Discrepancy of Hammersley points in Besov spaces of dominating mixed smoothness

2010 ◽  
Vol 283 (3) ◽  
pp. 478-488 ◽  
Author(s):  
Aicke Hinrichs
Keyword(s):  
Besov Spaces ◽  
Dominating Mixed Smoothness ◽  
Mixed Smoothness

scholarly journals Pointwise multipliers for Besov spaces of dominating mixed smoothness - II

2017 ◽  
Vol 60 (11) ◽  
pp. 2241-2262 ◽  
Author(s):  
Van Kien Nguyen ◽  
Winfried Sickel
Keyword(s):  
Besov Spaces ◽  
Pointwise Multipliers ◽  
Dominating Mixed Smoothness ◽  
Mixed Smoothness

The Jawerth–Franke Embedding of Spaces with Dominating Mixed Smoothness

2009 ◽  
Vol 16 (4) ◽  
pp. 667-682
Author(s):  
Markus Hansen ◽  
Jan Vybíral
Keyword(s):  
Function Spaces ◽  
Dominating Mixed Smoothness ◽  
Mixed Smoothness

Abstract We give a proof of the Jawerth embedding for function spaces with dominating mixed smoothness of Besov and Triebel–Lizorkin type where 0 < 𝑝0 < 𝑝1 ≤ ∞ and 0 < 𝑞0,𝑞1 ≤ ∞ and with If 𝑝1 < ∞, we prove also the Franke embedding Our main tools are discretization by a wavelet isomorphism and multivariate rearrangements.

scholarly journals Function Values Are Enough for $$L_2$$-Approximation

2020 ◽  
Author(s):  
David Krieg ◽  
Mario Ullrich
Keyword(s):  
Hilbert Space ◽  
Fourier Coefficients ◽  
Approximation Number ◽  
Approximation Numbers ◽  
Approximation Of Functions ◽  
Worst Case ◽  
Linear Information ◽  
Grid Algorithm ◽  
Dominating Mixed Smoothness ◽  
Mixed Smoothness

AbstractWe study the $$L_2$$ L 2 -approximation of functions from a Hilbert space and compare the sampling numbers with the approximation numbers. The sampling number $$e_n$$ e n is the minimal worst-case error that can be achieved with n function values, whereas the approximation number $$a_n$$ a n is the minimal worst-case error that can be achieved with n pieces of arbitrary linear information (like derivatives or Fourier coefficients). We show that $$\begin{aligned} e_n \,\lesssim \, \sqrt{\frac{1}{k_n} \sum _{j\ge k_n} a_j^2}, \end{aligned}$$ e n ≲ 1 k n ∑ j ≥ k n a j 2 , where $$k_n \asymp n/\log (n)$$ k n ≍ n / log ( n ) . This proves that the sampling numbers decay with the same polynomial rate as the approximation numbers and therefore that function values are basically as powerful as arbitrary linear information if the approximation numbers are square-summable. Our result applies, in particular, to Sobolev spaces $$H^s_\mathrm{mix}(\mathbb {T}^d)$$ H mix s ( T d ) with dominating mixed smoothness $$s>1/2$$ s > 1 / 2 and dimension $$d\in \mathbb {N}$$ d ∈ N , and we obtain $$\begin{aligned} e_n \,\lesssim \, n^{-s} \log ^{sd}(n). \end{aligned}$$ e n ≲ n - s log sd ( n ) . For $$d>2s+1$$ d > 2 s + 1 , this improves upon all previous bounds and disproves the prevalent conjecture that Smolyak’s (sparse grid) algorithm is optimal.

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