁢α⁢T′⁢(x0))=ei⁢α⁢T′⁢(x0)=∥T′∥∞.

Because S∈𝒯n and S is real valued, Theorem 1 yields

S′⁢(x0)2+n2⁢S⁢(x0)2≤n2⁢∥S∥∞2.

A fortiori,

S′⁢(x0)2≤n2⁢∥S∥∞2,

giving, because S′⁢(x0)=∥T′∥∞ and ∥S∥∞≤∥T∥∞,

∥T′∥∞2≤n2⁢∥T∥∞2,

proving the claim. ∎

The following is a version of Bernstein’s inequality.33 3 Ronald A. DeVore and George G. Lorentz, Constructive Approximation, p. 101, Theorem 2.4.

Theorem 3.

If T∈𝒯n and A⊂𝕋 is a Borel set, there is some x0∈𝕋 such that

∫A|T′⁢(t)|⁢𝑑t≤n⁢∫A-x0|T⁢(t)|⁢𝑑t.
Proof.

Let A⊂𝕋 be a Borel set with indicator function χA. Define Q:𝕋→ℂ by

Q⁢(x)=∫𝕋χA⁢(t)⁢T⁢(t+x)⁢sgn⁢T′⁢(t)⁢𝑑t,x∈𝕋,

which we can write as

Q⁢(x) =∫𝕋χA⁢(t)⁢∑jT^⁢(j)⁢ei⁢j⁢(t+x)⁢sgn⁢T′⁢(t)⁢d⁢t
=∑jT^⁢(j)⁢(∫𝕋χA⁢(t)⁢ei⁢j⁢t⁢sgn⁢T′⁢(t)⁢𝑑t)⁢ei⁢j⁢x,

showing that Q∈𝒯n. Also,

Q′⁢(x)=∫𝕋χA⁢(t)⁢T′⁢(t+x)⁢sgn⁢T′⁢(t)⁢𝑑t,x∈𝕋.

Let x0∈𝕋 with |Q⁢(x0)|=∥Q∥∞. Applying Theorem 2 we get

∥Q′∥∞≤n⁢∥Q∥∞.

Using

Q′⁢(0)=∫𝕋χA⁢(t)⁢T′⁢(t)⁢sgn⁢T′⁢(t)⁢𝑑t=∫𝕋χA⁢(t)⁢|T′⁢(t)|⁢𝑑t,

this gives

∫𝕋χA⁢(t)⁢|T′⁢(t)|⁢𝑑t ≤n⁢∥Q∥∞
=n⁢|Q⁢(t0)|
=n⁢|∫𝕋χA⁢(t)⁢T⁢(t+x0)⁢sgn⁢T′⁢(t)⁢𝑑t|
≤n⁢∫𝕋χA⁢(t)⁢|T⁢(t+x0)|⁢𝑑t
=n⁢∫𝕋χA-x0⁢(t)⁢|T⁢(t)|⁢𝑑t.

∎

Applying the above with A=𝕋 gives the following version of Bernstein’s inequality, for the L1 norm.

Theorem 4 (L1 Bernstein’s inequality).

If T∈𝒯n, then

∥T′∥1≤n⁢∥T∥1.

3 Nikolsky’s inequality for trigonometric polynomials

DeVore and Lorentz attribute the following inequality to Sergey Nikolsky.44 4 Ronald A. DeVore and George G. Lorentz, Constructive Approximation, p. 102, Theorem 2.6.

Theorem 5 (Nikolsky’s inequality).

If T∈𝒯n and 0<q≤p≤∞, then for r≥q2 an integer,

∥T∥p≤(2⁢n⁢r+1)1q-1p⁢∥T∥q.
Proof.

Let m=n⁢r. Then Tr∈𝒯m, so Tr*Dm=Tr, and using this and the Cauchy-Schwarz inequality we have, for x∈𝕋,

|T⁢(x)r| =|12⁢π⁢∫𝕋T⁢(t)r⁢Dm⁢(x-t)|
≤12⁢π⁢∫𝕋|T⁢(t)|r⁢|Dm⁢(x-t)|⁢𝑑t
≤∥T∥∞r-q2⋅12⁢π⁢∫𝕋|T⁢(t)|q2⁢|Dm⁢(x-t)|⁢𝑑t
≤∥T∥∞r-q2⁢∥|T|q/2∥2⁢∥Dm∥2
=∥T∥∞r-q2⁢∥T∥qq2⁢∥Dm^∥ℓ2⁢(ℤ)
=2⁢m+1⁢∥T∥∞r-q2⁢∥T∥qq2.

Hence

∥T∥∞r≤2⁢m+1⁢∥T∥∞r-q2⁢∥T∥qq2,

thus

∥T∥∞≤(2⁢m+1)1q⁢∥T∥q.

Then, using ∥T∥p≤∥T∥∞1-qp⁢∥T∥qqp, we have

∥T∥p≤(2⁢m+1)1q-1p⁢∥T∥q1-qp⁢∥T∥qqp=(2⁢m+1)1q-1p⁢∥T∥q.

∎

4 The complementary Bernstein inequality

We define a homogeneous Banach space to be a linear subspace B of L1⁢(𝕋) with a norm ∥f∥L1⁢(𝕋)≤∥f∥B with which B is a Banach space, such that if f∈B and τ∈𝕋 then fτ∈B and ∥fτ∥B=∥f∥B, and such that if f∈B then fτ→f in B as τ→0.

Fejér’s kernel is, for n≥0,

Kn⁢(t)=∑|j|≤n(1-|j|n+1)⁢ei⁢j⁢t=∑j∈ℤχn⁢(j)⁢(1-|j|n+1)⁢ei⁢j⁢t  t∈𝕋.

One calculates that, for t∉4⁢π⁢ℤ,

Kn⁢(t)=1n+1⁢(sin⁡n+12⁢tsin⁡12⁢t)2.

Bernstein’s inequality is a statement about functions whose Fourier transform is supported only on low frequencies. The following is a statement about functions whose Fourier transform is supported only on high frequencies.55 5 Yitzhak Katznelson, An Introduction to Harmonic Analysis, third ed., p. 55, Theorem 8.4. In particular, for 1≤p<∞, Lp⁢(𝕋) is a homogeneous Banach space, and so is C⁢(𝕋) with the supremum norm.

Theorem 6.

Let B be a homogeneous Banach space and let m be a positive integer. Define Cm as Cm=m+1 if m is even and Cm=12⁢m if m is odd. If

f⁢(t)=∑|j|≥naj⁢ei⁢j⁢t,t∈𝕋,

is m times differentiable and f(m)∈B, then f∈B and

∥f∥B≤Cm⁢n-m⁢∥f(m)∥B.
Proof.

Suppose that m is even. It is a fact that if aj,j∈ℤ, is an even sequence of nonnegative real numbers such that aj→0 as |j|→∞ and such that for each j>0,

aj-1+aj+1-2⁢aj≥0,

then there is a nonnegative function f∈L1⁢(𝕋) such that f^⁢(j)=aj for all j∈ℤ.66 6 Yitzhak Katznelson, An Introduction to Harmonic Analysis, third ed., p. 24, Theorem 4.1. Define

aj={j-m|j|≥nn-m+(n-|j|)⁢(n-m-(n+1)-m)|j|≤n-1.

It is apparent that aj is even and tends to 0 as |j|→∞. For 1≤j≤n-2,

aj-1+aj+1-2⁢aj=0.

For j=n-1,

aj-1+aj+1-2⁢aj =n-m+(n-(n-2))⁢(n-m-(n+1)-m)+n-m
-2⁢(n-m+(n-(n-1))⁢(n-m-(n+1)-m))
=0.

The function j↦j-m is convex on {n,n+1,…}, as m≥1, so for j≥n we have aj-1+aj+1-2⁢aj≥0. Therefore, there is some nonnegative ϕm,n∈L1⁢(𝕋) such that

ϕm,n^⁢(j)=aj,j∈ℤ.

Because ϕm,n is nonnegative, and using n-m-(n+1)-m<mn⁢n-m,

∥ϕm,n∥1=ϕm,n^⁢(0)=n-m+n⁢(n-m-(n+1)-m)<(m+1)⁢n-m.

Define d⁢μm,n⁢(t)=12⁢π⁢ϕm,n⁢(t)⁢d⁢t. For |j|≥n,

f(m)*μm,n^⁢(j) =f(m)^⁢(j)⁢μm,n^⁢(j)
=(i⁢j)m⁢f^⁢(j)⁢ϕm,n^⁢(j)
=(i⁢j)m⁢f^⁢(j)⋅|j|-m
=im⁢f^⁢(j).

For |j|<n, since f^⁢(j)=0 we have

f(m)*μm,n^⁢(j)=(i⁢j)m⁢f^⁢(j)⁢ϕm,n^⁢(j)=0=im⁢f^⁢(j),

so for all j∈ℤ,

f(m)*μm,n^⁢(j)=im⁢f^⁢(j).

This implies that f(m)*μm,n=im⁢f, which in particular tells us that f∈B. Then,

∥f∥B =∥im⁢f∥B
=∥f(m)*μm,n∥B
≤∥f(m)∥B⁢∥μm,n∥M⁢(𝕋)
=∥ϕm,n∥1⁢∥f(m)∥B
≤(m+1)⁢n-m⁢∥f(m)∥B.

This shows what we want in the case that m is even, with Cm=m+1.

Suppose that m is odd. For l a positive integer, define ψl:𝕋→ℂ by

ψl⁢(t)=(e2⁢l⁢i⁢t+12⁢e3⁢l⁢i⁢t)⁢Kl-1⁢(t),t∈𝕋.

There is a unique ln such that n∈{2⁢ln,2⁢ln+1}. For k≥0 an integer, define Ψn,k:𝕋→ℂ by

Ψn,k⁢(t)=ψln⁢2k⁢(t),t∈𝕋.

Ψn,k satisfies

∥Ψn,k∥1≤32⁢∥Kk-1∥1=32⋅12⁢π⁢∫𝕋|Kk-1⁢(t)|⁢𝑑t=32⋅12⁢π⁢∫𝕋Kk-1⁢(t)⁢𝑑t=32.

On the one hand, for j≤0, from the definition of ψl we have Ψn,k^⁢(j)=0, hence ∑k=0∞Ψn,k^⁢(j)=0. On the other hand, for j≥n we assert that

∑k=0∞Ψn,k^⁢(j)=1.

We define Φn:𝕋→ℂ by

Φn⁢(t)=∑k=0∞(Ψn,k*ϕ1,n⁢2k)⁢(t),t∈𝕋.

We calculate the Fourier coefficients of Φn. For j≥n,

Φn^⁢(j)=∑k=0∞Φn,k^⁢(j)⁢ϕ1,n⁢2k^⁢(j)=1j⁢∑k=0∞Φn,k^⁢(j)=1j.

As well,

∥Φn∥1≤∑k=0∞∥Ψn,k*ϕ1,n⁢2k∥1≤∑k=0∞∥Ψn,k∥1⁢∥ϕ1,n⁢2k∥1≤32⁢∑k=0∞2⁢(n⁢2k)-1=6n

We now define

d⁢μ1,n⁢(t)=12⁢π⁢(Φn⁢(t)-Φn⁢(-t))⁢d⁢t,

which satisfies for |j|≥n,

μ1,n^⁢(j)=Φn^⁢(j)-Φn^⁢(-j)=1j

and hence

f′*μ1,n^⁢(j)=f′^⁢(j)⁢μ1,n^⁢(j)=i⁢j⁢f^⁢(j)⋅1j=i⁢f^⁢(j).

Because f^⁢(j)=0 for |j|<n, f′*μ1,n^⁢(j)=0 for |j|<n, it follows that for any j∈ℤ,

f′*μ1,n^⁢(j)=i⁢f^⁢(j),

and therefore,

f′*μ1,n=i⁢f.

Then

∥f∥B=∥i⁢f∥B=∥f′*μ1,n∥B≤∥μ1,n∥M⁢(𝕋)⁢∥f′∥B≤2⁢∥Φn∥1⁢∥f′∥B≤12n⁢∥f′∥B.

That is, with C1=12 we have

∥f∥B≤12⁢n-1⁢∥f′∥B.

For m=2⁢ν+1, we define

μm,n=μ1,n*μ2⁢ν,n,

for which we have, for |j|≥n,

f(m)*μm,n^⁢(j)=(i⁢j)m⁢f^⁢(j)⁢μ1,n^⁢(j)⁢μ2⁢ν,n^⁢(j)=(i⁢j)m⁢f^⁢(j)⋅1j⋅j-2⁢ν=im⁢f^⁢(j).

It follows that

f(m)*μm,n=im⁢f,

whence

∥f∥B =∥im⁢f∥B
=∥f(m)*μm,n∥B
≤∥μm,n∥M⁢(𝕋)⁢∥f(m)∥B
≤∥μ1,n∥M⁢(𝕋)⁢∥μ2⁢ν,n∥M⁢(𝕋)⁢∥f(m)∥B
≤12n⋅(2⁢ν+1)⁢n-2⁢ν⁢∥f(m)∥B
=12⁢m⁢n-m⁢∥f(m)∥B.

That is, with Cm=12⁢m, we have

∥f∥B≤Cm⁢n-m⁢∥f(m)∥B,

completing the proof. ∎