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A generalization of Hardy’s operator and an asymptotic Müntz-Szász Theorem

Jim Agler    John E. McCarthy Partially supported by National Science Foundation Grant DMS 2054199

1 Overview

In this note we shall give a novel proof that Hardy’s Operator HH, defined on L2([0,1]){\rm L}^{2}([0,1]) by the formula,

Hf(x)=1x0xf(t)𝑑t,x(0,1],Hf(x)\ =\ \frac{1}{x}\int_{0}^{x}f(t)dt,\qquad x\in(0,1],

is bounded. This new proof relies only on algebra together with the observation that the monomial functions are eigenvectors for HH. Specifically, for each k0k\geq 0,

Hxk=1k+1xk.Hx^{k}=\frac{1}{k+1}x^{k}. (1.1)

Always intrigued by results in analysis whose proofs rely mainly on algebra, the new proof of Hardy’s Inequality prompts the authors to propose the following definition.

Definition 1.2.

We say that TT is a monomial operator if TT is a bounded operator on L2([0,1]){\rm L}^{2}([0,1]) and there exist a number mm and a sequence of scalars c0,c1,c2c_{0},c_{1},c_{2}\ldots such that for all kk,

Txk=ckxk+m.Tx^{k}=c_{k}x^{k+m}. (1.3)

We shall call the number mm in (1.3) the order of TT. It can be any complex number with non-negative real part, though in all our examples it will be a natural number. In addition to HH, a monomial operator of order 0, other examples of monomial operators are the multiplication operator MxM_{x} that sends a function f(x)f(x) to the function xf(x)xf(x), and the Volterra operator V=MxHV=M_{x}H, the operator given by

Vf(x)=0xf(t)𝑑t,x(0,1].Vf(x)\ =\ \int_{0}^{x}f(t)dt,\qquad x\in(0,1].

Both MxM_{x} and VV are of order 11.

For 0s10\leq s\leq 1 we shall use L2[s,1]L^{2}[s,1] to denote the closed subspace of L2[0,1]L^{2}[0,1] of functions that are 0 on [0,s][0,s].

Definition 1.4.

We shall say that a bounded operator T:L2[0,1]L2[0,1]T:L^{2}[0,1]\to L^{2}[0,1] is vanishing preserving if TL2[s,1]L2[s,1]TL^{2}[s,1]\subseteq L^{2}[s,1] for every ss in [0,1)[0,1).

In this note we shall prove the following result.

Theorem 1.5.

If TT is a monomial operator, then TT is vanishing preserving.

Why might such a theorem be true? If TT is a monomial operator, and ff is a polynomial that vanishes at 0 to some high order MM, then TfTf also vanishes to order at least MM. So if one thinks of vanishing on [0,s][0,s] as an extreme case of vanishing to high order, one might believe that monomial operators preserve this property.

Our proof of Theorem 1.5 relies on a new type of Müntz-Szász Theorem, wherein the monomial sequence is allowed to drift. This may be more interesting than the theorem itself!

2 Hardy’s Inequality

For a continuous function ff on [0,1][0,1] consider the continuous function HfHf on (0,1](0,1] defined by the formula

Hf(x)=1x0xf(t)𝑑t,x(0,1].Hf(x)=\frac{1}{x}\int_{0}^{x}f(t)dt,\qquad x\in(0,1]. (2.1)

Noting that as x0x\to 0, 1x\frac{1}{x}\to\infty and 0xf(t)𝑑t0\int_{0}^{x}f(t)dt\to 0, the following question arises:

How does HfHf behave near 0?

Invoking the Mean Value Theorem for Integrals yields that for each x[0,1]x\in[0,1], there exists c[0,x]c\in[0,x] such that Hf(x)=f(c)Hf(x)=f(c). Thus,

|Hf(x)|maxt[0,x]|f(t)|,x(0,1],|Hf(x)|\leq\max_{t\in[0,x]}|f(t)|,\qquad x\in(0,1], (2.2)

so that in particular, HfHf is bounded near 0. More delicately, if we apply the MVT to the function f(x)f(0)f(x)-f(0), we obtain the estimate

|Hf(x)f(0)|maxt[0,x]|f(t)f(0)|,x(0,1],|Hf(x)-f(0)|\leq\max_{t\in[0,x]}|f(t)-f(0)|,\qquad x\in(0,1], (2.3)

which implies that Hf(x)f(0)Hf(x)\to f(0) as x0x\to 0. Therefore, if we agree to extend the definition of HfHf at the point x=0x=0 by setting Hf(0)=f(0)Hf(0)=f(0), then our observations imply the following proposition.

Proposition 2.4.

If ff is a continuous function on [0,1][0,1], then HfHf is a continuous function on [0,1][0,1]. Furthermore,

maxx[0,1]|Hf(x)|maxx[0,1]|f(x)|.\max_{x\in[0,1]}|Hf(x)|\leq\max_{x\in[0,1]}|f(x)|. (2.5)

Hardy [5] was the first to study the local behavior of HH at 0 for functions equipped with norms other than the supremum norm. His result when specialized to L2([0,1]){\rm L}^{2}([0,1]), the Hilbert space of square integrable Lesbesgue measurable functions on [0,1][0,1], is as follows.

Proposition 2.6.

(Hardy’s Inequality in L2([0,1]){\rm L}^{2}([0,1])) If ff is a measurable function on [0,1][0,1], then HfHf is a measurable function on [0,1][0,1], and

01|Hf(x)|2𝑑x401|f(x)|2.\int_{0}^{1}|Hf(x)|^{2}dx\leq 4\int_{0}^{1}|f(x)|^{2}. (2.7)

A linear operator TT on a normed vector space 𝒱{\mathcal{V}} is called bounded111It is a straightforward exercise to show that a linear operator is bounded if and only if it is continuous. if there is some constant CC so that

TvCvv𝒱.\|Tv\|\ \leq\ C\|v\|\qquad\forall v\in{\mathcal{V}}. (2.8)

The infimum of all CC for which (2.8) holds is called the norm of TT, and written T\|T\|. Using this terminology, (2.7) says H2\|H\|\leq 2.

Our proof of Proposition 2.6 in Section 4 relies on a new “sum of squares identity” involving the operator HH, proved using (1.1).

3 Hilbert Space distance formula

Let h1,,hnh_{1},\ldots,h_{n} be nn vectors in a hilbert space \mathcal{H}. We may associate to these vectors their Gram matrix, i.e., the n×nn\times n matrix G[h1,,hn]G[h_{1},\ldots,h_{n}] defined by

[G[h1,,hn]]ij=[hj,hi].\big{[}G[h_{1},\ldots,h_{n}]\big{]}_{ij}\ =\ \big{[}\ \langle\,h_{j}\,,h_{i}\,\rangle_{\mathcal{H}}\ \big{]}.

An often used application is the following elegant formula for the distance to the span of h1,,hnh_{1},\ldots,h_{n}.

Theorem 3.1.

(Hilbert Space Distance Formula) If \mathcal{H} is a Hilbert space, hh\in\mathcal{H}, and h1,h2,,hNh_{1},h_{2},\ldots,h_{N}\in\mathcal{H} are linearly independent, then

dist(h,span{h1,h2,,hN})=detG[h,h1,h2,,hN]detG[h1,h2,,hN].\operatorname{dist}(h,\operatorname{span}\{h_{1},h_{2},\ldots,h_{N}\})\ =\ \sqrt{\frac{\det G[h,h_{1},h_{2},\ldots,h_{N}]}{\det G[h_{1},h_{2},\ldots,h_{N}]}}. (3.2)
Proof.

Write h=k+mh=k+m, where kk is in the span of {h1,,hN}\{h_{1},\dots,h_{N}\} and mm is perpendicular to the span. Then m=dist(h,span{h1,h2,,hN})\|m\|=\operatorname{dist}(h,\operatorname{span}\{h_{1},h_{2},\ldots,h_{N}\}).

We can write

detG[h,h1,h2,,hN]=detG[k,h1,h2,,hN]+detG[m,h1,h2,,hN].\det G[h,h_{1},h_{2},\ldots,h_{N}]\ =\ \det G[k,h_{1},h_{2},\ldots,h_{N}]+\det G[m,h_{1},h_{2},\ldots,h_{N}].

Since kk is in the span of {h1,,hN}\{h_{1},\dots,h_{N}\}, we have

detG[k,h1,h2,,hN]= 0.\det G[k,h_{1},h_{2},\ldots,h_{N}]\ =\ 0.

Moreover, G[m,h1,h2,,hN]G[m,h_{1},h_{2},\ldots,h_{N}] is a matrix whose first row is (m,m,0,,0)(\langle\,m\,,m\,\rangle,0,\dots,0). Therefore

detG[m,h1,h2,,hN]=m2detG[h1,h2,,hN.\det G[m,h_{1},h_{2},\ldots,h_{N}]\ =\ \|m\|^{2}\det G[h_{1},h_{2},\ldots,h_{N}.

Combining these observations, we get (3.2). ∎

4 A Hilbert space proof of Hardy’s Inequality

The key step in our proof is the following lemma.

Lemma 4.1.

An Identity for HH

f2=(1H)f2+|01f(x)𝑑x|2fL2([0,1]).\|f\|^{2}\ =\ \|(1-H)f\|^{2}+\left|\int_{0}^{1}f(x)dx\right|^{2}\qquad\forall f\in{\rm L}^{2}([0,1]).
Proof.

It suffices to show for ff a polynomial, since the polynomials are dense in L2[0,1]L^{2}[0,1]. If

f(x)=j=0najxjf(x)\ =\sum_{j=0}^{n}a_{j}x^{j}

then

f2\displaystyle\|f\|^{2} =\displaystyle\ =\ 01i,j=0naiaj¯xi+jdx\displaystyle\int_{0}^{1}\sum_{i,j=0}^{n}a_{i}\overline{a_{j}}x^{i+j}dx
=\displaystyle= i,j=0naiaj¯1i+j+1.\displaystyle\sum_{i,j=0}^{n}a_{i}\overline{a_{j}}\frac{1}{i+j+1}.

Likewise, as

(1H)f(x)=j=0njj+1ajxj,(1-H)f(x)\ =\ \sum_{j=0}^{n}\frac{j}{j+1}a_{j}x^{j},
(1H)f2=i,j=0naiaj¯ij(i+1)(j+1)(i+j+1).\|(1-H)f\|^{2}\ =\ \sum_{i,j=0}^{n}a_{i}\overline{a_{j}}\frac{ij}{(i+1)(j+1)(i+j+1)}.

Hence

f2(1H)f2\displaystyle\|f\|^{2}-\|(1-H)f\|^{2} =i,j=0naiaj¯(1i+j+1ij(i+1)(j+1)(i+j+1))\displaystyle\ =\ \sum_{i,j=0}^{n}a_{i}\overline{a_{j}}\left(\frac{1}{i+j+1}-\frac{ij}{(i+1)(j+1)(i+j+1)}\right)
=i,j=0naiaj¯(1(i+1)(j+1))\displaystyle\ =\ \sum_{i,j=0}^{n}a_{i}\overline{a_{j}}\left(\frac{1}{(i+1)(j+1)}\right)
=|01f(x)𝑑x|2.\displaystyle\ =\ |\int_{0}^{1}f(x)dx|^{2}.

Proof.

We now complete the proof of Proposition 2.6. We want to prove that H2\|H\|\leq 2. By Lemma 4.1, (1H)1\|(1-H)\|\leq 1. Therefore,

H=1(1H)1+1H1+1=2.\|H\|=\|1-(1-H)\|\leq\|1\|+\|1-H\|\leq 1+1=2.

5 An asymptotic Müntz-Szász Theorem

Let SS be a subset of the non-negative integers. When is the linear span of the monomials {xn:nS}\{x^{n}:n\in S\} dense? The Müntz-Szász Theorem, proved by Müntz [6] and Szász [7], answers this question in both L2[0,1]L^{2}[0,1] and C[0,1]C[0,1], the continuous functions on [0,1][0,1]. The answer is basically the same in both cases, but the constant function 11 plays a special role in C[0,1]C[0,1], since it cannot be approximated by any polynomial that vanishes at 0 (which all the other monomials do).

Theorem 5.1.

(Müntz-Szász Theorem) (i) The linear span of {xn:nS}\{x^{n}:n\in S\} is dense in L2[0,1]L^{2}[0,1] if and only if

nS1n+1=.\sum_{n\in S}\frac{1}{n+1}\ =\ \infty. (5.2)

(ii) The linear span of {xn:nS}\{x^{n}:n\in S\} is dense in C[0,1]C[0,1] if and only if 0S0\in S and (5.2) holds.

What happens if the approximants come from a set of linear combinations of monomials that is losing as well as gaining members? Fix an increasing sequence {Nn}\{N_{n}\} of natural numbers and for each nn define

Sn={n,n+1,,n+Nn}andS_{n}=\{n,n+1,\ldots,n+N_{n}\}\qquad\text{and}
n=span{xn,xn+1,,xn+Nn}.\mathcal{M}_{n}=\operatorname{span}\ \{x^{n},x^{n+1},\ldots,x^{n+N_{n}}\}.

For each nn let ρn\rho_{n} denote the fraction of the non-negative integers less than or equal to n+Nnn+N_{n} that do not lie in SnS_{n}, i.e.,

ρn=nn+Nn+1.\rho_{n}=\frac{n}{n+N_{n}+1}.

Finally, with this setup, let

={fL2([0,1])|limndist(f,n)=0}.\mathcal{M}_{\infty}=\{f\in{\rm L}^{2}([0,1])\,|\,\lim_{n\to\infty}\operatorname{dist}(f,\mathcal{M}_{n})=0\}. (5.3)

We wish to characterize \mathcal{M}_{\infty} (Theorem 5.12 below). We shall follow Müntz’s original proof of Theorem 5.1 [6]. His argument involved an ingenious calculation using Theorem 3.1 and the following venerable formula of Cauchy [3].

Theorem 5.4.

(The Cauchy Determinant Formula) If MM is the N×NN\times N Cauchy matrix defined by the formula

M=[1x1y11x1y21x1yN1x2y11x2y21x2yN1xNy11xNy21xNyN],M=\begin{bmatrix}\frac{1}{x_{1}-y_{1}}&\frac{1}{x_{1}-y_{2}}&\ldots&\frac{1}{x_{1}-y_{N}}\\ \frac{1}{x_{2}-y_{1}}&\frac{1}{x_{2}-y_{2}}&\ldots&\frac{1}{x_{2}-y_{N}}\\ \vdots&\vdots&\ddots&\vdots\\ \frac{1}{x_{N}-y_{1}}&\frac{1}{x_{N}-y_{2}}&\ldots&\frac{1}{x_{N}-y_{N}}\end{bmatrix},

where for all ii and jj, xiyjx_{i}\not=y_{j}, then

detM=1j<iN(xixj)(yjyi)1i,jN(xiyj).\det M=\frac{\prod\limits_{1\leq j<i\leq N}(x_{i}-x_{j})(y_{j}-y_{i})}{\prod\limits_{1\leq i,j\leq N}(x_{i}-y_{j})}.

We need two more auxiliary results.

Proposition 5.5.

(Baby Brodskii-Donoghue Theorem) Let {\mathcal{M}} be a closed subspace of L2[0,1]L^{2}[0,1] that is invariant under both MxM_{x} and VV. Then =L2[s,1]{\mathcal{M}}=L^{2}[s,1] for some ss between 0 and 11.

Proof.

Note that the constant function 1 has the unique representation in L2([0,1]){\rm L}^{2}([0,1]),

1=f+g1=f+g (5.6)

where ff\perp{\mathcal{M}} and gg\in{\mathcal{M}}. The fact that {\mathcal{M}} is MxM_{x} invariant, implies that pgpg\in{\mathcal{M}} whenever pp is a polynomial222Note that it is also true that pfpf\in{\mathcal{M}}^{\perp} whenever pp is a polynomial, since MxM_{x} is self-adjoint., it follows that fpgf\perp pg whenever pp is a polynomial. But then fg¯pf\bar{g}\perp p for all polynomials which implies that

fg¯=0f\bar{g}=0 (5.7)

For a Lesbesgue measurable set E[0,1]E\subseteq[0,1] we let χE\chi_{E} denote the characteristic function of EE, i.e., the function defined by

χE(x)={0if xE1if xE.\chi_{E}(x)=\left\{\begin{array}[]{ll}0&\mbox{if }x\not\in E\\ 1&\mbox{if }x\in E\end{array}\right..

We observe that (5.6) and (5.7) imply that there exists a partition of [0,1][0,1] into two measurable sets FF and GG such that f=χFf=\chi_{F} and g=χGg=\chi_{G}. We define a parameter s[0,1]s\in[0,1] by setting

s=sup{x|g(t)=0 for a.e. t[0,x]}.s=\sup\{x\,|\,g(t)=0\text{ for a.e. }t\in[0,x]\}. (5.8)

Notice that with this definition, we have that

Vg(x)=0 for a.e. [0,s] and Vg(x)>0 for a.e. [s,1].Vg(x)=0\text{ for a.e. }\in[0,s]\ \ \ \ \text{ and }\ \ \ \ Vg(x)>0\text{ for a.e. }\in[s,1]. (5.9)

Since gg\in{\mathcal{M}}, we have VgVg\in{\mathcal{M}}. Also, recall that ff\in{\mathcal{M}}^{\perp}. Therefore, using (5.9) we see that F[0,s]F\subseteq[0,s]. In light of (5.6), this implies [s,1]G[s,1]\subseteq G, which in turn, implies via (5.8) that

f=χ[0,s] and g=χ[s,1].f=\chi_{[0,s]}\ \ \text{ and }\ \ g=\chi_{[s,1]}. (5.10)

As pfpf\in{\mathcal{M}}^{\perp} and pgpg\in{\mathcal{M}} whenever pp is a polynomial, it follows immediately from (5.10) and the fact that the polynomials are dense in both L2([0,s]){{\rm L}^{2}([0,s])} and L2([s,1]){\rm L}^{2}([s,1]), that

L2([0,s]) and L2([s,1]).{{\rm L}^{2}([0,s])}\subseteq{\mathcal{M}}^{\perp}\ \ \text{ and }\ \ \ {\rm L}^{2}([s,1])\subseteq{\mathcal{M}}.

Hence, we have that both L2([s,1]){{\rm L}^{2}([s,1])}\supseteq{\mathcal{M}} and L2([s,1]){\rm L}^{2}([s,1])\subseteq{\mathcal{M}}, so that L2([s,1])={\rm L}^{2}([s,1])={\mathcal{M}}, as was to be proved. ∎

We call Proposition 5.5 the Baby Brodskii-Donoghue Theorem because Brodskii and Donoghue independently proved the far deeper fact that the only closed invariant subspaces of VV are L2[s,1]L^{2}[s,1] [2, 4]. The operator MxM_{x} has other invariant subspaces. Indeed the ideas in the preceding proof can be adapted to show that the invariant subspaces of MxM_{x} are the spaces {fL2[0,1]:f(x)=0a.e.onF}\{f\in L^{2}[0,1]:f(x)=0{\rm\ a.e.\ on\ }F\}, where FF is any measurable subset of [0,1][0,1].

Lemma 5.11.

If \mathcal{M}_{\infty} is as in (5.3), then there exists s[0,1]s\in[0,1] such that =L2([s,1])\mathcal{M}_{\infty}={\rm L}^{2}([s,1]).

Proof.

Observe first that if

p(x)=k=nn+Nnakxkn,p(x)=\sum_{k=n}^{n+N_{n}}a_{k}x^{k}\in\mathcal{M}_{n},

then

Mxp(x)=xp(x)=k=n+1n+Nn+1ak1xkn+1.M_{x}p(x)=xp(x)=\sum_{k=n+1}^{n+N_{n}+1}a_{k-1}x^{k}\in\mathcal{M}_{n+1}.

Hence,

Mx.M_{x}\mathcal{M}_{\infty}\subseteq\mathcal{M}_{\infty}.

Likewise,

V.V\mathcal{M}_{\infty}\subseteq\mathcal{M}_{\infty}.

Now the result follows from Lemma 5.5. ∎

Theorem 5.12.

(Asymptotic Müntz-Szász Theorem) Let Sn={n,n+1,,n+Nn}S_{n}=\{n,n+1,\ldots,n+N_{n}\}, let n=span{xn,xn+1,,xn+Nn}\mathcal{M}_{n}=\operatorname{span}\ \{x^{n},x^{n+1},\ldots,x^{n+N_{n}}\}, and let ρn=nn+Nn+1.\rho_{n}=\frac{n}{n+N_{n}+1}. If

limnρn=ρ,\lim_{n\to\infty}\rho_{n}=\rho,

then

=L2([ρ2,1]).\mathcal{M}_{\infty}={{\rm L}^{2}([\rho^{2},1])}.
Proof.

By Lemma 5.11 there exists s[0,1]s\in[0,1] such that

=L2([s,1]).\mathcal{M}_{\infty}={\rm L}^{2}([s,1]).

Noting that

dist(1,L2([s,1]))=s,\operatorname{dist}(1,{\rm L}^{2}([s,1]))=\sqrt{s},

we see that the theorem will follow if we can show that

dist(1,)=ρ,\operatorname{dist}(1,\mathcal{M}_{\infty})=\rho,

or equivalently that

limndist(1,n)=ρ.\lim_{n\to\infty}\operatorname{dist}(1,\mathcal{M}_{n})=\rho. (5.13)

Now fix N+1N+1 distinct real numbers α0,α1,,αN(12,)\alpha_{0},\alpha_{1},\ldots,\alpha_{N}\in(-\tfrac{1}{2},\infty). In Theorem 5.4 if for i,j=1,2,,Ni,j=1,2,\ldots,N we let xi=αi+12x_{i}=\alpha_{i}+\tfrac{1}{2} and yj=(αj+12)y_{j}=-(\alpha_{j}+\tfrac{1}{2}) we obtain that

detG(xα1,,xαN)=1j<iN(αiαj)21i,jN(αi+αj+1).\det G(x^{\alpha_{1}},\ldots,x^{\alpha_{N}})=\frac{\prod\limits_{1\leq j<i\leq N}(\alpha_{i}-\alpha_{j})^{2}}{\prod\limits_{1\leq i,j\leq N}(\alpha_{i}+\alpha_{j}+1)}.

Likewise,

detG(xα0,xα1,,xαN)=0j<iN(αiαj)20i,jN(αi+αj+1).\det G(x^{\alpha_{0}},x^{\alpha_{1}},\ldots,x^{\alpha_{N}})=\frac{\prod\limits_{0\leq j<i\leq N}(\alpha_{i}-\alpha_{j})^{2}}{\prod\limits_{0\leq i,j\leq N}(\alpha_{i}+\alpha_{j}+1)}.

Therefore,

detG(xα0,xα1,,xαN)detG(xα1,,xαN)=12α0+1i=1N(αiα0)2i=1N(αi+α0+1)2\frac{\det G(x^{\alpha_{0}},x^{\alpha_{1}},\ldots,x^{\alpha_{N}})}{\det G(x^{\alpha_{1}},\ldots,x^{\alpha_{N}})}=\frac{1}{2\alpha_{0}+1}\ \frac{\prod_{i=1}^{N}(\alpha_{i}-\alpha_{0})^{2}}{\prod_{i=1}^{N}(\alpha_{i}+\alpha_{0}+1)^{2}}

Hence, using Theorem 3.1 we get

dist(1,n)2\displaystyle\operatorname{dist}(1,\mathcal{M}_{n})^{2} =\displaystyle\ =\ detG(x0,xn,,xn+Nn)detG(xn,,xn+Nn)\displaystyle\frac{\det G(x^{0},x^{n},\dots,x^{n+N_{n}})}{\det G(x^{n},\dots,x^{n+N_{n}})}
=\displaystyle= i=0Nn(n+i)2i=0Nn(n+i+1)2\displaystyle\frac{\prod_{i=0}^{N_{n}}(n+i)^{2}}{\prod_{i=0}^{N_{n}}(n+i+1)^{2}}
=\displaystyle= (nn+Nn+1)2\displaystyle(\frac{n}{n+N_{n}+1})^{2}
=\displaystyle= ρn2.\displaystyle\rho_{n}^{2}.

Equation (5.13) now follows. ∎

6 The Bernstein Conundrum: Asymptotic Müntz-Szász Theorem for C[0,1]C[0,1]

The C[0,1]C[0,1] Müntz-Szász Theorem can be deduced from the L2L^{2} version. What about the asymptotic version? Let n\mathcal{M}_{n} be as in Section 5, and let unif{\mathcal{M}}_{\infty}^{\rm unif} be

unif={fC[0,1]|limndist(f,n)=0},{\mathcal{M}}_{\infty}^{\rm unif}\ =\ \{f\in C[0,1]\,|\,\lim_{n\to\infty}\operatorname{dist}(f,\mathcal{M}_{n})=0\}, (6.1)

where in this section all distances are with respect to the supremum norm333This means fC[0,1]=sup0x1|f(x)|\|f\|_{C[0,1]}=\sup_{0\leq x\leq 1}|f(x)|..

One way to prove the Weierstraß approximation theorem is to use the Bernstein polynomials. For each nn, these are the n+1n+1 polynomials defined by

bk,n(x)=(nk)xk(1x)nk.b_{k,n}(x)\ =\ {{n}\choose{k}}x^{k}(1-x)^{n-k}.

Bernstein proved in 1912 [1] that for every continuous function fC[0,1]f\in C[0,1], the polynomials

pn(x)=k=0nf(kn)bk,n(x)p_{n}(x)\ =\ \sum_{k=0}^{n}f(\frac{k}{n})\ b_{k,n}(x) (6.2)

converge uniformly on [0,1][0,1] to ff.

As the lowest order term in bk,nb_{k,n} is xkx^{k}, if ff vanished on [0,ρn][0,\rho_{n}] and one used the Bernstein formula (6.2) to approximate it, the corresponding polynomial pn+Nn+1p_{n+N_{n}+1} would lie in the span of {xn+1,,xn+Nn+1}\{x^{n+1},\dots,x^{n+N_{n}+1}\} which is in xnn+1x\mathcal{M}_{n}\subseteq\mathcal{M}_{n+1}. So one immediately gets that unif{\mathcal{M}}_{\infty}^{\rm unif} contains all the continuous functions that vanish on [0,ρ][0,\rho].

This construction seems natural, and could lead one to suspect that unif{\mathcal{M}}_{\infty}^{\rm unif} should be the functions that vanish on [0,ρ][0,\rho]. However, Theorem 6.3 shows this is incorrect.

Theorem 6.3.

(Asymptotic Müntz-Szász Theorem, Continuous Case) If

limnρn=ρ,\lim_{n\to\infty}\rho_{n}=\rho,

then

unif={fC[0,1]|f=0on[0,ρ2]}.{\mathcal{M}}_{\infty}^{\rm unif}\ =\ \{f\in C[0,1]|f=0{\rm\ on\ }[0,\rho^{2}]\}.
Proof.

As the supremum norm is larger than the L2L^{2} norm, we have

unif\displaystyle{\mathcal{M}}_{\infty}^{\rm unif} \displaystyle\ \subseteq\ C[0,1]\displaystyle\mathcal{M}_{\infty}\cap C[0,1]
=\displaystyle= {fC[0,1]|f=0on[0,ρ2]}.\displaystyle\{f\in C[0,1]|f=0{\rm\ on\ }[0,\rho^{2}]\}.

For the reverse inclusion, notice that it follows from Cauchy-Schwarz that the Volterra operator is a bounded linear map from L2[0,1]L^{2}[0,1] into C[0,1]C[0,1]. (Indeed, if gL2[0,1]g\in L^{2}[0,1], we get that VgVg satisfies a Hölder continuity condition of order 12\frac{1}{2}.)

Let ff be a C1C^{1} function that vanishes on [0,ρ2][0,\rho^{2}]. Then f=Vgf=Vg, where g=fg=f^{\prime}. By Theorem 5.12, there are polynomials pnnp_{n}\in\mathcal{M}_{n} that converge in L2L^{2} to gg. Then VpnVp_{n} converges in C[0,1]C[0,1] to ff, so ff is in unif{\mathcal{M}}_{\infty}^{\rm unif}. As unif{\mathcal{M}}_{\infty}^{\rm unif} is closed, and the C1C^{1} functions that vanish on [0,ρ2][0,\rho^{2}] are dense in the continuous ones, we get

{fC[0,1]|f=0on[0,ρ2]}unif.\{f\in C[0,1]|f=0{\rm\ on\ }[0,\rho^{2}]\}\ \subseteq\ {\mathcal{M}}_{\infty}^{\rm unif}.

Question 6.4.

Can one prove Theorem 6.3 directly using Bernstein approximation?

7 Proof of Theorem 1.5

Proof.

Assume that TT is a monomial operator of order mm, let s(0,1)s\in(0,1), and fix fL2([s,1])f\in{\rm L}^{2}([s,1]). We wish to prove that TfL2([s,1])Tf\in{\rm L}^{2}([s,1]).

Choose an increasing sequence of natural numbers {Nn}\{N_{n}\} such that

limnnn+Nn=s.\lim_{n\to\infty}\frac{n}{n+N_{n}}=\sqrt{s}.

By Theorem 5.12, there exists a sequence of polynomials {pn}\{p_{n}\} where for each nn, pnp_{n} has the form

pn(x)=k=nn+Nnckxkp_{n}(x)=\sum_{k=n}^{n+N_{n}}c_{k}x^{k}

and such that

pnf in L2([0,1]).p_{n}\to f\ \text{ in }\ {\rm L}^{2}([0,1]).

As TT is bounded,

TpnTf in L2([0,1]).Tp_{n}\to Tf\ \text{ in }\ {\rm L}^{2}([0,1]).

Also, as TT is a monomial operator of order mm, for each nn, TpnTp_{n} has the form

Tpn(x)=xmk=nn+Nndkxk.Tp_{n}(x)=x^{m}\sum_{k=n}^{n+N_{n}}d_{k}x^{k}.

For any s>0s>0, multiplication by xmx^{m} is a bounded invertible map from L2([s,1]){\rm L}^{2}([s,1]) to itself. Therefore

k=nn+Nndkxk\sum_{k=n}^{n+N_{n}}d_{k}x^{k}

converges, as nn\to\infty, to some function g(x)g(x), which by Theorem 5.12 is in L2([s,1]){\rm L}^{2}([s,1]). So Tf=xmg(x)Tf=x^{m}g(x), and lies in in L2([s,1]){\rm L}^{2}([s,1]). ∎

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