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Lebesgue-Type Inequalities for Quasi-greedy Bases

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Abstract

We show that, for quasi-greedy bases in real or complex Banach spaces, an optimal bound for the ratio between greedy N-term approximation ∥xG N x∥ and the best N-term approximation σ N (x) is controlled by max{μ(N),k N }, where μ(N) and k N are well-known constants that quantify the democracy and conditionality of the basis. In particular, for democratic bases this bound is O(logN). We show with various examples that these bounds are actually attained.

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Notes

  1. Normalization is assumed for notational convenience; all the results remain valid for semi-normalized bases, i.e., 0<c 1≤∥e j ∥≤c 2<∞, if the constants in the theorems are allowed to depend on c 1,c 2.

  2. In these definitions, it is understood that \(\frac{0}{0}=1\) (which may only happen if xΣ N ).

  3. Democracy is not explicitly stated, but follows easily from the inclusions 1BV 1,∞ as in [2, p. 239]. The fact that the Haar system is a basic sequence in BV (hence a basis in its closed linear span \({\mathbb{X}}\)), is a consequence of the uniform boundedness of the projections, see [26, Corollary 12]. Finally, it is a semi-normalized system with the normalization in (6.2); see [2, (1.6)].

  4. As usual, in \({\mathbb{X}}\oplus {\mathbb{Y}}\) one just writes x in place of (x,0) and y in place of (0,y).

  5. Various choices may happen in case of ties in the size of the coefficients of x.

  6. Personal communication, June 2012.

References

  1. Bechler, P.: Inequivalence of wavelet systems in \(L_{1}(\mathbb{R}^{d})\) and \(\mathrm{BV}(\mathbb{R}^{d})\). Bull. Pol. Acad. Sci., Math. 53(1), 25–37 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  2. Cohen, A., Dahmen, W., Daubechies, I., DeVore, R.: Harmonic analysis of the space BV. Rev. Mat. Iberoam. 19(1), 235–263 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  3. Cohen, A., DeVore, R., Petrushev, P., Xu, H.: Nonlinear approximation and the space BV(R 2). Am. J. Math. 121(3), 587–628 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  4. Dilworth, S.J., Kalton, N.J., Kutzarova, D.: On the existence of almost greedy bases in Banach spaces. Stud. Math. 159(1), 67–101 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  5. Dilworth, S.J., Kalton, N.J., Kutzarova, D., Temlyakov, V.N.: The thresholding greedy algorithm, greedy bases, and duality. Constr. Approx. 19, 575–597 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  6. Dilworth, S.J., Soto-Bajo, M., Temlyakov, V.N.: Quasi-greedy bases and Lebesgue-type inequalities. Stud. Math. 211, 41–69 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  7. Dilworth, S.J., Mitra, D.: A conditional quasi-greedy basis of 1. Stud. Math. 144, 95–100 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  8. Garrigós, G., Hernández, E., Martell, J.M.: Wavelets, Orlicz spaces and greedy bases. Appl. Comput. Harmon. Anal. 24, 70–93 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  9. Garrigós, G., Hernández, E., de Natividade, M.: Democracy functions and optimal embeddings for approximation spaces. Adv. Comput. Math. 37(2), 255–283 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  10. Garrigós, G., Wojtaszczyk, P.: Conditional quasi-greedy bases in Hilbert and Banach spaces. Indiana Univ. Math. J. (2013, to appear)

  11. Hajek, P., Montesinos Santalucia, V., Vanderwerff, J., Zizler, V.: Biorthogonal Systems in Banach Spaces. Springer, Berlin (2008)

    MATH  Google Scholar 

  12. Hernández, E.: Lebesgue-type inequalities for quasi-greedy bases. Preprint (2011). arXiv:1111.0460v2 [matFA]

  13. Kamont, A., Temlyakov, V.N.: Greedy approximation and the multivariate Haar system. Stud. Math. 161(3), 199–223 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  14. Konyagin, S.V., Temlyakov, V.N.: A remark on greedy approximation in Banach spaces. East J. Approx. 5, 365–379 (1999)

    MathSciNet  MATH  Google Scholar 

  15. Konyagin, S.V., Temlyakov, V.N.: Greedy approximation with regard to bases and general minimal systems. Serdica Math. J. 28(4), 305–328 (2002)

    MathSciNet  MATH  Google Scholar 

  16. Lindenstrauss, J.: On a certain subspace of 1. Bull. Acad. Polon. Sci. 12, 539–542 (1964)

    MathSciNet  MATH  Google Scholar 

  17. Lindenstrauss, J., Tzafriri, L.: Classical Banach Spaces, Vol I. Springer, Berlin (1977)

    Book  Google Scholar 

  18. Singer, I.: Bases in Banach Spaces, Vol. I. Springer, Berlin (1970)

    Book  Google Scholar 

  19. Temlyakov, V.N.: Greedy algorithm and n-term trigonometric approximation. Constr. Approx. 14, 569–587 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  20. Temlyakov, V.N.: Greedy approximation. Acta Numer. 17, 335–409 (2008)

    Article  MathSciNet  Google Scholar 

  21. Temlyakov, V.N.: Greedy Approximation. Cambridge University Press, Cambridge (2011)

    Book  MATH  Google Scholar 

  22. Temlyakov, V.N., Yang, M., Ye, P.: Greedy approximation with regard to non-greedy bases. Adv. Comput. Math. 34, 319–337 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  23. Temlyakov, V.N., Yang, M., Ye, P.: Lebesgue-type inequalities for greedy approximation with respect to quasi-greedy bases. East J. Approx. 17, 127–138 (2011)

    MathSciNet  Google Scholar 

  24. Wojtaszczyk, P.: Greedy algorithm for general biorthogonal systems. J. Approx. Theory 107, 293–314 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  25. Wojtaszczyk, P.: Greedy type bases in Banach spaces. In: Constructive Theory of Functions, pp. 136–155. DARBA, Sofia (2003)

    Google Scholar 

  26. Wojtaszczyk, P.: Projections and non-linear approximation in the space \(\mathrm {BV}(\mathbb{R}^{d})\). Proc. Lond. Math. Soc. 87(2), 471–497 (2003)

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

This work started when the second and third authors participated in the Concentration week on greedy algorithms in Banach spaces and compressed sensing held July 18–22, 2011, at Texas A&M University. We express our gratitude to the organizing committee for the invitation to participate in this meeting. In addition, the third author thanks the second author for arranging his visit to Universidad Autónoma de Madrid, where this work continued. A preliminary version of this paper ([12]) was written by the second author and posted in arXiv in November 2011.

First author partially supported by Grants MTM2010-16518 and MTM2011-25377 (Spain). Second author supported by Grant MTM2010-16518 (Spain). Third author supported by Simons Foundation Travel Grant 210060, and by a COR grant from University of California System.

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Correspondence to Eugenio Hernández.

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Communicated by: Vladimir N. Temlyakov.

Appendix: Proof of (2.1)

Appendix: Proof of (2.1)

The proof suggested in [5] for the inequalities in (2.1) is only valid for real scalars \(a_{k}\in {\mathbb{R}}\); we give below a minor modification of their argument that establishes (2.1) also for complex scalars a k . Below K denotes the quasi-greedy constant in \({\mathbb{X}}\).

The first two lemmas are similar to [24, Proposition 2].

Lemma 10.1

Let \(\{{\mathbf{e}}_{j}\}_{j=1}^{\infty}\) be a quasi-greedy basis in a Banach space \({\mathbb{X}}\). For all \(\beta_{j}\in {\mathbb{C}}\) with |β j |=1 and all finite sets A 1A, it holds that

$$ \biggl\|\sum_{j\in A_1}\beta_j{\mathbf{e}}_j \biggr\| \leq K \biggl\| \sum_{j\in A}\beta_j {\mathbf{e}}_j \biggr\|. $$
(10.1)

Proof

Call A 2=AA 1. For ε>0, define \(x=\sum_{j\in A_{1}}(1+{\varepsilon })\beta_{j}{\mathbf{e}}_{j}+\sum_{j\in A_{2}}\beta_{j}{\mathbf{e}}_{j}\). Then

$$\bigl\|G_{|A_1|}(x)\bigr\|=(1+{\varepsilon }) \biggl\|\sum_{j\in A_1} \beta_j{\mathbf{e}}_j \biggr\| \leq K\|x\|= \biggl\|(1+{\varepsilon })\sum _{j\in A_1}\beta_j{\mathbf{e}}_j+ \sum _{j\in A_2}\beta_j{\mathbf{e}}_j \biggr\|. $$

Letting ε→0, we obtain (10.1). □

Lemma 10.2

Let \(\{{\mathbf{e}}_{j}\}_{j=1}^{\infty}\) be a quasi-greedy basis in a Banach space \({\mathbb{X}}\). For all ε j ∈{±1,±i} and all finite sets A, it holds that

$$ \frac{1}{4K} \biggl\|\sum_{j\in A}{\mathbf{e}}_j \biggr\|\leq \biggl\| \sum_{j\in A}{\varepsilon }_j{\mathbf{e}}_j \biggr\|\leq 4K \biggl\|\sum_{j\in A}{\mathbf{e}}_j \biggr\|. $$
(10.2)

Proof

Call A k ={jA:ε j =i k}, k=1,2,3,4. Then, the triangle inequality and (10.1) (with all β j =1) give

$$\biggl\|\sum_{j\in A}{\varepsilon }_j{\mathbf{e}}_j \biggr\|\leq \sum_{k=1}^4 \biggl\|\sum _{j\in A_k}{\mathbf{e}}_j \biggr\|\leq 4K \biggl\|\sum _{j\in A}{\mathbf{e}}_j \biggr\|, $$

establishing the right-hand side of (10.2). Arguing similarly,

$$\biggl\|\sum_{j\in A}{\mathbf{e}}_j \biggr\|\leq\sum _{k=1}^4 \biggl\|\sum_{j\in A_k} {\mathbf{e}}_j \biggr\|\leq 4K \biggl\|\sum_{j\in A} {\varepsilon }_j{\mathbf{e}}_j \biggr\|, $$

where we have now used (10.1) with β j =ε j . □

Lemma 10.3

For all complex β=a+ib with |a|+|b|≤1 and for all \(x,y\in {\mathbb{X}}\), it holds that

$$ \|x+\beta y \|\leq \max \bigl\{\|x\pm y\|,\|x\pm i y\| \bigr\}. $$
(10.3)

Proof

We may assume that a∈[0,1). Then

$$\begin{aligned} \|x+\beta y\| \leq& \|ax + ay\|+ \bigl\|(1-a)x+iby\bigr\| \\ = & a\|x+y\|+(1-a)\|x+i{\gamma} y\|, \end{aligned}$$
(10.4)

where we have set γ=b/(1−a), which is a real number with |γ|≤1. Now

$$\begin{aligned} \|x+i{\gamma}y\| = & \biggl\|\frac{1-{\gamma}}{2}(x-iy)+\frac{1+{\gamma}}{2}(x+iy) \biggr\| \\ \leq &\frac{1-{\gamma}}{2} \|x-iy \|+\frac{1+{\gamma}}{2} \|x+iy \|\leq \max \|x\pm iy \|, \end{aligned}$$

where we have used that −1≤γ≤1. Inserting this into (10.4) easily leads to (10.3). □

We now justify the right-hand bound in (2.1). For a complex number α=a+ib, we shall write |α|1=|a|+|b|. Then, iterating the previous lemma, we obtain

$$\begin{aligned} \biggl\|\sum_{j\in A}{\alpha }_j{\mathbf{e}}_j \biggr\| \leq& \max_{j\in A}|{\alpha }_j|_1\max _{{\varepsilon }_j\in\{\pm1,\pm i\}} \biggl\|\sum_{j\in A} {\varepsilon }_j{\mathbf{e}}_j \biggr\| \\ \leq& 4\sqrt{2} K\max_{j\in A}|{\alpha }_j| \biggl\|\sum _{j\in A}{\mathbf{e}}_j \biggr\|, \end{aligned}$$
(10.5)

where in the last step we have used Lemma 10.2 and the trivial estimate \(|{\alpha }|_{1}\leq\sqrt{2} |{\alpha }|\).

We can now state a slightly more general version of Lemma 10.2.

Lemma 10.4

Let \(\{{\mathbf{e}}_{j}\}_{j=1}^{\infty}\) be a quasi-greedy basis in a Banach space \({\mathbb{X}}\). For all \({\varepsilon }_{j}\in {\mathbb{C}}\) with |ε j |=1 and all finite sets A, it holds that

$$ \frac{1}{4\sqrt{2} K} \biggl\|\sum_{j\in A}{\mathbf{e}}_j \biggr\|\leq \biggl\|\sum_{j\in A}{\varepsilon }_j{\mathbf{e}}_j \biggr\|\leq 4\sqrt{2} K \biggl\|\sum_{j\in A}{\mathbf{e}}_j \biggr\|. $$
(10.6)

Proof

The right-hand side is a special case of (10.5). To obtain the left-hand side, we consider the system \(\{{\tilde{{\mathbf{e}}}}_{j}:={\varepsilon }_{j}{\mathbf{e}}_{j}\}\), which is also a quasi-greedy basis in \({\mathbb{X}}\) with the same constant K. Thus, (10.5) for this system (with \({\alpha }_{j}=\bar{{\varepsilon }}_{j}\)) gives

$$\biggl\|\sum_{j\in A}\bar{{\varepsilon }}_j{\tilde{{\mathbf{e}}}}_j \biggr\|\leq 4\sqrt{2} K \biggl\|\sum_{j\in A}{\tilde{{\mathbf{e}}}}_j \biggr\|, $$

but this is the same as the left-hand side of (10.6). □

We turn now to the left-hand inequality in (2.1), for which we follow the arguments in [5, p. 579]. We shall prove that, if A is finite, then

$$ \biggl\|\sum_{j\in A}\alpha_j{\mathbf{e}}_j \biggr\| \geq \frac{1}{8\sqrt{2} K^2}\min_{j\in A}| {\alpha }_j| \biggl\|\sum_{j\in A}{\mathbf{e}}_j \biggr\|. $$
(10.7)

Write each scalar α j =ε j |α j |, with \({\varepsilon }_{j}\in {\mathbb{C}}\) such that |ε j |=1, and consider a permutation {j 1,…,j N } of A such that \(|{\alpha }_{j_{1}}|\geq|{\alpha }_{j_{2}}|\geq\cdots\geq|{\alpha }_{j_{N}}|\). Let x=∑ jA α j e j , and set G 0(x)=0. Then

$$\begin{aligned} &|{\alpha }_{j_N}| \Biggl\|\sum_{\ell=1}^N {\varepsilon }_{j_\ell} {\mathbf{e}}_{j_\ell} \Biggr\| \\ &\quad {} = |{\alpha }_{j_N}| \Biggl\|\sum _{\ell=1}^N\frac{1}{|{\alpha }_{j_\ell}|} \bigl(G_\ell (x)-G_{\ell-1}(x) \bigr) \Biggr\| \\ &\quad {} = |{\alpha }_{j_N}| \Biggl\|\sum_{\ell=1}^{N-1} \biggl(\frac{1}{|{\alpha }_{j_\ell }|}-\frac{1}{|{\alpha }_{j_{\ell+1}}|} \biggr)G_\ell(x)+ \frac{1}{|{\alpha }_{j_N}|}G_{N}(x) \Biggr\| \\ &\quad {} \leq |{\alpha }_{j_N}| \Biggl[\frac{1}{|{\alpha }_{j_N}|}+ \sum _{\ell=1}^{N-1} \biggl(\frac{1}{| {\alpha }_{j_{\ell+1}}|}-\frac{1}{|{\alpha }_{j_{\ell}}|} \biggr) \Biggr]K \|x \| \leq 2K \|x \|. \end{aligned}$$
(10.8)

On the other hand, by Lemma 10.4, the expression on the left of (10.8) can be estimated from below by \(|{\alpha }_{j_{N}}|\|\sum_{j\in A}{\mathbf{e}}_{j}\|/{4\sqrt{2} K}\), from which (10.7) follows.

Thus, putting together (10.5) and (10.7), we have shown:

Proposition 10.5

Let \(\{{\mathbf{e}}_{j}\}_{j=1}^{\infty}\) be a quasi-greedy basis in a Banach space \({\mathbb{X}}\). If A is finite and \({\alpha }_{j}\in {\mathbb{C}}\), then

$$\frac{1}{8\sqrt{2} K^2} \min_{j\in A}| {\alpha }_j| \biggl\|\sum_{j\in A}{\mathbf{e}}_j \biggr\| \leq \biggl\|\sum_{j\in A}\alpha_j {\mathbf{e}}_j \biggr\|\leq 4\sqrt{2} K \max_{j\in A}| {\alpha }_j| \biggl\|\sum_{j\in A}{\mathbf{e}}_j \biggr\|. $$

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Garrigós, G., Hernández, E. & Oikhberg, T. Lebesgue-Type Inequalities for Quasi-greedy Bases. Constr Approx 38, 447–470 (2013). https://doi.org/10.1007/s00365-013-9209-z

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