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Best nn-term approximation of diagonal operators and application to function spaces with mixed smoothness

Van Kien Nguyen Faculty of Basic Sciences, University of Transport and Communications
No.3 Cau Giay Street, Lang Thuong Ward, Dong Da District, Hanoi, Vietnam
Email: [email protected][email protected]
Van Dung Nguyen Faculty of Basic Sciences, University of Transport and Communications
No.3 Cau Giay Street, Lang Thuong Ward, Dong Da District, Hanoi, Vietnam
Email: [email protected][email protected]
Abstract

In this paper we give exact values of the best nn-term approximation widths of diagonal operators between p()\ell_{p}(\mathbb{N}) and q()\ell_{q}(\mathbb{N}) with 0<p,q0<p,q\leq\infty. The result will be applied to obtain the asymptotic constants of best nn-term approximation widths of embeddings of function spaces with mixed smoothness by trigonometric system.

Keywords and Phrases: diagonal operator, best nn-term approximation, mixed smoothness, asymptotic constant, dimensional dependence

Mathematics Subject Classification 2020: 41A44, 41A45, 41A60, 42A10, 47B06

1 Introduction

Nowadays, it is well understood that nonlinear methods of approximation and numerical methods derived from them often produce superior performance when compared with linear methods. In the last three decades there has been a great success in studying nonlinear approximation which was motivated by numerous applications such as numerical analysis, image processing, statistical learning as well as in the design of neural networks. We refer the reader to [13, 14, 15] for the development of nonlinear approximation and its application.

In the present paper we concentrate on a particular nonlinear method, the so-called best nn-term approximation. Our particular interest is exact values of best nn-term approximation of diagonal linear operators from p()\ell_{p}(\mathbb{N}) to q()\ell_{q}(\mathbb{N}). The exact values of approximation quantities of diagonal operators play an important role in high-dimensional approximation and particularly in studying tractability, see, e.g., [7, 24, 25, 26]. In this paper, the exact values of best nn-term approximation of diagonal operators will be applied to get the asymptotic constants of best nn-term approximation of function spaces with mixed smoothness by trigonometric system.

Let XX, YY be Banach spaces and TT a continuous linear operator from XX to YY. Let 𝒟\mathcal{D} be a given countable set in YY, called dictionary. For given xXx\in X we consider the algorithm to approximate TxTx by a finite linear combination of elements contained in this dictionary. The error of this approximation is

σn(Tx;𝒟):=inf(aj)j=1n,(yj)j=1n𝒟Txj=1najyjY,n.\sigma_{n}(Tx;\mathcal{D}):=\inf_{(a_{j})_{j=1}^{n}\subset\mathbb{C},\atop(y_{j})_{j=1}^{n}\subset\mathcal{D}}\Bigg{\|}Tx-\sum_{j=1}^{n}a_{j}y_{j}\Bigg{\|}_{Y},\qquad n\in\mathbb{N}.

We wish to approximate TxTx for all xx in the closed unit ball of XX with respect to the dictionary 𝒟\mathcal{D}. This can be measured by the following benchmark quantity

σn(T;𝒟):=supxX,xX1σn(Tx;𝒟),n.\sigma_{n}(T;\mathcal{D}):=\sup_{x\in X,\,\|x\|_{X}\leq 1}\sigma_{n}(Tx;\mathcal{D}),\qquad n\in\mathbb{N}.

In what follows, we shall call this quantity the best nn-term approximation width.

Let p()\ell_{p}(\mathbb{N}), 0<p0<p\leq\infty, be the classical complex sequence space with the usual (quasi) norm. For 0<p,q0<p,q\leq\infty and positive non-increasing sequence λ=(λk)k\lambda=(\lambda_{k})_{k\in\mathbb{N}}, consider the diagonal linear operator

Tλ:(ξk)k(λkξk)kT_{\lambda}:\,(\xi_{k})_{k\in\mathbb{N}}\mapsto(\lambda_{k}\xi_{k})_{k\in\mathbb{N}} (1.1)

from p()\ell_{p}(\mathbb{N}) to q()\ell_{q}(\mathbb{N}) and ={ek:k}\mathcal{E}=\{e_{k}:k\in\mathbb{N}\} where ek=(δk,j)je_{k}=(\delta_{k,j})_{j\in\mathbb{N}}. We are concerned with the exact value of σn(Tλ,)\sigma_{n}(T_{\lambda},\mathcal{E}). The first result in this direction was given by Stepanets [34] in the case p=qp=q with the condition limkλk=0\lim_{k\to\infty}\lambda_{k}=0. Later Stepanets generalized his result to the case 0<pq<0<p\leq q<\infty, see [35]. Under the same condition limkλk=0\lim_{k\to\infty}\lambda_{k}=0 but by different approach, Gensun and Lixin [17] also obtained exact value of σn(Tλ,)\sigma_{n}(T_{\lambda},\mathcal{E}) in the case p=qp=q. In this paper we give exact values of the best nn-term approximation widths σn(Tλ,)\sigma_{n}(T_{\lambda},\mathcal{E}), nn\in\mathbb{N}, in all cases. We also show that the condition limkλk=0\lim_{k\to\infty}\lambda_{k}=0 in the case 0<p<q<0<p<q<\infty is not necessary. Our main result reads as follows. If 0<p<q<0<p<q<\infty, then we have

σn(Tλ,)=(nn)1/q(k=1nλkp)1/p,\sigma_{n}(T_{\lambda},\mathcal{E})=\dfrac{(n^{*}-n)^{1/q}}{\big{(}\sum_{k=1}^{n^{*}}\lambda_{k}^{-p}\big{)}^{1/p}},

where nn^{*} is the smallest integer m>nm>n such that

(mn)1/q(k=1mλkp)1/p(m+1n)1/q(k=1m+1λkp)1/p.\dfrac{(m-n)^{1/q}}{\big{(}\sum_{k=1}^{m}\lambda_{k}^{-p}\big{)}^{1/p}}\geq\dfrac{(m+1-n)^{1/q}}{\big{(}\sum_{k=1}^{m+1}\lambda_{k}^{-p}\big{)}^{1/p}}.

In the case 0<q<p<0<q<p<\infty and the series k=1λkpq/(pq)\sum_{k=1}^{\infty}\lambda_{k}^{pq/(p-q)} converges, we get

σn(Tλ,)=((nn)ppq(k=1nλkp)qpq+k=n+1λkpqpq)1q1p,\sigma_{n}(T_{\lambda},\mathcal{E})=\Bigg{(}\dfrac{(n_{*}-n)^{\frac{p}{p-q}}}{\big{(}\sum_{k=1}^{n_{*}}\lambda_{k}^{-p}\big{)}^{\frac{q}{p-q}}}+\sum_{k=n_{*}+1}^{\infty}\lambda_{k}^{\frac{pq}{p-q}}\Bigg{)}^{\frac{1}{q}-\frac{1}{p}},

where nn_{*} is the largest integer m>nm>n such that

(mn)λmpk=1mλkp.(m-n)\lambda_{m}^{-p}\leq\sum_{k=1}^{m}\lambda_{k}^{-p}.

The limiting cases p=qp=q or p=p=\infty and/or q=q=\infty are also obtained, see Theorem 2.1.

The above results will be applied to study best nn-term approximation of embedding of function spaces with mixed smoothness by trigonometric system 𝒯d:={eikx:kd}\mathcal{T}^{d}:=\{e^{{\rm i}kx}:\,k\in\mathbb{Z}^{d}\} on the torus 𝕋d{\mathbb{T}}^{d} of dimension dd. Our motivation stems from high-dimensional approximation which has been the object of an intensive study recently. In many high-dimensional approximation problems when the high-dimensional signals or functions have appropriate mixed smoothness, one can apply efficiently approximation methods and sampling algorithms constructed on sparse grids to obtain tractability for algorithms or numerical methods. We refer the reader to the monographs [27, 28] for concepts of computation complexity and results on high dimensional problems. Survey on various aspects of high-dimensional approximation of functions having mixed smoothness can be found in the recent book [12].

There has been a numerous papers working on best nn-term approximation of embeddings of function spaces with mixed smoothness by different dictionaries. For instance, Bazarkhanov [2], Dinh Dũng [8, 10, 11], Kashin and Temlyakov [23], Romanyuk [31, 32], Romanyuk and Romanyuk [33], Temlyakov [36, 37, 39, 40] worked on trigonometric system; Hansen and Sickel [1, 9, 19, 20] on wavelet system. For some recent contributions in this direction we refer to [3, 5, 41, 42]. Historical comments and further references for studies of best nn-term approximation of function spaces with mixed smoothness can be found in the two recent books [12, Chapter 7] and [38, Chapter 9]. Let us emphasize here that all the above mentioned papers worked only on the asymptotic order of best nn-term approximation widths of embeddings of function spaces with mixed smoothness. The asymptotic constants and pre-asymptotic estimates of this quantity have not been considered.

Let 0<s<0<s<\infty and 0<r0<r\leq\infty. This paper considers the best nn-term approximation of embedding of Sobolev space with mixed smoothness Hmixs,r(𝕋d)H^{s,r}_{\mathop{\rm mix}}({\mathbb{T}}^{d}) on the torus 𝕋d{\mathbb{T}}^{d} into either L2(𝕋d)L_{2}({\mathbb{T}}^{d}) or Wiener space 𝒜(𝕋d)\mathcal{A}({\mathbb{T}}^{d}). In this context we will not only investigate the optimal order of the decay of the best nn-term approximation widths but we will determine the asymptotic constant as well. This sheds some light not only on the dependence on nn, but also on the dependence on s,rs,r and in particular on dd. We have

limnσn(id:Hmixs,r(𝕋d)L2(𝕋d),𝒯d)ns(lnn)s(d1)=ss(s+12)s(2d(d1)!)s\lim\limits_{n\to\infty}\frac{\sigma_{n}\big{(}id:H^{s,r}_{\mathop{\rm mix}}({\mathbb{T}}^{d})\to L_{2}({\mathbb{T}}^{d}),\mathcal{T}^{d}\big{)}}{n^{-s}(\ln n)^{s(d-1)}}=\frac{s^{s}}{(s+\frac{1}{2})^{s}}\bigg{(}\frac{2^{d}}{(d-1)!}\bigg{)}^{s}

and if s>1/2s>1/2

limnσn(id:Hmixs,r(𝕋d)𝒜(𝕋d),𝒯d)ns+12(lnn)s(d1)=(ss+12)s(1s12)12(2d(d1)!)s.\lim\limits_{n\to\infty}\frac{\sigma_{n}\big{(}id:H^{s,r}_{\mathop{\rm mix}}({\mathbb{T}}^{d})\to\mathcal{A}({\mathbb{T}}^{d}),\mathcal{T}^{d}\big{)}}{n^{-s+\frac{1}{2}}(\ln n)^{s(d-1)}}=\bigg{(}\frac{s}{s+\frac{1}{2}}\bigg{)}^{s}\bigg{(}\frac{1}{s-\frac{1}{2}}\bigg{)}^{\frac{1}{2}}\bigg{(}\frac{2^{d}}{(d-1)!}\bigg{)}^{s}\,.

In this paper we also obtain the asymptotic constants of best nn-term approximation widths of embeddings of Sobolev spaces with mixed smoothness Hmixs,2(𝕋d)H^{s,2}_{\mathop{\rm mix}}({\mathbb{T}}^{d}) into the energy norm space H1(𝕋d)H^{1}({\mathbb{T}}^{d}). Those embeddings are of particular importance with respect to the numerical solution of the Poisson equation, see [4]. In this case, with s>1s>1 we get

limnσn(id:Hmixs,2(𝕋d)H1(𝕋d),𝒯d)ns+1=(s1)s1(s12)s1(2d)s1(2S+1)(s1)(d1),\lim\limits_{n\to\infty}\frac{\sigma_{n}\big{(}id:H_{\mathop{\rm mix}}^{s,2}({\mathbb{T}}^{d})\to H^{1}({\mathbb{T}}^{d}),\mathcal{T}^{d}\big{)}}{n^{-s+1}}=\frac{(s-1)^{s-1}}{(s-\frac{1}{2})^{s-1}}(2d)^{s-1}(2S+1)^{(s-1)(d-1)},

where

S:=k=1+1(k2+1)s2(s1).S:=\sum_{k=1}^{+\infty}\frac{1}{(k^{2}+1)^{\frac{s}{2(s-1)}}}\,.

The paper is organized as follows. In Section 2 we collect some properties of best nn-term approximation widths and give exact values of best nn-term approximation widths of diagonal operators. The next Section 3 is devoted to the study of asymptotic constants of best nn-term approximation widths of embeddings of weighted classes Fω,p(𝕋d)F_{\omega,p}({\mathbb{T}}^{d}). These results will be used in final Section 4, where we deal with the particular family of weights associated to function spaces of dominating mixed smoothness.

Notation

As usual, \mathbb{N} denotes the natural numbers, 0\mathbb{N}_{0} the non-negative integers, \mathbb{Z} the integers, \mathbb{R} the real numbers, and \mathbb{C} the complex numbers. We denote by 𝕋{\mathbb{T}} the torus, represented by the interval [0,2π][0,2\pi], where the end points of the interval are identified. For a real number aa we denote by a\lfloor a\rfloor the greatest integer not larger than aa. The letter dd is always reserved for the dimension in d\mathbb{N}^{d}, d\mathbb{Z}^{d}, d\mathbb{R}^{d}, d\mathbb{C}^{d}, and 𝕋d\mathbb{T}^{d}. For two Banach spaces XX and YY, (X,Y){\mathcal{L}}(X,Y) denotes the set of continuous linear operators from XX to YY. If (an)n(a_{n})_{n\in\mathbb{N}} and (bn)n(b_{n})_{n\in\mathbb{N}} are two sequences, the symbol anbn,na_{n}\sim b_{n},n\to\infty, indicates that limnanbn=1\lim_{n\to\infty}\frac{a_{n}}{b_{n}}=1. The equivalence anbna_{n}\asymp b_{n} means that there are constants 0<c1c2<0<c_{1}\leq c_{2}<\infty such that c1anbnc2anc_{1}\,a_{n}\leq b_{n}\leq c_{2}\,a_{n} for all nn\in\mathbb{N}.

2 Best nn-term approximation widths of diagonal operators

This section is devoted to give exact values of the best nn-term approximation widths of the diagonal operator defined in (1.1). Let X,YX,Y be Banach spaces, T(X,Y)T\in\mathcal{L}(X,Y), and 𝒟Y\mathcal{D}\subset Y a dictionary. By definition, it is clear that (σn(T,𝒟))n(\sigma_{n}(T,\mathcal{D}))_{n\in\mathbb{N}} is a non-increasing sequence. If W,ZW,Z are Banach spaces and A(W,X)A\in{\mathcal{L}}(W,X), B(Y,Z)B\in{\mathcal{L}}(Y,Z) then we have

σn(BTA,B(𝒟))Bσn(T,𝒟)A.\sigma_{n}(BTA,B(\mathcal{D}))\leq\|B\|\cdot\sigma_{n}(T,\mathcal{D})\cdot\|A\|. (2.1)

A proof of this fact can be found in [5, Lemma 6.1]. For further properties of the best nn-term approximation widths such as additivity, interpolation we refer the reader to [18, 5, 42]. In fact the best nn-term approximation widths belong to the notion of pseudo ss-numbers introduced by Pietsch, see [42].

Let TλT_{\lambda} be the diagonal operator from p()\ell_{p}(\mathbb{N}) to q()\ell_{q}(\mathbb{N}) defined in (1.1). By definition we have

σn(Tλ,)={sup(ξk)kBpinfΓn(kΓn|λkξk|q)1/qif 0<q<sup(ξk)kBpinfΓnsupkΓn|λkξk|if q=,\sigma_{n}(T_{\lambda},\mathcal{E})=\begin{cases}\sup\limits_{(\xi_{k})_{k\in\mathbb{N}}\in B_{p}}\inf\limits_{\Gamma_{n}}\bigg{(}\sum\limits_{k\not\in\Gamma_{n}}|\lambda_{k}\xi_{k}|^{q}\bigg{)}^{1/q}&\text{if }0<q<\infty\\ \sup\limits_{(\xi_{k})_{k\in\mathbb{N}}\in B_{p}}\inf\limits_{\Gamma_{n}}\sup\limits_{k\not\in\Gamma_{n}}|\lambda_{k}\xi_{k}|&\text{if }q=\infty,\end{cases} (2.2)

where BpB_{p} is the closed unit ball of p()\ell_{p}(\mathbb{N}) and Γn\Gamma_{n} is an arbitrary subset of \mathbb{N} with nn elements. In the following we give exact value of σn(Tλ,)\sigma_{n}(T_{\lambda},\mathcal{E}) with 0<p,q0<p,q\leq\infty. The proof is mainly based on the exact values of nn-term approximation widths of the diagonal operators from pM\ell_{p}^{M} to qM\ell_{q}^{M} obtained by Gao in [16]. Here pM\ell_{p}^{M} stands for M\mathbb{C}^{M} equipped with the usual norm pM\|\cdot\|_{\ell_{p}^{M}}. For a vector λ=(λj)j=1M\lambda=(\lambda_{j})_{j=1}^{M} with λ1λ2λM>0\lambda_{1}\geq\lambda_{2}\geq\ldots\geq\lambda_{M}>0 the diagonal operator TλMT^{M}_{\lambda} from pM\ell_{p}^{M} to qM\ell_{q}^{M} is defined by (ξj)j=1M(λjξj)j=1M(\xi_{j})_{j=1}^{M}\mapsto(\lambda_{j}\xi_{j})_{j=1}^{M}. Let M={e1,,eM}\mathcal{E}_{M}=\{e_{1},\ldots,e_{M}\} be the standard basis of M\mathbb{R}^{M}. If nn\in\mathbb{N} and nMn\leq M we have

σn(TλM,M)=sup(ξk)k=1MBpMinfΓnM(kΓnM|λkξk|q)1/q,0<q<,\sigma_{n}(T_{\lambda}^{M},\mathcal{E}_{M})=\sup\limits_{(\xi_{k})_{k=1}^{M}\in B_{p}^{M}}\inf\limits_{\Gamma_{n}^{M}}\bigg{(}\sum\limits_{k\not\in\Gamma_{n}^{M}}|\lambda_{k}\xi_{k}|^{q}\bigg{)}^{1/q},\quad 0<q<\infty, (2.3)

where BpMB_{p}^{M} is the closed unit ball of pM\ell_{p}^{M} and ΓnM\Gamma_{n}^{M} is an arbitrary subset of {1,,M}\{1,\ldots,M\} with nn elements. For λ=(λj)j\lambda=(\lambda_{j})_{j\in\mathbb{N}} we define TλM:=Tλ~MT^{M}_{\lambda}:=T^{M}_{\tilde{\lambda}} where λ~=(λj)j=1M\tilde{\lambda}=(\lambda_{j})_{j=1}^{M}. When q=q=\infty the summation in (2.3) is replaced by supremum.

Note that if λ=(λj)j\lambda=(\lambda_{j})_{j\in\mathbb{N}} satisfying λ1λ2λM>0\lambda_{1}\geq\lambda_{2}\geq\ldots\geq\lambda_{M}>0 and λj=0\lambda_{j}=0 for jM+1j\geq M+1, then

σn(Tλ,)=σn(TλM,M),n\sigma_{n}(T_{\lambda},\mathcal{E})=\sigma_{n}(T^{M}_{\lambda},\mathcal{E}_{M}),\quad n\in\mathbb{N}

which were obtained in [16]. Therefore, we only consider the operator TλT_{\lambda} where λ=(λj)j\lambda=(\lambda_{j})_{j\in\mathbb{N}} is a positive sequence. Our main result in this section reads as follows.

Theorem 2.1.

Let 0<p,q0<p,q\leq\infty and λ=(λk)k\lambda=(\lambda_{k})_{k\in\mathbb{N}} be a positive non-increasing sequence. Let TλT_{\lambda} be defined in (1.1) and nn\in\mathbb{N}.

(i)

If 0<pq<0<p\leq q<\infty we have

σn(Tλ,)=supm>n(mn)1/q(k=1mλkp)1/p.\sigma_{n}(T_{\lambda},\mathcal{E})=\sup_{m>n}\dfrac{(m-n)^{1/q}}{\big{(}\sum_{k=1}^{m}\lambda_{k}^{-p}\big{)}^{1/p}}.

Moreover, if either 0<p<q<0<p<q<\infty or limkλk=0\lim\limits_{k\to\infty}\lambda_{k}=0 then

σn(Tλ,)=(nn)1/q(k=1nλkp)1/p,\sigma_{n}(T_{\lambda},\mathcal{E})=\dfrac{(n^{*}-n)^{1/q}}{\big{(}\sum_{k=1}^{n^{*}}\lambda_{k}^{-p}\big{)}^{1/p}}\,,

where nn^{*} is the smallest integer m>nm>n such that

(mn)1/q(k=1mλkp)1/p(m+1n)1/q(k=1m+1λkp)1/p.\dfrac{(m-n)^{1/q}}{\big{(}\sum_{k=1}^{m}\lambda_{k}^{-p}\big{)}^{1/p}}\geq\dfrac{(m+1-n)^{1/q}}{\big{(}\sum_{k=1}^{m+1}\lambda_{k}^{-p}\big{)}^{1/p}}.
(ii)

If 0<q<p<0<q<p<\infty and the series k=1λkpq/(pq)\sum_{k=1}^{\infty}\lambda_{k}^{pq/(p-q)} converges, then we have

σn(Tλ,)=((nn)ppq(k=1nλkp)qpq+k=n+1λkpqpq)1q1p,\sigma_{n}(T_{\lambda},\mathcal{E})=\Bigg{(}\dfrac{(n_{*}-n)^{\frac{p}{p-q}}}{\big{(}\sum_{k=1}^{n_{*}}\lambda_{k}^{-p}\big{)}^{\frac{q}{p-q}}}+\sum_{k=n_{*}+1}^{\infty}\lambda_{k}^{\frac{pq}{p-q}}\Bigg{)}^{\frac{1}{q}-\frac{1}{p}}\,, (2.4)

where nn_{*} is the largest integer m>nm>n such that

(mn)λmpk=1mλkp.(m-n)\lambda_{m}^{-p}\leq\sum_{k=1}^{m}\lambda_{k}^{-p}. (2.5)
(iii)

If 0<p<q=0<p<q=\infty then

σn(Tλ,)=(k=1n+1λkp)1/p.\sigma_{n}(T_{\lambda},\mathcal{E})=\bigg{(}\sum_{k=1}^{n+1}\lambda_{k}^{-p}\bigg{)}^{-1/p}.
(iv)

If 0<q<p=0<q<p=\infty and the series k=1λkq\sum_{k=1}^{\infty}\lambda_{k}^{q} converges then

σn(Tλ,)=(k=n+1λkq)1/q.\sigma_{n}(T_{\lambda},\mathcal{E})=\Bigg{(}\sum_{k=n+1}^{\infty}\lambda_{k}^{q}\Bigg{)}^{1/q}.
(v)

If p=q=p=q=\infty then

σn(Tλ,)=λn+1.\sigma_{n}(T_{\lambda},\mathcal{E})=\lambda_{n+1}.

As mentioned in Introduction, the exact values of σn(Tλ,)\sigma_{n}(T_{\lambda},\mathcal{E}), nn\in\mathbb{N}, in the case 0<pq0<p\leq q\leq\infty were obtained in [34, 35, 17] under the condition limkλk=0\lim_{k\to\infty}\lambda_{k}=0. To prove the above theorem, we need some auxiliary results.

Lemma 2.2.

Let 0<p<q<0<p<q<\infty and (λk)k=1(\lambda_{k})_{k=1}^{\infty} be a positive non-increasing sequence. Then for nn\in\mathbb{N}, there is n=n(n)n^{*}=n^{*}(n)\in\mathbb{N} such that

supm>n(mn)1/q(k=1mλkp)1/p=(nn)1/q(k=1nλkp)1/p.\sup_{m>n}\dfrac{(m-n)^{1/q}}{\big{(}\sum_{k=1}^{m}\lambda_{k}^{-p}\big{)}^{1/p}}=\dfrac{(n^{*}-n)^{1/q}}{\big{(}\sum_{k=1}^{n^{*}}\lambda_{k}^{-p}\big{)}^{1/p}}. (2.6)

Moreover, nn^{*} can be chosen as the smallest integer m>nm>n such that

(mn)1/q(k=1mλkp)1/p(m+1n)1/q(k=1m+1λkp)1/p.\dfrac{(m-n)^{1/q}}{\big{(}\sum_{k=1}^{m}\lambda_{k}^{-p}\big{)}^{1/p}}\geq\dfrac{(m+1-n)^{1/q}}{\big{(}\sum_{k=1}^{m+1}\lambda_{k}^{-p}\big{)}^{1/p}}.
Proof.

We first show that nn^{*} exists. The case limkλk=0\lim_{k\to\infty}\lambda_{k}=0 was already considered in [35]. We prove the case limkλk=K>0\lim_{k\to\infty}\lambda_{k}=K>0. We will show that there exists n0n_{0}\in\mathbb{N} such that

(mn)1/q(k=1mλkp)1/p<(2nn)1/q(k=12nλkp)1/p\dfrac{(m-n)^{1/q}}{\big{(}\sum_{k=1}^{m}\lambda_{k}^{-p}\big{)}^{1/p}}<\dfrac{(2n-n)^{1/q}}{\big{(}\sum_{k=1}^{2n}\lambda_{k}^{-p}\big{)}^{1/p}} (2.7)

for m>n0m>n_{0} and as a consequence we obtain (2.6) for some n{n+1,,n0}n^{*}\in\{n+1,\ldots,n_{0}\}. Observe that if m{jn+1,,(j+1)n}m\in\{jn+1,\ldots,(j+1)n\} for some jj\in\mathbb{N} we have

(mn)1/q(k=1mλkp)1/p<(jn)1/q(k=1jnλkp)1/p(jn)1/qλ11(jn)1/p.\displaystyle\dfrac{(m-n)^{1/q}}{\big{(}\sum_{k=1}^{m}\lambda_{k}^{-p}\big{)}^{1/p}}<\dfrac{(jn)^{1/q}}{\big{(}\sum_{k=1}^{jn}\lambda_{k}^{-p}\big{)}^{1/p}}\leq\dfrac{(jn)^{1/q}}{\lambda_{1}^{-1}(jn)^{1/p}}.

We also have

n1/qK1(2n)1/p(2nn)1/q(k=12nλkp)1/p.\displaystyle\dfrac{n^{1/q}}{K^{-1}(2n)^{1/p}}\leq\dfrac{(2n-n)^{1/q}}{\big{(}\sum_{k=1}^{2n}\lambda_{k}^{-p}\big{)}^{1/p}}.

Therefore

supm{jn+1,,(j+1)n}(mn)1/q(k=1mλkp)1/p<(2nn)1/q(k=12nλkp)1/p\sup_{m\in\{jn+1,\ldots,(j+1)n\}}\dfrac{(m-n)^{1/q}}{\big{(}\sum_{k=1}^{m}\lambda_{k}^{-p}\big{)}^{1/p}}<\dfrac{(2n-n)^{1/q}}{\big{(}\sum_{k=1}^{2n}\lambda_{k}^{-p}\big{)}^{1/p}}

if

(jn)1/qλ11(jn)1/p<n1/qK1(2n)1/pj>(λ121/pK)pqqp.\displaystyle\dfrac{(jn)^{1/q}}{\lambda_{1}^{-1}(jn)^{1/p}}<\dfrac{n^{1/q}}{K^{-1}(2n)^{1/p}}\quad\Longleftrightarrow\quad j>\bigg{(}\dfrac{\lambda_{1}2^{1/p}}{K}\bigg{)}^{\frac{pq}{q-p}}.

Denoting n0=(λ121/pK)pqqpnn_{0}=\big{\lceil}\big{(}\frac{\lambda_{1}2^{1/p}}{K}\big{)}^{\frac{pq}{q-p}}\big{\rceil}n we obtain (2.7) for m>n0m>n_{0} and (2.6) follows.

We turn to the second statement. Assume

m0n(k=1m0λkp)q/pm0+1n(k=1m0+1λkp)q/p\dfrac{m_{0}-n}{\big{(}\sum_{k=1}^{m_{0}}\lambda_{k}^{-p}\big{)}^{q/p}}\geq\dfrac{m_{0}+1-n}{\big{(}\sum_{k=1}^{m_{0}+1}\lambda_{k}^{-p}\big{)}^{q/p}} (2.8)

for some m0>nm_{0}>n, m0m_{0}\in\mathbb{N}. Such m0m_{0} exists by the first statement. We will prove

m0+1n(k=1m0+1λkp)q/pm0+2n(k=1m0+2λkp)q/p\dfrac{m_{0}+1-n}{\big{(}\sum_{k=1}^{m_{0}+1}\lambda_{k}^{-p}\big{)}^{q/p}}\geq\dfrac{m_{0}+2-n}{\big{(}\sum_{k=1}^{m_{0}+2}\lambda_{k}^{-p}\big{)}^{q/p}} (2.9)

by showing that

m0+1n(k=1m0+1λkp)q/pm0+2n(k=1m0+1λkp+λm0+1p)q/p.\dfrac{m_{0}+1-n}{\big{(}\sum_{k=1}^{m_{0}+1}\lambda_{k}^{-p}\big{)}^{q/p}}\geq\dfrac{m_{0}+2-n}{\big{(}\sum_{k=1}^{m_{0}+1}\lambda_{k}^{-p}+\lambda_{m_{0}+1}^{-p}\big{)}^{q/p}}\,. (2.10)

Putting A=k=1m0λkpA=\sum_{k=1}^{m_{0}}\lambda_{k}^{-p} and a=λm0+1pa=\lambda_{m_{0}+1}^{-p} we consider the function g(h)=m0n+h(A+ha)q/p.g(h)=\frac{m_{0}-n+h}{(A+ha)^{q/p}}\,. We have

g(h)=(A+ha)qpa(m0n+h)(A+ha)qp+1g^{\prime}(h)=\dfrac{(A+ha)-\frac{q}{p}a(m_{0}-n+h)}{(A+ha)^{\frac{q}{p}+1}}

and g(h)0g^{\prime}(h)\leq 0 if

(A+ha)qpa(m0n+h)0hAqpa(m0n)a(qp1).(A+ha)-\frac{q}{p}a(m_{0}-n+h)\leq 0\ \ \Longleftrightarrow\ \ h\geq\dfrac{A-\frac{q}{p}a(m_{0}-n)}{a(\frac{q}{p}-1)}.

Assume

Aqpa(m0n)a(qp1)1aA1qp(m0n)+qp1.\dfrac{A-\frac{q}{p}a(m_{0}-n)}{a(\frac{q}{p}-1)}\geq 1\ \ \Longleftrightarrow\ \ \frac{a}{A}\leq\frac{1}{\frac{q}{p}(m_{0}-n)+\frac{q}{p}-1}\,. (2.11)

Observe that the condition (2.8) implies (1+aA)q/p1+1m0n.\big{(}1+\frac{a}{A}\big{)}^{q/p}\geq 1+\frac{1}{m_{0}-n}. From this and (2.11) we get

(1+1qp(m0n)+qp1)q/p1+1m0n.\bigg{(}1+\frac{1}{\frac{q}{p}(m_{0}-n)+\frac{q}{p}-1}\bigg{)}^{q/p}\geq 1+\frac{1}{m_{0}-n}.

But this is a contradiction since φ(t)=(1+1t(m0n)+t1)t\varphi(t)=(1+\frac{1}{t(m_{0}-n)+t-1})^{t} is a strictly decreasing function on [1,+)[1,+\infty) and φ(1)=1+1m0n\varphi(1)=1+\frac{1}{m_{0}-n}. Consequently, gg is decreasing on [1,+)[1,+\infty). This proves (2.10) and (2.9) follows. ∎

Lemma 2.3.

Let (δk)k(\delta_{k})_{k\in\mathbb{N}} be a positive increasing sequence and limkδk=+\lim\limits_{k\to\infty}\delta_{k}=+\infty. Let nn\in\mathbb{N} and n=n(n)n_{*}=n_{*}(n) be the largest integer m>nm>n such that

(mn)δmk=1mδk.(m-n)\delta_{m}\leq\sum_{k=1}^{m}\delta_{k}. (2.12)

Then nn_{*} is finite and for any m{n+1,,n}m\in\{n+1,\ldots,n_{*}\} the inequality (2.12) holds true.

Proof.

First of all, observe that m=n+1m=n+1 satisfies (2.12). If mn+1m\geq n+1 and mm satisfies (2.12) we can write

m\displaystyle m n+δ1++δn+1δm+δn+2++δmδm\displaystyle\leq n+\frac{\delta_{1}+\ldots+\delta_{n+1}}{\delta_{m}}+\frac{\delta_{n+2}+\ldots+\delta_{m}}{\delta_{m}}
n+δ1++δn+1δm+mn1=m1+δ1++δn+1δm.\displaystyle\leq n+\frac{\delta_{1}+\ldots+\delta_{n+1}}{\delta_{m}}+m-n-1=m-1+\frac{\delta_{1}+\ldots+\delta_{n+1}}{\delta_{m}}.

Observe that the term δ1++δn+1δm\frac{\delta_{1}+\ldots+\delta_{n+1}}{\delta_{m}} tends to zero when mm\to\infty. Consequently nn_{*} is finite. Assume

(m0n)δm0>k=1m0δk(m_{0}-n)\delta_{m_{0}}>\sum_{k=1}^{m_{0}}\delta_{k} (2.13)

for some m0m_{0}\in\mathbb{N}, m0>nm_{0}>n. Then we have

m0+1>1δm0k=1m0δk+n+1>1δm0+1k=1m0δk+1+n=1δm0+1k=1m0+1δk+n.\displaystyle m_{0}+1>\frac{1}{\delta_{m_{0}}}\sum_{k=1}^{m_{0}}\delta_{k}+n+1>\frac{1}{\delta_{m_{0}+1}}\sum_{k=1}^{m_{0}}\delta_{k}+1+n=\frac{1}{\delta_{m_{0}+1}}\sum_{k=1}^{m_{0}+1}\delta_{k}+n.

This shows that the inequality (2.13) is satisfied with m0m_{0} being replaced by m0+1m_{0}+1 and therefore is satisfied with any m>m0m>m_{0}, mm\in\mathbb{N}. As a consequence we conclude that for any m{n+1,,n}m\in\{n+1,\ldots,n_{*}\} the inequality (2.12) holds true. ∎

We are now in position to prove Theorem 2.1.

Proof.

Step 1. Proof of (i). Given ε>0\varepsilon>0. For any (ξk)kBp(\xi_{k})_{k\in\mathbb{N}}\in B_{p} we take MM\in\mathbb{N} (depending on (ξk)k(\xi_{k})_{k\in\mathbb{N}}) such that

k=M+1|ξk|p<ε.\sum_{k=M+1}^{\infty}|\xi_{k}|^{p}<\varepsilon.

Then we have

infΓn(kΓn|λkξk|q)\displaystyle\inf_{\Gamma_{n}}\bigg{(}\sum_{k\not\in\Gamma_{n}}|\lambda_{k}\xi_{k}|^{q}\bigg{)} infΓnM(kΓnM|λkξk|q)=infΓnM(k{1,,M}\ΓnM|λkξk|q)+k=M+1|λkξk|q.\displaystyle\leq\inf_{\Gamma_{n}^{M}}\bigg{(}\sum_{k\not\in\Gamma_{n}^{M}}|\lambda_{k}\xi_{k}|^{q}\bigg{)}=\inf_{\Gamma_{n}^{M}}\bigg{(}\sum_{k\in\{1,\ldots,M\}\backslash\Gamma_{n}^{M}}|\lambda_{k}\xi_{k}|^{q}\bigg{)}+\sum_{k=M+1}^{\infty}|\lambda_{k}\xi_{k}|^{q}. (2.14)

The first term on the right side can be estimated as follows

infΓnM(k{1,,M}\ΓnM|λkξk|q)sup(γk)k=1MBpMinfΓnM(k{1,,M}\ΓnM|λkγk|q)=σn(TλM,M)q,\inf_{\Gamma_{n}^{M}}\bigg{(}\sum_{k\in\{1,\ldots,M\}\backslash\Gamma_{n}^{M}}|\lambda_{k}\xi_{k}|^{q}\bigg{)}\leq\sup_{(\gamma_{k})_{k=1}^{M}\in B_{p}^{M}}\inf_{\Gamma_{n}^{M}}\bigg{(}\sum_{k\in\{1,\ldots,M\}\backslash\Gamma_{n}^{M}}|\lambda_{k}\gamma_{k}|^{q}\bigg{)}=\sigma_{n}(T_{\lambda}^{M},\mathcal{E}_{M})^{q}, (2.15)

see (2.3). It has been proved in [16] that

σn(TλM,M)=supn<mM(mn)1/q(k=1mλkp)1/p.\sigma_{n}(T^{M}_{\lambda},\mathcal{E}_{M})=\sup_{n<m\leq M}\dfrac{(m-n)^{1/q}}{\big{(}\sum_{k=1}^{m}\lambda_{k}^{-p}\big{)}^{1/p}}. (2.16)

Hence we get

infΓnM(k{1,,M}\ΓnM|λkξk|q)\displaystyle\inf_{\Gamma_{n}^{M}}\bigg{(}\sum_{k\in\{1,\ldots,M\}\backslash\Gamma_{n}^{M}}|\lambda_{k}\xi_{k}|^{q}\bigg{)} supn<mMmn(k=1mλkp)q/psupn<mmn(k=1mλkp)q/p.\displaystyle\leq\sup_{n<m\leq M}\dfrac{m-n}{\big{(}\sum_{k=1}^{m}\lambda_{k}^{-p}\big{)}^{q/p}}\leq\sup_{n<m}\dfrac{m-n}{\big{(}\sum_{k=1}^{m}\lambda_{k}^{-p}\big{)}^{q/p}}.

Since 0<pq0<p\leq q\leq\infty, for the second term we have

k=M+1|λkξk|q(k=M+1|λkξk|p)q/p\displaystyle\sum_{k=M+1}^{\infty}|\lambda_{k}\xi_{k}|^{q}\leq\bigg{(}\sum_{k=M+1}^{\infty}|\lambda_{k}\xi_{k}|^{p}\bigg{)}^{q/p} supk>M|λk|q(k=M+1|ξk|p)q/pλ1qεq/p.\displaystyle\leq\sup_{k>M}|\lambda_{k}|^{q}\bigg{(}\sum_{k=M+1}^{\infty}|\xi_{k}|^{p}\bigg{)}^{q/p}\leq\lambda_{1}^{q}\varepsilon^{q/p}.

Consequently we obtain

infΓn(kΓn|λkξk|q)\displaystyle\inf_{\Gamma_{n}}\bigg{(}\sum_{k\not\in\Gamma_{n}}|\lambda_{k}\xi_{k}|^{q}\bigg{)} supm>nmn(k=1mλkp)q/p+λ1qεq/p.\displaystyle\leq\sup_{m>n}\dfrac{m-n}{\big{(}\sum_{k=1}^{m}\lambda_{k}^{-p}\big{)}^{q/p}}+\lambda_{1}^{q}\varepsilon^{q/p}.

Observe that the right-hand side is independent of (ξk)kBp(\xi_{k})_{k\in\mathbb{N}}\in B_{p} and ε>0\varepsilon>0 is arbitrarily small. In view of (2.2) we obtain the upper bound.

We now give a proof for the lower bound. Take MM\in\mathbb{N} arbitrarily large and consider the following diagram

pM@ >TλM>>qMJQp()@ >Tλ>>q(),\begin{CD}\ell_{p}^{M}@ >T_{\lambda}^{M}>>\ell_{q}^{M}\\ @V{}V{J}V@A{}A{Q}A\\ \ell_{p}(\mathbb{N})@ >T_{\lambda}>>\ell_{q}(\mathbb{N})\,,\end{CD}

where

J(ξ1,,ξM)=(ξ1,,ξM,0,0,)\displaystyle J(\xi_{1},\ldots,\xi_{M})=(\xi_{1},\ldots,\xi_{M},0,0,\ldots)
Q(ξ1,,ξM,ξM+1,)=(ξ1,,ξM).\displaystyle Q(\xi_{1},\ldots,\xi_{M},\xi_{M+1},\ldots)=(\xi_{1},\ldots,\xi_{M}).

We have TλM=QTλJT_{\lambda}^{M}=QT_{\lambda}J and J=Q=1\|J\|=\|Q\|=1 which by property (2.1) implies

σn(TλM,M)Jσn(Tλ,)Q=σn(Tλ,).\sigma_{n}(T^{M}_{\lambda},\mathcal{E}_{M})\leq\|J\|\cdot\sigma_{n}(T_{\lambda},\mathcal{E})\cdot\|Q\|=\sigma_{n}(T_{\lambda},\mathcal{E}).

Using (2.16) again we deduce

supn<mM(mn)1/q(k=1mλkp)1/pσn(Tλ,).\sup_{n<m\leq M}\dfrac{(m-n)^{1/q}}{\big{(}\sum_{k=1}^{m}\lambda_{k}^{-p}\big{)}^{1/p}}\leq\sigma_{n}(T_{\lambda},\mathcal{E}).

Since MM is arbitrarily large, we obtain the lower bound. The second statement follows from Lemma 2.2.
Step 2. Proof of (ii). First note that nn_{*} is finite by Lemma 2.3. Let ε>0\varepsilon>0. We choose M>nM>n_{*} such that

(k=M+1λkpqpq)pqp<ε.\bigg{(}\sum_{k=M+1}^{\infty}\lambda_{k}^{\frac{pq}{p-q}}\bigg{)}^{\frac{p-q}{p}}<\varepsilon.

For (ξk)kBp(\xi_{k})_{k\in\mathbb{N}}\in B_{p}, we use the estimate (2.14). Applying Hölder’s inequality we get

k=M+1|λkξk|q(k=M+1λkpqpq)pqp(k=M+1|ξk|p)qp<ε\displaystyle\sum_{k=M+1}^{\infty}|\lambda_{k}\xi_{k}|^{q}\leq\bigg{(}\sum_{k=M+1}^{\infty}\lambda_{k}^{\frac{pq}{p-q}}\bigg{)}^{\frac{p-q}{p}}\bigg{(}\sum_{k=M+1}^{\infty}|\xi_{k}|^{p}\bigg{)}^{{\frac{q}{p}}}<\varepsilon

which by (2.14) implies

infΓn(kΓn|λkξk|q)\displaystyle\inf_{\Gamma_{n}}\bigg{(}\sum_{k\not\in\Gamma_{n}}|\lambda_{k}\xi_{k}|^{q}\bigg{)} infΓnM(k{1,,M}\ΓnM|λkξk|q)+ε=σn(TλM,M)q+ε,\displaystyle\leq\inf_{\Gamma_{n}^{M}}\bigg{(}\sum_{k\in\{1,\ldots,M\}\backslash\Gamma_{n}^{M}}|\lambda_{k}\xi_{k}|^{q}\bigg{)}+\varepsilon=\sigma_{n}(T^{M}_{\lambda},\mathcal{E}_{M})^{q}+\varepsilon,

see (2.15). Using the result in [16] for the case 0<q<p<0<q<p<\infty

σn(TλM,M)\displaystyle\sigma_{n}(T^{M}_{\lambda},\mathcal{E}_{M}) =((nn)ppq(k=1nλkp)qpq+k=n+1Mλkpqpq)pqpq((nn)ppq(k=1nλkp)qpq+k=n+1λkpqpq)pqpq\displaystyle=\Bigg{(}\dfrac{(n_{*}-n)^{\frac{p}{p-q}}}{\big{(}\sum_{k=1}^{n_{*}}\lambda_{k}^{-p}\big{)}^{\frac{q}{p-q}}}+\sum_{k=n_{*}+1}^{M}\lambda_{k}^{\frac{pq}{p-q}}\Bigg{)}^{\frac{p-q}{pq}}\leq\Bigg{(}\dfrac{(n_{*}-n)^{\frac{p}{p-q}}}{\big{(}\sum_{k=1}^{n_{*}}\lambda_{k}^{-p}\big{)}^{\frac{q}{p-q}}}+\sum_{k=n_{*}+1}^{\infty}\lambda_{k}^{\frac{pq}{p-q}}\Bigg{)}^{\frac{p-q}{pq}}

and following the argument as in Step 1 we obtain the upper bound. The lower bound is carried out similarly as Step 1 with M>nM>n_{*}. The other cases are proved similarly with a slight modification. ∎

3 Best nn-term approximation of function classes Fω,p(𝕋d)F_{\omega,p}({\mathbb{T}}^{d})

Let 𝕋d{\mathbb{T}}^{d} be the dd-dimensional torus. We equip 𝕋d{\mathbb{T}}^{d} with the probability measure (2π)ddx(2\pi)^{-d}\mathrm{d}x. In this section we study the asymptotic constants of best nn-term approximation widths of embeddings of the weighted function classes Fω,p(𝕋d)F_{\omega,p}({\mathbb{T}}^{d}) by trigonometric system 𝒯d\mathcal{T}^{d}. For a function fL1(𝕋d)f\in L_{1}({\mathbb{T}}^{d}), its Fourier coefficients are defined as

f^(k):=(2π)d𝕋df(x)eikxdx,kd.\hat{f}(k):=(2\pi)^{-d}\int_{{\mathbb{T}}^{d}}f(x)e^{-{\rm i}kx}\mathrm{d}x,\qquad k\in\mathbb{Z}^{d}.

Hence, it holds for any fL2(𝕋d)f\in L_{2}({\mathbb{T}}^{d}) that

fL2(𝕋d)2=(2π)d𝕋d|f(x)|2dx=kd|f^(k)|2.\|f\|_{L_{2}({\mathbb{T}}^{d})}^{2}=(2\pi)^{-d}\int_{{\mathbb{T}}^{d}}|f(x)|^{2}\mathrm{d}x=\sum_{k\in\mathbb{Z}^{d}}|\hat{f}(k)|^{2}\,.

Let ω=(ω(k))kd\omega=(\omega(k))_{k\in\mathbb{Z}^{d}} be a sequence of positive numbers. Those sequences we will call a weight in what follows. For 0<p0<p\leq\infty we introduce the class Fω,p(𝕋d)F_{\omega,p}({\mathbb{T}}^{d}) as the collection of all functions fL1(𝕋d)f\in L_{1}({\mathbb{T}}^{d}) such that

fFω,p(𝕋d):=(kd|ω(k)f^(k)|p)1/p<.\|f\|_{F_{\omega,p}({\mathbb{T}}^{d})}:=\bigg{(}\sum_{k\in\mathbb{Z}^{d}}|\omega(k)\hat{f}(k)|^{p}\bigg{)}^{1/p}<\infty\,.

When ω(k)=1\omega(k)=1 for all kdk\in\mathbb{Z}^{d} we use the notation Fp(𝕋d)F_{p}({\mathbb{T}}^{d}) instead of Fω,p(𝕋d)F_{\omega,p}({\mathbb{T}}^{d}). In this case we get back the space L2(𝕋d)L_{2}({\mathbb{T}}^{d}) when p=2p=2 and the classical Wiener algebra 𝒜(𝕋d)\mathcal{A}({\mathbb{T}}^{d}) when p=1p=1.

We suppose that

lim|k1|++|kd|ω(k)=+,k=(k1,,kd).\lim_{|k_{1}|+\ldots+|k_{d}|\to\infty}\omega(k)=+\infty,\quad k=(k_{1},\ldots,k_{d}). (3.1)

In what follows we denote the non-increasing rearrangement of the sequence (1/ω(k))kd(1/\omega(k))_{k\in\mathbb{Z}^{d}} by λ=(λn)n\lambda=(\lambda_{n})_{n\in\mathbb{N}}. Observe that id:Fω,2(𝕋d)L2(𝕋d)id:F_{\omega,2}({\mathbb{T}}^{d})\to L_{2}({\mathbb{T}}^{d}) is compact if and only if limnλn=0.\lim_{n\to\infty}\,\lambda_{n}=0. In fact we have

λn=an(id:Fω,2(𝕋d)L2(𝕋d)),\lambda_{n}=a_{n}(id:F_{\omega,2}({\mathbb{T}}^{d})\to L_{2}({\mathbb{T}}^{d})),

where an(id:Fω,2(𝕋d)L2(𝕋d))a_{n}(id:F_{\omega,2}({\mathbb{T}}^{d})\to L_{2}({\mathbb{T}}^{d})) is the nn-th approximation number (linear width) of the operator id:Fω,2(𝕋d)L2(𝕋d)id:F_{\omega,2}({\mathbb{T}}^{d})\to L_{2}({\mathbb{T}}^{d}), see [25]. Recall that for two Banach spaces XX, YY and T(X,Y)T\in{\mathcal{L}}(X,Y), the nn-th approximation number of TT is defined as

an(T):=inf{TA:XY:A(X,Y),rank(A)<n},n.a_{n}(T):=\inf\big{\{}\|T-A:X\to Y\|:\ A\in\mathcal{L}(X,Y),\ \ \text{rank}(A)<n\big{\}}\,,\quad n\in\mathbb{N}\,.

Basic properties of this quantity can be found in [29, 30].

We have the following embedding property of the class Fω,p(𝕋d)F_{\omega,p}({\mathbb{T}}^{d}).

Lemma 3.1.

Let 0<p,q0<p,q\leq\infty and ω=(ω(k))kd\omega=(\omega(k))_{k\in\mathbb{Z}^{d}} be a weight satisfying (3.1). Then the operator id:Fω,p(𝕋d)Fq(𝕋d)id:F_{\omega,p}({\mathbb{T}}^{d})\hookrightarrow F_{q}({\mathbb{T}}^{d}) is continuous if either pqp\leq q or q<pq<p and the series kdω(k)pqpq\sum_{k\in\mathbb{Z}^{d}}\omega(k)^{-\frac{pq}{p-q}} converges.

Proof.

If q<pq<p and fFω,p(𝕋d)f\in F_{\omega,p}({\mathbb{T}}^{d}), applying Hölder’s inequality we get

(kd|f^(k)|q)1q(kdω(k)pqpq)pqpq(kd|ω(k)f^(k)|p)1p.\displaystyle\bigg{(}\sum_{k\in\mathbb{Z}^{d}}|\hat{f}(k)|^{q}\bigg{)}^{\frac{1}{q}}\leq\bigg{(}\sum_{k\in\mathbb{Z}^{d}}\omega(k)^{-\frac{pq}{p-q}}\bigg{)}^{\frac{p-q}{pq}}\bigg{(}\sum_{k\in\mathbb{Z}^{d}}|\omega(k)\hat{f}(k)|^{p}\bigg{)}^{{\frac{1}{p}}}.

This proves the case q<pq<p. The case pqp\leq q is obvious. ∎

Our result for the best nn-term approximation of the embedding Fω,p(𝕋d)Fq(𝕋d)F_{\omega,p}({\mathbb{T}}^{d})\to F_{q}({\mathbb{T}}^{d}) by the trigonometric system 𝒯d\mathcal{T}^{d} reads as follows.

Theorem 3.2.

Let 0<p,q0<p,q\leq\infty and ω=(ω(k))kd\omega=(\omega(k))_{k\in\mathbb{Z}^{d}} be a weight satisfying conditions in Lemma 3.1. Then we have

σn(id:Fω,p(𝕋d)Fq(𝕋d),𝒯d)=σn(Tλ:p()q(),),n,\sigma_{n}\big{(}id:F_{\omega,p}({\mathbb{T}}^{d})\to F_{q}({\mathbb{T}}^{d}),\mathcal{T}^{d}\big{)}=\sigma_{n}\big{(}T_{\lambda}:\ell_{p}(\mathbb{N})\to\ell_{q}(\mathbb{N}),\mathcal{E}\big{)},\quad n\in\mathbb{N},

where the value of σn(Tλ,)\sigma_{n}(T_{\lambda},\mathcal{E}) is given as in Theorem 2.1.

Proof.

We consider the following commutative diagram

Fω,p(𝕋d)@ >id>>Fq(𝕋d)ABp(d)@ >Dω>>q(d),\begin{CD}F_{\omega,p}({\mathbb{T}}^{d})@ >id>>F_{q}({\mathbb{T}}^{d})\\ @V{}V{A}V@A{}A{B}A\\ \ell_{p}(\mathbb{Z}^{d})@ >D_{\omega}>>\ell_{q}(\mathbb{Z}^{d})\,,\end{CD}

where the linear operators AA, BB and DωD_{\omega} are defined as

Af\displaystyle Af :=(ω(k)f^(k))kd,\displaystyle:=(\omega(k)\hat{f}(k))_{k\in\mathbb{Z}^{d}}\,,
Dωξ\displaystyle D_{\omega}\xi :=(ξ(k)/ω(k))kd,ξ=(ξ(k))kd\displaystyle:=(\xi(k)/\omega(k))_{k\in\mathbb{Z}^{d}}\,,\qquad\xi=(\xi(k))_{k\in\mathbb{Z}^{d}}
(Bξ)(x)\displaystyle(B\xi)(x) :=kdξkeikx,x𝕋d.\displaystyle:=\sum_{k\in\mathbb{Z}^{d}}\xi_{k}\,e^{{\rm i}kx}\,,\qquad x\in{\mathbb{T}}^{d}\,.

It is obvious that A=B=1\|A\|=\|B\|=1. Let d:={ek:kd}\mathcal{E}^{d}:=\{e_{k}:k\in\mathbb{Z}^{d}\} where ek=(δk,l)lde_{k}=(\delta_{k,l})_{l\in\mathbb{Z}^{d}}. By the property (2.1) and the identity id=BDωAid=B\,D_{\omega}\,A it follows

σn(id:Fω,p(𝕋d)Fq(𝕋d),𝒯d)σn(Dω:p(d)q(d),d),n.\sigma_{n}\big{(}id:F_{\omega,p}({\mathbb{T}}^{d})\to F_{q}({\mathbb{T}}^{d}),\mathcal{T}^{d}\big{)}\leq\sigma_{n}\big{(}D_{\omega}:\ell_{p}(\mathbb{Z}^{d})\to\ell_{q}(\mathbb{Z}^{d}),\mathcal{E}^{d}\big{)},\qquad n\in\mathbb{N}\,.

From the fact that

σn(Dω:p(d)q(d),d)=σn(Tλ:p()q(),)\sigma_{n}\big{(}D_{\omega}:\ell_{p}(\mathbb{Z}^{d})\to\ell_{q}(\mathbb{Z}^{d}),\mathcal{E}^{d}\big{)}=\sigma_{n}\big{(}T_{\lambda}:\ell_{p}(\mathbb{N})\to\ell_{q}(\mathbb{N}),\mathcal{E}\big{)} (3.2)

we obtain the estimate from above. Now we employ the same type of arguments with respect to the diagram

p(d)@ >Dω>>q(d)A1B1Fω,p(𝕋d)@ >id>>Fq(𝕋d).\begin{CD}\ell_{p}(\mathbb{Z}^{d})@ >D_{\omega}>>\ell_{q}(\mathbb{Z}^{d})\\ @V{}V{A^{-1}}V@A{}A{B^{-1}}A\\ F_{\omega,p}({\mathbb{T}}^{d})@ >id>>F_{q}({\mathbb{T}}^{d})\,.\end{CD}

It is easy to see that the operators AA and BB are invertible and that A1=B1=1\|A^{-1}\|=\|B^{-1}\|=1. As above we conclude

σn(Dω:p(d)q(d),d)σn(id:Fω,p(𝕋d)Fq(𝕋d),𝒯d),n.\sigma_{n}\big{(}D_{\omega}:\ell_{p}(\mathbb{Z}^{d})\to\ell_{q}(\mathbb{Z}^{d}),\mathcal{E}^{d}\big{)}\leq\sigma_{n}\big{(}id:F_{\omega,p}({\mathbb{T}}^{d})\to F_{q}({\mathbb{T}}^{d}),\mathcal{T}^{d}\big{)},\qquad n\in\mathbb{N}\,.

Now the estimate from below follows from (3.2). ∎

We need following auxiliary results.

Lemma 3.3.
(i)

Let s>0s>0, a>1a>1, and β0\beta\geq 0. Then we have

limnan1ys(lnnln(yn))βdy=1s+1.\lim\limits_{n\to\infty}\,\int_{\frac{a}{n}}^{1}y^{s}\bigg{(}\frac{\ln n}{\ln(yn)}\bigg{)}^{\beta}\mathrm{d}y=\frac{1}{s+1}.
(ii)

Let s>1s>1, β0\beta\geq 0. Then we have

limn1+1ts(ln(nt)lnn)βdt=1s1.\lim_{n\to\infty}\int_{1}^{+\infty}\dfrac{1}{t^{s}}\bigg{(}\frac{\ln(nt)}{\ln n}\bigg{)}^{\beta}\mathrm{d}t=\dfrac{1}{s-1}.
Proof.

The first statement was proved in [26]. We prove the second one with concentration on the case β>0\beta>0 since the case β=0\beta=0 is obvious. We consider the sequence of functions

fn(t)=1ts(ln(nt)lnn)β,t1,n.f_{n}(t)=\dfrac{1}{t^{s}}\bigg{(}\frac{\ln(nt)}{\ln n}\bigg{)}^{\beta},\ \ \ t\geq 1,\ \ n\in\mathbb{N}.

Clearly, this sequence converges pointwise to f(t)=1tsf(t)=\frac{1}{t^{s}}. For n3n\geq 3, from the inequality (x+y)βCβ(xβ+yβ)(x+y)^{\beta}\leq C_{\beta}(x^{\beta}+y^{\beta}), for some Cβ>0C_{\beta}>0, we derive:

fn(t)=1ts(1+lntlnn)β<1ts(1+lnt)βCβ1ts(1+(lnt)β):=g(t).f_{n}(t)=\frac{1}{t^{s}}\bigg{(}1+\dfrac{\ln t}{\ln n}\bigg{)}^{\beta}<\frac{1}{t^{s}}\big{(}1+\ln t\big{)}^{\beta}\leq C_{\beta}\dfrac{1}{t^{s}}\big{(}1+(\ln t)^{\beta}\big{)}:=g(t).

Since g(t)g(t) is integrable on [1,+)[1,+\infty), the desired result follows from Lebesgue’s dominated convergence theorem. ∎

The asymptotic constants of best nn-term approximation widths of embeddings of the classes Fω,p(𝕋d)F_{\omega,p}({\mathbb{T}}^{d}) in Fq(𝕋d)F_{q}({\mathbb{T}}^{d}) are given in the following theorem.

Theorem 3.4.

Let s>0s>0, β0\beta\geq 0 and let ω\omega be a given weight. Assume that there exists CC\in\mathbb{R} such that

limnλnns(lnn)β=limnan(id:Fω,2(𝕋d)L2(𝕋d))ns(lnn)β=C.\lim_{n\to\infty}\,\frac{\lambda_{n}}{n^{-s}(\ln n)^{\beta}\,}=\lim_{n\to\infty}\,\frac{a_{n}\big{(}id:F_{\omega,2}({\mathbb{T}}^{d})\to L_{2}({\mathbb{T}}^{d})\big{)}}{n^{-s}(\ln n)^{\beta}}=C\,. (3.3)
(i)

If 0<pq0<p\leq q\leq\infty we have

limnσn(id:Fω,p(𝕋d)Fq(𝕋d),𝒯d)ns1p+1q(lnn)β=(s+1p1q)s+1p1q(s+1p)sp1pq1qC.\lim\limits_{n\to\infty}\frac{\sigma_{n}\big{(}id:F_{\omega,p}({\mathbb{T}}^{d})\to F_{q}({\mathbb{T}}^{d}),\mathcal{T}^{d}\big{)}}{n^{-s-\frac{1}{p}+\frac{1}{q}}(\ln n)^{\beta}}=\frac{(s+\frac{1}{p}-\frac{1}{q})^{s+\frac{1}{p}-\frac{1}{q}}}{(s+\frac{1}{p})^{s}}\frac{p^{\frac{1}{p}}}{q^{\frac{1}{q}}}C\,.

If q=q=\infty and/or p=p=\infty, the asymptotic constant is understood as the limit of the right-hand side when qq\to\infty and/or pp\to\infty.

(ii)

If 0<q<p<0<q<p<\infty and s>1q1ps>\frac{1}{q}-\frac{1}{p} we have

limnσn(id:Fω,p(𝕋d)Fq(𝕋d),𝒯d)ns1p+1q(lnn)β=(ss+1p)s(1qs+1p1q)1q1pC.\lim\limits_{n\to\infty}\frac{\sigma_{n}\big{(}id:F_{\omega,p}({\mathbb{T}}^{d})\to F_{q}({\mathbb{T}}^{d}),\mathcal{T}^{d}\big{)}}{n^{-s-\frac{1}{p}+\frac{1}{q}}(\ln n)^{\beta}}=\bigg{(}\frac{s}{s+\frac{1}{p}}\bigg{)}^{s}\bigg{(}\dfrac{\frac{1}{q}}{s+\frac{1}{p}-\frac{1}{q}}\bigg{)}^{\frac{1}{q}-\frac{1}{p}}C.
Proof.

We prove the case 0<p,q<0<p,q<\infty. The cases p=p=\infty and/or q=q=\infty are carried out similarly with slight modification. In this proof, for simplicity we denote

σn:=σn(id:Fω,p(𝕋d)Fq(𝕋d),𝒯d).\sigma_{n}:=\sigma_{n}\big{(}id:F_{\omega,p}({\mathbb{T}}^{d})\to F_{q}({\mathbb{T}}^{d}),\mathcal{T}^{d}\big{)}.

Step 1. We need some preparations. Assumption (3.3) indicates that for any ε>0\varepsilon>0 there exists n1:=n1(ε)n_{1}:=n_{1}(\varepsilon)\in\mathbb{N} such that for k>n1k>n_{1} we have

|λkks(lnk)βC|εCελkks(lnk)βε+C.\bigg{|}\frac{\lambda_{k}}{k^{-s}(\ln k)^{\beta}}-C\bigg{|}\leq\varepsilon\quad\Longleftrightarrow\quad C-\varepsilon\leq\frac{\lambda_{k}}{k^{-s}(\ln k)^{\beta}}\leq\varepsilon+C. (3.4)

Denote by n2=n2(p,s)n_{2}=n_{2}(p,s)\in\mathbb{N} from which the function ψ(t)=tps(lnt)pβ\psi(t)=t^{ps}(\ln t)^{-p\beta} is increasing. Then for m>n0:=max{n1,n2}m>n_{0}:=\max\{n_{1},n_{2}\} we have

k=1mλkp=k=1n0λkp+k=n0+1mλkpk=1n0λkp+1(Cε)pk=n0+1mkps(lnk)pβ.\sum_{k=1}^{m}\lambda_{k}^{-p}=\sum_{k=1}^{n_{0}}\lambda_{k}^{-p}+\sum_{k=n_{0}+1}^{m}\lambda_{k}^{-p}\leq\sum_{k=1}^{n_{0}}\lambda_{k}^{-p}+\dfrac{1}{(C-\varepsilon)^{p}}\sum_{k=n_{0}+1}^{m}k^{ps}(\ln k)^{-p\beta}\,.

Estimating the summation by an integral and afterwards changing variable y=tm+1y=\frac{t}{m+1} we find

k=n0+1mkps(lnk)pβ\displaystyle\sum_{k=n_{0}+1}^{m}k^{ps}(\ln k)^{-p\beta} n0+1m+1tps(lnt)pβdt=(m+1)ps+1(ln(m+1))pβn0+1m+11yps(ln(m+1)ln(y(m+1)))pβdy.\displaystyle\leq\int_{n_{0}+1}^{m+1}t^{ps}(\ln t)^{-p\beta}\mathrm{d}t=\frac{(m+1)^{ps+1}}{(\ln(m+1))^{p\beta}}\int_{\frac{n_{0}+1}{m+1}}^{1}y^{ps}\bigg{(}\frac{\ln(m+1)}{\ln(y(m+1))}\bigg{)}^{p\beta}\mathrm{d}y\,.

By Lemma 3.3 (i) we can choose n3>n0n_{3}>n_{0} such that for mn3m\geq n_{3} we have

n0+1m+11yps(ln(m+1)ln(ym+y))pβdy1+εps+1and(ln(m+1))pβ(m+1)ps+1k=1n0λkpε\int_{\frac{n_{0}+1}{m+1}}^{1}y^{ps}\bigg{(}\frac{\ln(m+1)}{\ln(ym+y)}\bigg{)}^{p\beta}\mathrm{d}y\leq\frac{1+\varepsilon}{ps+1}\qquad\text{and}\qquad\frac{(\ln(m+1))^{p\beta}}{(m+1)^{ps+1}}\sum_{k=1}^{n_{0}}\lambda_{k}^{-p}\leq\varepsilon

which leads to

k=1mλkp(m+1)ps+1(ln(m+1))pβ(ε+1+ε(Cε)p(ps+1))\sum_{k=1}^{m}\lambda_{k}^{-p}\leq\frac{(m+1)^{ps+1}}{(\ln(m+1))^{p\beta}}\bigg{(}\varepsilon+\frac{1+\varepsilon}{(C-\varepsilon)^{p}(ps+1)}\bigg{)} (3.5)

for mn3m\geq n_{3}. Similarly we have

k=n0+1mkps(lnk)pβ\displaystyle\sum_{k=n_{0}+1}^{m}k^{ps}(\ln k)^{-p\beta} n0mtps(lnt)pβdtmps+1(lnm)pβn0m1yps(lnmln(ym))pβdymps+1(lnm)pβ1εps+1,\displaystyle\geq\int_{n_{0}}^{m}\frac{t^{ps}}{(\ln t)^{p\beta}}\mathrm{d}t\geq\frac{m^{ps+1}}{(\ln m)^{p\beta}}\int_{\frac{n_{0}}{m}}^{1}y^{ps}\bigg{(}\frac{\ln m}{\ln(ym)}\bigg{)}^{p\beta}\mathrm{d}y\geq\frac{m^{ps+1}}{(\ln m)^{p\beta}}\frac{1-\varepsilon}{ps+1}\,,

which implies

k=1mλkpk=n0+1mλkp1(C+ε)pk=n0+1mkps(lnk)pβmps+1(lnm)pβ1ε(C+ε)p(ps+1).\sum_{k=1}^{m}\lambda_{k}^{-p}\geq\sum_{k=n_{0}+1}^{m}\lambda_{k}^{-p}\geq\dfrac{1}{(C+\varepsilon)^{p}}\sum_{k=n_{0}+1}^{m}k^{ps}(\ln k)^{-p\beta}\geq\frac{m^{ps+1}}{(\ln m)^{p\beta}}\cdot\frac{1-\varepsilon}{(C+\varepsilon)^{p}(ps+1)}. (3.6)

Step 2. Proof of the case 0<pq<0<p\leq q<\infty. From (3.6) we get

mn(k=1mλkp)qp\displaystyle\frac{m-n}{\big{(}\sum_{k=1}^{m}\lambda_{k}^{-p}\big{)}^{\frac{q}{p}}} (mn)(lnm)qβmqs+qp((C+ε)p(ps+1)1ε)qp.\displaystyle\leq\frac{(m-n)(\ln m)^{q\beta}}{m^{qs+\frac{q}{p}}}\bigg{(}\frac{(C+\varepsilon)^{p}(ps+1)}{1-\varepsilon}\bigg{)}^{\frac{q}{p}}\,.

Considering the function g(t):=tntqs+qp(lnt)qβ,t[n,),g(t):=\frac{t-n}{t^{qs+\frac{q}{p}}}(\ln t)^{q\beta},\ t\in[n,\infty)\,, we have

g(t)=(t(qs+qp1)+n(qs+qp)tqs+qp+1)(lnt)qβ+(tntqs+qp+1)qβ(lnt)qβ1.g^{\prime}(t)=\bigg{(}\frac{-t\big{(}qs+\frac{q}{p}-1\big{)}+n\big{(}qs+\frac{q}{p}\big{)}}{t^{qs+\frac{q}{p}+1}}\bigg{)}(\ln t)^{q\beta}+\bigg{(}\frac{t-n}{t^{qs+\frac{q}{p}+1}}\bigg{)}q\beta(\ln t)^{q\beta-1}\,.

We put

f(t):=[t(qs+qp1)+n(qs+qp)]lnt+(tn)qβ,t[n,).\displaystyle f(t):=\bigg{[}-t\bigg{(}qs+\frac{q}{p}-1\bigg{)}+n\bigg{(}qs+\frac{q}{p}\bigg{)}\bigg{]}\ln t+\big{(}t-n\big{)}q\beta\,,\qquad t\in[n,\infty).

Then g(t)=0g^{\prime}(t)=0 is equivalent to f(t)=0f(t)=0. We have

f(t)\displaystyle f^{\prime}(t) =(qs+qp1)(lnt+1)+n(qs+qp)t+qβ<(qs+qp1)lnt+1+qβ.\displaystyle=-\Big{(}qs+\frac{q}{p}-1\Big{)}(\ln t+1)+\frac{n(qs+\frac{q}{p})}{t}+q\beta<-\Big{(}qs+\frac{q}{p}-1\Big{)}\ln t+1+q\beta\,.

This implies f(t)<0f^{\prime}(t)<0 if t>e(1+qβ)/(qs+q/p1)t>e^{(1+q\beta)/(qs+q/p-1)}. Observe that

f(n+nqs+qp1)>0,f(n+2nqs+qp1)<0and\displaystyle f\bigg{(}n+\frac{n}{qs+\frac{q}{p}-1}\bigg{)}>0,\qquad f\bigg{(}n+\frac{2n}{qs+\frac{q}{p}-1}\bigg{)}<0\quad\text{and}\quad

for nn4n\geq n_{4} depending only on p,q,sp,q,s and β\beta. Consequently the equation f(t)=0f(t)=0 (or g(t)=0g^{\prime}(t)=0) has a unique solution belonging to the interval In:=[n+nqs+qp1,n+2nqs+qp1].I_{n}:=\big{[}n+\frac{n}{qs+\frac{q}{p}-1},\ n+\frac{2n}{qs+\frac{q}{p}-1}\big{]}. From this we deduce

σnq=supmn(mn(k=1mλkp)qp)\displaystyle\sigma_{n}^{q}=\sup_{m\geq n}\Bigg{(}\frac{m-n}{\big{(}\sum_{k=1}^{m}\lambda_{k}^{-p}\big{)}^{\frac{q}{p}}}\Bigg{)} suptIn((tn)(lnt)qβtqs+qp)((C+ε)p(ps+1)1ε)qp\displaystyle\leq\sup_{t\in I_{n}}\bigg{(}\frac{(t-n)(\ln t)^{q\beta}}{t^{qs+\frac{q}{p}}}\bigg{)}\bigg{(}\frac{(C+\varepsilon)^{p}(ps+1)}{1-\varepsilon}\bigg{)}^{\frac{q}{p}}

which leads to

σnqn1qsqp(lnn)qβ\displaystyle\frac{\sigma_{n}^{q}}{n^{1-qs-\frac{q}{p}}(\ln n)^{q\beta}} suptIn((tn)(lnt)qβn(tn1)qs+qp(lnn)qβ)((C+ε)p(ps+1)1ε)qp\displaystyle\leq\sup_{t\in I_{n}}\bigg{(}\frac{(t-n)(\ln t)^{q\beta}}{n(tn^{-1})^{qs+\frac{q}{p}}(\ln n)^{q\beta}}\bigg{)}\bigg{(}\frac{(C+\varepsilon)^{p}(ps+1)}{1-\varepsilon}\bigg{)}^{\frac{q}{p}}\,
supt,tn(tnn(tn1)qs+qp)((C+ε)p(ps+1)(1ε)2)qp\displaystyle\leq\sup_{t\in\mathbb{R},\,t\geq n}\bigg{(}\frac{t-n}{n(tn^{-1})^{qs+\frac{q}{p}}}\bigg{)}\bigg{(}\frac{(C+\varepsilon)^{p}(ps+1)}{(1-\varepsilon)^{2}}\bigg{)}^{\frac{q}{p}}

if nn is large enough. It is easy to see that the function h(t):=tntqs+q/ph(t):=\frac{t-n}{t^{qs+q/p}}, t[n,)t\in[n,\infty), attains its maximum at t0=(1+1qs+q/p1)nt_{0}=\big{(}1+\frac{1}{qs+q/p-1}\big{)}n. Hence, we find

(σnns1p+1q(lnn)β)q\displaystyle\bigg{(}\frac{\sigma_{n}}{n^{-s-\frac{1}{p}+\frac{1}{q}}(\ln n)^{\beta}}\bigg{)}^{q} 1(qs+qp1)(1+1qs+q/p1)qs+q/p((C+ε)p(ps+1)(1ε)2)qp\displaystyle\leq\frac{1}{(qs+\frac{q}{p}-1)\big{(}1+\frac{1}{qs+q/p-1}\big{)}^{qs+q/p}}\bigg{(}\frac{(C+\varepsilon)^{p}(ps+1)}{(1-\varepsilon)^{2}}\bigg{)}^{\frac{q}{p}} (3.7)

if nn is large enough. Taking the limits nn\to\infty and afterwards ε0\varepsilon\downarrow 0 in (3.7) we obtain the upper bound. In view of Theorem 2.1 (i), by choosing m(1+1qs+q/p1)nm\sim\big{(}1+\frac{1}{qs+q/p-1}\big{)}n we also obtain the lower bound in this case.
Step 3. Proof of the case 0<q<p<0<q<p<\infty. Firstly, we estimate nn_{*} in (2.4). From (2.5) we have

(nn)λnpk=1nλkp.(n_{*}-n)\lambda_{n_{*}}^{-p}\leq\sum_{k=1}^{n_{*}}\lambda_{k}^{-p}.

In view of (3.4) and (3.5) we get for nn3n\geq n_{3}

(nn)(C+ε)pnps(lnn)pβ(n+1)ps+1(ln(n+1))pβ(ε+1+ε(Cε)p(ps+1))\displaystyle(n_{*}-n)(C+\varepsilon)^{-p}\frac{n_{*}^{ps}}{(\ln n_{*})^{p\beta}}\leq\frac{(n_{*}+1)^{ps+1}}{(\ln(n_{*}+1))^{p\beta}}\bigg{(}\varepsilon+\frac{1+\varepsilon}{(C-\varepsilon)^{p}(ps+1)}\bigg{)}

which implies

nnn(1+1n)ps+1(ε(C+ε)p+(1+ε)(C+ε)p(Cε)p(ps+1)).\displaystyle\dfrac{n_{*}-n}{n_{*}}\leq\bigg{(}1+\frac{1}{n_{*}}\bigg{)}^{ps+1}\bigg{(}\varepsilon(C+\varepsilon)^{p}+\frac{(1+\varepsilon)(C+\varepsilon)^{p}}{(C-\varepsilon)^{p}(ps+1)}\bigg{)}.

Therefore, for any ϵ>0\epsilon>0, exist N1>0N_{1}>0 such that for n>N1n>N_{1} we have

nnn1ps+1+ϵornnpsps+1ϵ.\displaystyle\dfrac{n_{*}-n}{n_{*}}\leq\dfrac{1}{ps+1}+\epsilon\qquad\text{or}\qquad n_{*}\leq\dfrac{n}{\frac{ps}{ps+1}-\epsilon}. (3.8)

Using (3.4) and (3.6) the condition (mn)λmpk=1mλkp(m-n)\lambda_{m}^{-p}\leq\sum_{k=1}^{m}\lambda_{k}^{-p} is satisfied if

mps+1(lnm)pβ1ε(C+ε)p(ps+1)(mn)(Cε)pmps(lnm)pβ\frac{m^{ps+1}}{(\ln m)^{p\beta}}\cdot\frac{1-\varepsilon}{(C+\varepsilon)^{p}(ps+1)}\geq\dfrac{(m-n)(C-\varepsilon)^{-p}m^{ps}}{(\ln m)^{p\beta}}

which is equivalent to

mnm(1ε)(Cε)p(C+ε)p(ps+1).\dfrac{m-n}{m}\leq\dfrac{(1-\varepsilon)(C-\varepsilon)^{p}}{(C+\varepsilon)^{p}(ps+1)}.

Hence, for any ϵ>0\epsilon>0, there exists N2N_{2}\in\mathbb{N} such that for n>N2n>N_{2} we have

(1ε)(Cε)p(C+ε)p(ps+1)1ps+1ϵ.\dfrac{(1-\varepsilon)(C-\varepsilon)^{p}}{(C+\varepsilon)^{p}(ps+1)}\geq\dfrac{1}{ps+1}-\epsilon.

Therefore, the condition (mn)λmpk=1mλkp(m-n)\lambda_{m}^{-p}\leq\sum_{k=1}^{m}\lambda_{k}^{-p} is satisfied if

mnm1ps+1ϵormnpsps+1+ϵ.\dfrac{m-n}{m}\leq\dfrac{1}{ps+1}-\epsilon\qquad\text{or}\qquad m\leq\dfrac{n}{\frac{ps}{ps+1}+\epsilon}.

This leads to nnpsps+1+ϵ.n_{*}\geq\frac{n}{\frac{ps}{ps+1}+\epsilon}. From this and (3.8) we deduce

n(1+1ps)n,n+.n_{*}\sim\bigg{(}1+\frac{1}{ps}\bigg{)}n,\ \ \ n\to+\infty\,.

Denoting α=pqpq\alpha=\frac{pq}{p-q}, from (2.4) we have

σnα=((nn)1/q(k=1nλkp)1/p)α+k=n+1λkα.\displaystyle\sigma_{n}^{\alpha}=\Bigg{(}\dfrac{(n_{*}-n)^{1/q}}{\big{(}\sum_{k=1}^{n_{*}}\lambda_{k}^{-p}\big{)}^{1/p}}\Bigg{)}^{\alpha}+\sum_{k=n_{*}+1}^{\infty}\lambda_{k}^{\alpha}. (3.9)

Using (3.5) and (3.6) again we get

k=1nλkp1Cp(ps+1)nps+1(lnn)pβ1Cp(ps+1)(1+1ps)ps+1nps+1(lnn)pβ,n+.\sum_{k=1}^{n_{*}}\lambda_{k}^{-p}\sim\dfrac{1}{C^{p}(ps+1)}\dfrac{n_{*}^{ps+1}}{(\ln n_{*})^{p\beta}}\sim\dfrac{1}{C^{p}(ps+1)}\dfrac{(1+\frac{1}{ps})^{ps+1}n^{ps+1}}{(\ln n)^{p\beta}},\ \ n\to+\infty.

Therefore, the first term in (3.9) can be estimated:

((nn)1q(k=1nλkp)1p)α\displaystyle\Bigg{(}\dfrac{(n_{*}-n)^{\frac{1}{q}}}{\big{(}\sum_{k=1}^{n_{*}}\lambda_{k}^{-p}\big{)}^{\frac{1}{p}}}\Bigg{)}^{\alpha} n+(C(ps+1)1p(lnn)β(1+1ps)s+1pns+1p(nps)1q)α=Cαps(ps1+ps)sα(lnn)αβnα(s+1p1q).\displaystyle\overset{n\to+\infty}{\sim}\Bigg{(}\dfrac{C(ps+1)^{\frac{1}{p}}(\ln n)^{\beta}}{(1+\frac{1}{ps})^{s+\frac{1}{p}}n^{s+\frac{1}{p}}}\Big{(}\frac{n}{ps}\Big{)}^{\frac{1}{q}}\Bigg{)}^{\alpha}=\frac{C^{\alpha}}{ps}\bigg{(}\frac{ps}{1+ps}\bigg{)}^{s\alpha}\frac{(\ln n)^{\alpha\beta}}{n^{\alpha(s+\frac{1}{p}-\frac{1}{q})}}. (3.10)

Now, we estimate the second term in (3.9). Observe that f(t)=tsα(lnt)αβf(t)=t^{-s\alpha}(\ln t)^{\alpha\beta} is a decreasing function when tt0t\geq t_{0} for some t0>0t_{0}>0. Hence, when nn is large enough, in view of (3.4) we can bound

k=n+1λkα\displaystyle\sum_{k=n_{*}+1}^{\infty}\lambda_{k}^{\alpha} (C+ε)αk=n+1(lnk)αβksα(C+ε)αn+(lnt)αβtsαdt\displaystyle\leq(C+\varepsilon)^{\alpha}\sum_{k=n_{*}+1}^{\infty}\frac{(\ln k)^{\alpha\beta}}{k^{s\alpha}}\leq(C+\varepsilon)^{\alpha}\int_{n_{*}}^{+\infty}\frac{(\ln t)^{\alpha\beta}}{t^{s\alpha}}\mathrm{d}t
=(C+ε)α(lnn)αβnsα11+1tsα(lnntlnn)αβdt.\displaystyle=(C+\varepsilon)^{\alpha}\frac{(\ln n_{*})^{\alpha\beta}}{n_{*}^{s\alpha-1}}\int_{1}^{+\infty}\dfrac{1}{t^{s\alpha}}\bigg{(}\dfrac{\ln n_{*}t}{\ln n_{*}}\bigg{)}^{\alpha\beta}\mathrm{d}t. (3.11)

Similarly, we also have the estimate

k=n+1λkα\displaystyle\sum_{k=n_{*}+1}^{\infty}\lambda_{k}^{\alpha} (Cε)αk=n+1(lnk)αβksα(Cε)αn+1+(lnt)αβtsαdt\displaystyle\geq(C-\varepsilon)^{\alpha}\sum_{k=n_{*}+1}^{\infty}\frac{(\ln k)^{\alpha\beta}}{k^{s\alpha}}\geq(C-\varepsilon)^{\alpha}\int_{n_{*}+1}^{+\infty}\frac{(\ln t)^{\alpha\beta}}{t^{s\alpha}}\mathrm{d}t
=(Cε)α(ln(n+1))αβ(n+1)sα11+1tsα(ln(n+1)tln(n+1))αβdt.\displaystyle=(C-\varepsilon)^{\alpha}\frac{(\ln(n_{*}+1))^{\alpha\beta}}{(n_{*}+1)^{s\alpha-1}}\int_{1}^{+\infty}\dfrac{1}{t^{s\alpha}}\bigg{(}\dfrac{\ln(n_{*}+1)t}{\ln(n_{*}+1)}\bigg{)}^{\alpha\beta}\mathrm{d}t. (3.12)

Note that the condition s>1q1ps>\frac{1}{q}-\frac{1}{p} implies sα>1s\alpha>1. Using Lemma 3.3 (ii), from (3) and (3) we get

k=n+1λkα\displaystyle\sum_{k=n_{*}+1}^{\infty}\lambda_{k}^{\alpha} n+1sα1Cα(1+1ps)1sα(lnn)αβnsα1\displaystyle\overset{n\to+\infty}{\sim}\dfrac{1}{s\alpha-1}C^{\alpha}\bigg{(}1+\frac{1}{ps}\bigg{)}^{1-s\alpha}\frac{(\ln n)^{\alpha\beta}}{n^{s\alpha-1}}
=pqspqp+q(1+1ps)Cα(psps+1)αs(lnn)αβnα(s+1p1q).\displaystyle\ \ \ =\dfrac{p-q}{spq-p+q}\bigg{(}1+\frac{1}{ps}\bigg{)}C^{\alpha}\bigg{(}\frac{ps}{ps+1}\bigg{)}^{\alpha s}\frac{(\ln n)^{\alpha\beta}}{n^{\alpha(s+\frac{1}{p}-\frac{1}{q})}}.

From this and (3.10) we finally obtain

σnα\displaystyle\sigma_{n}^{\alpha} n+[1ps+pqspqp+q(1+1ps)]Cα(psps+1)sα(lnn)αβnα(s+1p1q)\displaystyle\overset{n\to+\infty}{\sim}\bigg{[}\frac{1}{ps}+\dfrac{p-q}{spq-p+q}\Big{(}1+\frac{1}{ps}\Big{)}\bigg{]}C^{\alpha}\bigg{(}\frac{ps}{ps+1}\bigg{)}^{s\alpha}\frac{(\ln n)^{\alpha\beta}}{n^{\alpha(s+\frac{1}{p}-\frac{1}{q})}}
=pspqp+qCα(psps+1)sα(lnn)αβnα(s+1p1q)\displaystyle\ \ \ =\frac{p}{spq-p+q}C^{\alpha}\bigg{(}\frac{ps}{ps+1}\bigg{)}^{s\alpha}\frac{(\ln n)^{\alpha\beta}}{n^{\alpha(s+\frac{1}{p}-\frac{1}{q})}}

which proves the second statement. ∎

4 Best nn-term approximation of function spaces with mixed smoothness

In this section we shall apply the result in Section 3 to the family of weights

ωs,r(k):=\displaystyle\omega_{s,r}(k):= i=1d(1+|ki|r)s/r,0<r<,\displaystyle\prod_{i=1}^{d}\big{(}1+|k_{i}|^{r}\big{)}^{s/r}\,,\qquad 0<r<\infty\,,
ωs,r(k):=\displaystyle\omega_{s,r}(k):= i=1dmax(1,|ki|)s,r=,\displaystyle\prod_{i=1}^{d}\max(1,|k_{i}|)^{s}\,,\qquad r=\infty\,,

kdk\in\mathbb{Z}^{d}, where the parameter ss satisfies 0<s<0<s<\infty. We shall use the notation Hmixs,r(𝕋d):=Fωs,r,2(𝕋d){H}_{\mathop{\rm mix}}^{s,r}({\mathbb{T}}^{d}):={F}_{\omega_{s,r},2}({\mathbb{T}}^{d}) and 𝒜mixs,r(𝕋d):=Fωs,r,1(𝕋d)\mathcal{A}_{\mathop{\rm mix}}^{s,r}({\mathbb{T}}^{d}):=F_{\omega_{s,r},1}({\mathbb{T}}^{d}), respectively. The classes Hmixs,r(𝕋d){H}_{\mathop{\rm mix}}^{s,r}({\mathbb{T}}^{d}) are called periodic Sobolev spaces with mixed smoothness and well-known in approximation theory, see, e.g., [27, 28, 12]. The classes 𝒜mixs,r(𝕋d)\mathcal{A}_{\mathop{\rm mix}}^{s,r}({\mathbb{T}}^{d}) are the weighted Wiener algebras. These spaces have been studied extensively recently in [21, 6, 22, 26]. In both spaces Hmixs,r(𝕋d){H}_{\mathop{\rm mix}}^{s,r}({\mathbb{T}}^{d}) and 𝒜mixs,r(𝕋d)\mathcal{A}_{\mathop{\rm mix}}^{s,r}({\mathbb{T}}^{d}), for different rr, we obtain the same sets of functions. A change of the parameter rr leads to a change of the quasinorm only.

Let mm\in\mathbb{N}. We define the space Hmixm(𝕋d)H^{m}_{\mathop{\rm mix}}({\mathbb{T}}^{d}) to be the collection of all functions fL2(𝕋d)f\in L_{2}({\mathbb{T}}^{d}) such that all distributional derivatives DαfD^{\alpha}f with α=(α1,,αd)\alpha=(\alpha_{1},\ldots,\alpha_{d}), maxj=1,,dαjm\max_{j=1,\ldots,d}\alpha_{j}\leq m belong to L2(𝕋d)L_{2}({\mathbb{T}}^{d}). The space Hmixm(𝕋d)H^{m}_{\mathop{\rm mix}}({\mathbb{T}}^{d}) is equipped with the norm

fHmixm(𝕋d):=(α=(α1,,αd)0dαjm,j=1,,dDαfL2(𝕋d)2)1/2.\big{\|}\,f\,\big{\|}_{H^{m}_{\mathop{\rm mix}}({\mathbb{T}}^{d})}:=\Bigg{(}\sum_{\alpha=(\alpha_{1},\ldots,\alpha_{d})\in\mathbb{N}_{0}^{d}\atop\alpha_{j}\leq m,\,j=1,\ldots,d}\,\big{\|}\,D^{\alpha}f\big{\|}_{L_{2}({\mathbb{T}}^{d})}^{2}\Bigg{)}^{1/2}\,.

Then Hmixm(𝕋d)=Hmixm,r(𝕋d)H^{m}_{\mathop{\rm mix}}({\mathbb{T}}^{d})=H^{m,r}_{\mathop{\rm mix}}({\mathbb{T}}^{d}) for all rr in the sense of equivalent quasinorms. If m=1m=1, then we have Hmix1,2(𝕋d)=Hmix1(𝕋d).\|\cdot\|_{H^{1,2}_{\mathop{\rm mix}}({\mathbb{T}}^{d})}=\|\cdot\|_{H^{1}_{\mathop{\rm mix}}({\mathbb{T}}^{d})}. If m2m\geq 2, then the norm Hmixm(𝕋d)\|\cdot\|_{H^{m}_{\mathop{\rm mix}}({\mathbb{T}}^{d})} itself does not belong to the family of norms Hmixm,r(𝕋d)\|\cdot\|_{H^{m,r}_{\mathop{\rm mix}}({\mathbb{T}}^{d})}, 0<r0<r\leq\infty. But the choice r=2mr=2m leads to the following standard norm

fHmixm,2m(𝕋d)=(α{0,m}dDαfL2(𝕋d)2)1/2,\big{\|}\,f\,\big{\|}_{H^{m,2m}_{\mathop{\rm mix}}({\mathbb{T}}^{d})}=\Bigg{(}\sum_{\alpha\in\{0,m\}^{d}}\big{\|}\,D^{\alpha}f\,\big{\|}_{L_{2}({\mathbb{T}}^{d})}^{2}\Bigg{)}^{1/2}\,,

see [25].

Let λ=(λn)n\lambda=(\lambda_{n})_{n\in\mathbb{N}} denote the non-increasing rearrangement of the sequence (1/ωs,r(k))kd(1/\omega_{s,r}(k))_{k\in\mathbb{Z}^{d}}. That leads to λn=an(id:Hmixs,r(𝕋d)L2(𝕋d))\lambda_{n}=a_{n}\big{(}id:H^{s,r}_{\mathop{\rm mix}}({\mathbb{T}}^{d})\to L_{2}({\mathbb{T}}^{d})\big{)}. We recall a result obtained in [25].

Proposition 4.1.

Let 0<s<0<s<\infty and 0<r0<r\leq\infty. Then it holds

limnλnns(lnn)s(d1)=limnan(id:Hmixs,r(𝕋d)L2(𝕋d))ns(lnn)s(d1)=(2d(d1)!)s.\lim\limits_{n\to\infty}\frac{\lambda_{n}}{n^{-s}(\ln n)^{s(d-1)}}=\lim\limits_{n\to\infty}\frac{a_{n}\big{(}id:H^{s,r}_{\mathop{\rm mix}}({\mathbb{T}}^{d})\to L_{2}({\mathbb{T}}^{d})\big{)}}{n^{-s}(\ln n)^{s(d-1)}}=\bigg{(}\frac{2^{d}}{(d-1)!}\bigg{)}^{s}\,.

From this and Theorem 3.4 we get the following.

Theorem 4.2.

Let 0<s<0<s<\infty and 0<r0<r\leq\infty. Then it holds

limnσn(id:Hmixs,r(𝕋d)L2(𝕋d),𝒯d)ns(lnn)s(d1)=ss(s+12)s(2d(d1)!)s\lim\limits_{n\to\infty}\frac{\sigma_{n}\big{(}id:H^{s,r}_{\mathop{\rm mix}}({\mathbb{T}}^{d})\to L_{2}({\mathbb{T}}^{d}),\mathcal{T}^{d}\big{)}}{n^{-s}(\ln n)^{s(d-1)}}=\frac{s^{s}}{(s+\frac{1}{2})^{s}}\bigg{(}\frac{2^{d}}{(d-1)!}\bigg{)}^{s}\,

and if s>1/2s>1/2

limnσn(id:Hmixs,r(𝕋d)𝒜(𝕋d),𝒯d)ns+12(lnn)s(d1)=(ss+12)s(1s12)12(2d(d1)!)s.\lim\limits_{n\to\infty}\frac{\sigma_{n}\big{(}id:H^{s,r}_{\mathop{\rm mix}}({\mathbb{T}}^{d})\to\mathcal{A}({\mathbb{T}}^{d}),\mathcal{T}^{d}\big{)}}{n^{-s+\frac{1}{2}}(\ln n)^{s(d-1)}}=\bigg{(}\frac{s}{s+\frac{1}{2}}\bigg{)}^{s}\bigg{(}\frac{1}{s-\frac{1}{2}}\bigg{)}^{\frac{1}{2}}\bigg{(}\frac{2^{d}}{(d-1)!}\bigg{)}^{s}\,.

The asymptotic constants for embeddings of best nn-term approximation widths of embedding of weighted Wiener classes 𝒜mixs,r(𝕋d)\mathcal{A}^{s,r}_{\mathop{\rm mix}}({\mathbb{T}}^{d}) are also obtained from Theorem 3.4.

Theorem 4.3.

Let 0<s<0<s<\infty and 0<r0<r\leq\infty. Then it holds

limnσn(id:𝒜mixs,r(𝕋d)L2(𝕋d),𝒯d)ns12(lnn)s(d1)=(s+12)s+122(s+1)s(2d(d1)!)s\lim\limits_{n\to\infty}\frac{\sigma_{n}\big{(}id:\mathcal{A}_{\mathop{\rm mix}}^{s,r}({\mathbb{T}}^{d})\to L_{2}({\mathbb{T}}^{d}),\mathcal{T}^{d}\big{)}}{n^{-s-\frac{1}{2}}(\ln n)^{s(d-1)}}=\frac{(s+\frac{1}{2})^{s+\frac{1}{2}}}{\sqrt{2}(s+1)^{s}}\bigg{(}\frac{2^{d}}{(d-1)!}\bigg{)}^{s}

and

limnσn(id:𝒜mixs,r(𝕋d)𝒜(𝕋d),𝒯d)ns(lnn)s(d1)=ss(s+1)s(2d(d1)!)s.\lim\limits_{n\to\infty}\frac{\sigma_{n}\big{(}id:\mathcal{A}_{\mathop{\rm mix}}^{s,r}({\mathbb{T}}^{d})\to\mathcal{A}({\mathbb{T}}^{d}),\mathcal{T}^{d}\big{)}}{n^{-s}(\ln n)^{s(d-1)}}=\frac{s^{s}}{(s+1)^{s}}\bigg{(}\frac{2^{d}}{(d-1)!}\bigg{)}^{s}.
Remark 4.4.

Let us compare the asymptotic decay of ana_{n} and σn\sigma_{n}. The equivalence

an(id:Hmixs,r(𝕋d)L2(𝕋d))σn(id:Hmixs,r(𝕋d)L2(𝕋d),𝒯d)a_{n}\big{(}id:H^{s,r}_{\mathop{\rm mix}}({\mathbb{T}}^{d})\to L_{2}({\mathbb{T}}^{d})\big{)}\asymp\sigma_{n}\big{(}id:H^{s,r}_{\mathop{\rm mix}}({\mathbb{T}}^{d})\to L_{2}({\mathbb{T}}^{d}),\mathcal{T}^{d}\big{)}

has been known with a long history, see, e.g., [12, Chapters 4 and 7] for comments. From Theorem 4.2 and [26] we also have

σn(id:Hmixs,r(𝕋d)𝒜(𝕋d),𝒯d)σn(id:Hmixs,r(𝕋d)𝒜(𝕋d),𝒯d).\sigma_{n}\big{(}id:H^{s,r}_{\mathop{\rm mix}}({\mathbb{T}}^{d})\to\mathcal{A}({\mathbb{T}}^{d}),\mathcal{T}^{d}\big{)}\asymp\sigma_{n}\big{(}id:H^{s,r}_{\mathop{\rm mix}}({\mathbb{T}}^{d})\to\mathcal{A}({\mathbb{T}}^{d}),\mathcal{T}^{d}\big{)}.

However, by Theorem 4.3 and [26] we find

an(id:𝒜mixs,r(𝕋d)L2(𝕋d))n12σn(id:𝒜mixs,r(𝕋d)L2(𝕋d),𝒯d).a_{n}\big{(}id:\mathcal{A}_{\mathop{\rm mix}}^{s,r}({\mathbb{T}}^{d})\to L_{2}({\mathbb{T}}^{d})\big{)}\asymp n^{\frac{1}{2}}\sigma_{n}\big{(}id:\mathcal{A}_{\mathop{\rm mix}}^{s,r}({\mathbb{T}}^{d})\to L_{2}({\mathbb{T}}^{d}),\mathcal{T}^{d}\big{)}.

This indicates that approximating functions in the class 𝒜mixs,r(𝕋d)\mathcal{A}_{\mathop{\rm mix}}^{s,r}({\mathbb{T}}^{d}) by nn-term improves the convergence rate 1/21/2 compared to linear method.

We are also interested in asymptotic constants of best nn-term approximation of embeddings of function spaces with mixed smoothness into H1(𝕋d)H^{1}({\mathbb{T}}^{d}). Here H1(𝕋d)H^{1}({\mathbb{T}}^{d}) is equipped with the norm

fH1(𝕋d):\displaystyle\|\,f\,\|_{H^{1}({\mathbb{T}}^{d})}: =(kd(1+j=1d|kj|2)|f^(k)|2)1/2=(fL2(𝕋d)2+j=1dfxjL2(𝕋d)2)1/2.\displaystyle=\Bigg{(}\sum_{k\in\mathbb{Z}^{d}}\bigg{(}1+\sum_{j=1}^{d}|k_{j}|^{2}\bigg{)}|\hat{f}(k)|^{2}\Bigg{)}^{1/2}=\Bigg{(}\|\,f\,\|_{L_{2}({\mathbb{T}}^{d})}^{2}+\sum_{j=1}^{d}\Big{\|}\,\frac{\partial f}{\partial x_{j}}\,\Big{\|}_{L_{2}({\mathbb{T}}^{d})}^{2}\Bigg{)}^{1/2}\,.

I.e., H1(𝕋d)H^{1}({\mathbb{T}}^{d}) is the standard isotropic periodic Sobolev space with smoothness 11. We define a weight ω~\tilde{\omega} by

ω~(k):=j=1d(1+|kj|2)s/2(1+j=1d|kj|2)1/2,k=(k1,,kd)d.\tilde{\omega}(k):=\frac{\prod_{j=1}^{d}(1+|k_{j}|^{2})^{s/2}}{\big{(}1+\sum_{j=1}^{d}|k_{j}|^{2}\big{)}^{1/2}},\qquad k=(k_{1},\ldots,k_{d})\in\mathbb{Z}^{d}\,. (4.1)

Rearranging non-increasingly the sequence (1/ω~(k))kd(1/\tilde{\omega}(k))_{k\in\mathbb{Z}^{d}} with the outcome denoted by (λ~n)n(\tilde{\lambda}_{n})_{n\in\mathbb{N}}, we obtain λ~n=an(id:Hmixs,2(𝕋d)H1(𝕋d))\tilde{\lambda}_{n}=a_{n}\big{(}id:H^{s,2}_{\mathop{\rm mix}}({\mathbb{T}}^{d})\to H^{1}({\mathbb{T}}^{d})\big{)}. The asymptotic constant of an(id:Hmixs,2(𝕋d)H1(𝕋d))a_{n}\big{(}id:H^{s,2}_{\mathop{\rm mix}}({\mathbb{T}}^{d})\to H^{1}({\mathbb{T}}^{d})\big{)} was obtained recently in [26].

Proposition 4.5.

Let dd\in\mathbb{N}, s>1s>1 and

S:=k=1+1(k2+1)s2(s1).S:=\sum_{k=1}^{+\infty}\dfrac{1}{(k^{2}+1)^{\frac{s}{2(s-1)}}}. (4.2)

Then we have

limn+λ~nn1s=limn+an(id:Hmixs,2(𝕋d)H1(𝕋d))n1s=(2d)s1(2S+1)(s1)(d1).\lim_{n\to+\infty}\frac{\tilde{\lambda}_{n}}{n^{1-s}}=\lim_{n\to+\infty}\frac{a_{n}\big{(}id:H^{s,2}_{\mathop{\rm mix}}({\mathbb{T}}^{d})\to H^{1}({\mathbb{T}}^{d})\big{)}}{n^{1-s}}=(2d)^{s-1}(2S+1)^{(s-1)(d-1)}.

From this and Theorem 3.4 we obtain the following.

Theorem 4.6.

Let dd\in\mathbb{N}, s>1s>1 and SS be given in (4.2). Then it holds

limnσn(id:Hmixs,2(𝕋d)H1(𝕋d),𝒯d)ns+1=(s1s12)s1(2d)s1(2S+1)(s1)(d1)\lim\limits_{n\to\infty}\frac{\sigma_{n}\big{(}id:H_{\mathop{\rm mix}}^{s,2}({\mathbb{T}}^{d})\to H^{1}({\mathbb{T}}^{d}),\mathcal{T}^{d}\big{)}}{n^{-s+1}}=\bigg{(}\frac{s-1}{s-\frac{1}{2}}\bigg{)}^{s-1}(2d)^{s-1}(2S+1)^{(s-1)(d-1)}

and

limnσn(id:𝒜mixs,2(𝕋d)H1(𝕋d),𝒯d)ns+12=(s12)s122ss1(2d)s1(2S+1)(s1)(d1).\lim\limits_{n\to\infty}\frac{\sigma_{n}\big{(}id:\mathcal{A}_{\mathop{\rm mix}}^{s,2}({\mathbb{T}}^{d})\to H^{1}({\mathbb{T}}^{d}),\mathcal{T}^{d}\big{)}}{n^{-s+\frac{1}{2}}}=\frac{(s-\frac{1}{2})^{s-\frac{1}{2}}}{\sqrt{2}\,s^{s-1}}(2d)^{s-1}(2S+1)^{(s-1)(d-1)}.
Proof.

Let ω~\tilde{\omega} be given in (4.1). We will show that

σn(id:Hmixs,2(𝕋d)H1(𝕋d),𝒯d)=σn(id:Fω~,2(𝕋d)F2(𝕋d),𝒯d)\sigma_{n}\big{(}id:H_{\mathop{\rm mix}}^{s,2}({\mathbb{T}}^{d})\to H^{1}({\mathbb{T}}^{d}),\mathcal{T}^{d}\big{)}=\sigma_{n}\big{(}id:F_{{\tilde{\omega}},2}({\mathbb{T}}^{d})\to F_{2}({\mathbb{T}}^{d}),\mathcal{T}^{d}\big{)} (4.3)

and

σn(id:𝒜mixs,2(𝕋d)H1(𝕋d),𝒯d)=σn(id:Fω~,1(𝕋d)F2(𝕋d),𝒯d),\sigma_{n}\big{(}id:\mathcal{A}_{\mathop{\rm mix}}^{s,2}({\mathbb{T}}^{d})\to H^{1}({\mathbb{T}}^{d}),\mathcal{T}^{d}\big{)}=\sigma_{n}\big{(}id:F_{{\tilde{\omega}},1}({\mathbb{T}}^{d})\to F_{2}({\mathbb{T}}^{d}),\mathcal{T}^{d}\big{)}, (4.4)

by using standard lifting arguments. We consider the diagram

Hmixs,2(𝕋d)@ >id>>H1(𝕋d)ABFω~,2(𝕋d)@ >id>>F2(𝕋d)\begin{CD}H_{\mathop{\rm mix}}^{s,2}({\mathbb{T}}^{d})@ >id>>H^{1}({\mathbb{T}}^{d})\\ @V{}V{A}V@A{}A{B}A\\ F_{{\tilde{\omega}},2}({\mathbb{T}}^{d})@ >id>>F_{2}({\mathbb{T}}^{d})\,\end{CD}

where the linear operators AA and BB are defined for fHmixs,2(𝕋d)f\in H^{s,2}_{\mathop{\rm mix}}({\mathbb{T}}^{d}) and gF2(𝕋d)g\in F_{2}({\mathbb{T}}^{d}) respectively by

Af^(k):=(1+j=1d|kj|2)1/2f^(k),Bg^(k):=(1+j=1d|kj|2)1/2g^(k),k=(k1,,kd)d.\widehat{Af}(k):=\bigg{(}1+\sum_{j=1}^{d}|k_{j}|^{2}\bigg{)}^{1/2}\hat{f}(k),\quad\widehat{Bg}(k):=\bigg{(}1+\sum_{j=1}^{d}|k_{j}|^{2}\bigg{)}^{-1/2}\hat{g}(k),\ \ k=(k_{1},\ldots,k_{d})\in\mathbb{Z}^{d}.

It is obvious that A=B=1\|A\|=\|B\|=1. Now by the property (2.1), we obtain

σn(id:Hmixs,2(𝕋d)H1(𝕋d),𝒯d)σn(id:Fω~,2(𝕋d)F2(𝕋d),𝒯d).\sigma_{n}\big{(}id:H_{\mathop{\rm mix}}^{s,2}({\mathbb{T}}^{d})\to H^{1}({\mathbb{T}}^{d}),\mathcal{T}^{d}\big{)}\leq\sigma_{n}\big{(}id:F_{{\tilde{\omega}},2}({\mathbb{T}}^{d})\to F_{2}({\mathbb{T}}^{d}),\mathcal{T}^{d}\big{)}.

The reverse inequality follows from the modified diagram

Hmixs,2(𝕋d)@ >id>>H1(𝕋d)A1B1Fω~,2(𝕋d)@ >id>>F2(𝕋d).\begin{CD}H_{\mathop{\rm mix}}^{s,2}({\mathbb{T}}^{d})@ >id>>H^{1}({\mathbb{T}}^{d})\\ @A{}A{{A}^{-1}}A@V{}V{{B}^{-1}}V\\ F_{{\tilde{\omega}},2}({\mathbb{T}}^{d})@ >id>>F_{2}({\mathbb{T}}^{d})\,.\end{CD}

Hence (4.3) is proved. Proof of (4.4) is carried out similarly. Now the assertion follows from Proposition 4.5 and Theorem 3.4. ∎

References

  • [1] S. Balgimbayeva and T. Smirnov. Nonlinear wavelet approximation of periodic function classes with generalized mixed smoothnes. Anal. Math., 43:1–26, 2017.
  • [2] D. Bazarkhanov. Nonlinear trigonometric approximations of multivariate function classes. Proc. Steklov Inst. Math., 293:2–36, 2016.
  • [3] K. A. Bekmaganbetov and Y. Toleugazy. On the order of the trigonometric diameter of the anisotropic nikol’skii–besov class in the metric of anisotropic lorentz spaces. Anal. Math., 45:237–247, 2019.
  • [4] H.-J. Bungartz and M. Griebel. Sparse grids. Acta Numer., 13:147–269, 2004.
  • [5] G. Byrenheid. Sparse Representation of Multivariate Functions Based on Discrete Point Evaluations. PhD thesis, Universität Bonn, 2018.
  • [6] G. Byrenheid, L. Kämmerer, T. Ullrich, and T. Volkmer. Approximation of multivariate periodic functions based on sampling along multiple rank-1 lattices. Numer. Math., 136:99–1034, 2017.
  • [7] F. Cobos, T. Kühn, and W. Sickel. Optimal approximation of Sobolev functions in the sup-norm. J. Funct. Anal., 270:4196–4212, 2016.
  • [8] D. Dũng. On nonlinear nn-widths and nn-term approximation. Vietnam J. Math., 26:165–176, 1998.
  • [9] D. Dũng. Non- linear approximations using wavelet decompositions. Vietnam J. Math., 29:197–224, 2001.
  • [10] D. Dũng. Continuous algorithms in nn-term approximation and non-linear widths. J. Approx. Theory, 7:55–76, 2000.
  • [11] D. Dũng. Asymptotic orders of optimal non-linear approximation. East J. Approx., 102:217–242, 2001.
  • [12] D. Dũng, V. N. Temlyakov, and T. Ullrich. Hyperbolic Cross Approximation. Advanced Courses in Mathematics - CRM Barcelona, Birkhäuser/Springer, 2018.
  • [13] I. Daubechies, R. DeVore, S. Foucart, B. Hanin, and G. Petrova. Nonlinear approximation and (Deep) ReLU networks. arXiv:1905.02199, 2019.
  • [14] R. DeVore. Nonlinear approximation. Acta Numer., 7:51–150, 1998.
  • [15] R. A. DeVore. Nonlinear approximation and its applications. In: DeVore R., Kunoth A. (eds) Multiscale, Nonlinear and Adaptive Approximation. Springer, Berlin, Heidelberg, pages 169–201, 2009.
  • [16] F. Gao. Exact value of the nn-term approximation of a diagonal operator. J. Approx. Theory, 162:646–652, 2010.
  • [17] F. Gensun and Q. Lixin. Approximation characteristics for diagonal operators in different computational settings. J. Approx. Theory, 140:178–190, 2001.
  • [18] M. Hansen. Nonlinear Approximation and Function Spaces of Dominating Mixed Smoothness. PhD thesis, Jena University, 2010.
  • [19] M. Hansen and W. Sickel. Best mm-term approximation and tensor products of Sobolev and Besov spaces – the case of noncompact embeddings. Constr. Approx., 16:313–356, 2010.
  • [20] M. Hansen and W. Sickel. Best mm-term approximation and tensor products of Sobolev and Besov spaces – the case of compact embeddings. Constr. Approx., 36:1–51, 2012.
  • [21] L. Kämmerer, D. Potts, and T. Volkmer. Approximation of multivariate periodic functions by trigonometric polynomials based on rank-1 lattice sampling. J. Complexity, 31:543–576, 2015.
  • [22] L. Kämmerer and T. Volkmer. Approximation of multivariate periodic functions based on sampling along multiple rank-1 lattices. J. Approx. Theory, 246:1–27, 2019.
  • [23] B. Kashin and V. Temlyakov. On best mm-term approximations and the entropy of sets in the space L1L^{1}. Math. Notes, 56:1137–1157, 1994.
  • [24] T. Kühn, W. Sickel, and T. Ullrich. Approximation numbers of Sobolev embeddings – Sharp constants and tractability. J. Complexity, 30:95–116, 2014.
  • [25] T. Kühn, W. Sickel, and T. Ullrich. Approximation of mixed order Sobolev functions on the dd-torus – Asymptotics, preasymptotics and dd-dependence. Constr. Approx., 42:353–398, 2015.
  • [26] V. D. Nguyen, V. K. Nguyen, and W. Sickel. Widths of embeddings of weighted Wiener classes. arxiv.org/abs/2011.07663, 2020.
  • [27] E. Novak and H. Woźniakowski. Tractability of Multivariate Problems, Volume I: Linear Information. EMS Tracts in Mathematics, Vol. 6, Eur. Math. Soc. Publ. House, Zürich, 2008.
  • [28] E. Novak and H. Woźniakowski. Tractability of Multivariate Problems, Volume II: Standard Information for Functionals. EMS Tracts in Mathematics, Vol. 12, Eur. Math. Soc. Publ. House, Zürich, 2010.
  • [29] A. Pietsch. Operator Ideals. North-Holland, Amsterdam, 1980.
  • [30] A. Pietsch. Eigenvalues and ss-numbers. Cambridge University Press, Cambridge, 1987.
  • [31] A. S. Romanyuk. Best mm-term trigonometric approximations of besov classes of periodic functions of several variables. Izvestia RAN, Ser. Mat., 67:61–100, 2003.
  • [32] A. S. Romanyuk. Best trigonometric approximations for some classes of periodic functions of several variables in the uniform metric. Math. Notes, 82:216–228, 2007.
  • [33] A. S. Romanyuk and V. S. Romanyuk. Asymptotic estimates for the best trigonometric and bilinear approximations of classes of functions of several variables. Ukr. Math. J., 62:612–629, 2010.
  • [34] A. I. Stepanets. Approximation characteristics of spaces SφpS^{p}_{\varphi}. Ukr. Math. J., 53:446–475, 2001.
  • [35] A. I. Stepanets. Approximation characteristics of the space SφpS_{\varphi}^{p} in different metrics. Ukr. Math. J., 53:1340–1374, 2001.
  • [36] V. Temlyakov. Approximation of functions with bounded mixed derivative. Trudy MIAN, 178:1–112, 1986.
  • [37] V. Temlyakov. Greedy algorithms with regard to multivariate systems with special structure. Constr. Approx., 16:399–425, 2000.
  • [38] V. Temlyakov. Multivariate Approximation. Cambridge University Press, 2018.
  • [39] V. N. Temlyakov. Approximation of periodic functions of several variables by bilinear forms. Izvestiya AN SSSR, 50:137–155, 1986.
  • [40] V. N. Temlyakov. Constructive sparse trigonometric approximation and other problems for functions with mixed smoothness. Matem. Sb., 206:131–160, 2015.
  • [41] V. N. Temlyakov. Constructive sparse trigonometric approximation for functions with small mixed smoothness. Constr. Approx., 45:467–495, 2017.
  • [42] V. N. Temlyakov and T. Ullrich. Approximation of functions with small mixed smoothness in the uniform norm. arxiv.org/abs/2012.11983, 2020.