Skip to main content

Tractability of Multivariate Integration in Hybrid Function Spaces

  • Conference paper
  • First Online:
Monte Carlo and Quasi-Monte Carlo Methods

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 163))

Abstract

We consider tractability of integration in reproducing kernel Hilbert spaces which are a tensor product of a Walsh space and a Korobov space. The main result provides necessary and sufficient conditions for weak, polynomial, and strong polynomial tractability.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Aronszajn, N.: Theory of reproducing kernels. Trans. Am. Math. Soc. 68, 337–404 (1950)

    Article  MathSciNet  MATH  Google Scholar 

  2. Dick, J., Kuo, F.Y., Pillichshammer, F., Sloan, I.H.: Construction algorithms for polynomial lattice rules for multivariate integration. Math. Comput. 74, 1895–1921 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  3. Dick, J., Pillichshammer, F.: Multivariate integration in weighted Hilbert spaces based on Walsh functions and weighted Sobolev spaces. J. Complex. 21, 149–195 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  4. Dick, J., Pillichshammer, F.: Digital Nets and Sequences. Discrepancy Theory and Quasi-Monte Carlo Integration. Cambridge University Press, Cambridge (2010)

    Book  MATH  Google Scholar 

  5. Hellekalek, P.: Hybrid function systems in the theory of uniform distribution of sequences. In: Plaskota, L., Woźniakowski, H. (eds.) Monte Carlo and Quasi-Monte Carlo Methods 2010, pp. 435–449. Springer, Berlin (2012)

    Chapter  Google Scholar 

  6. Hellekalek, P., Kritzer, P.: On the diaphony of some finite hybrid point sets. Acta Arithmetica 156, 257–282 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  7. Hlawka, E.: Zur angenäherten Berechnung mehrfacher Integrale. Monatshefte für Mathematik 66, 140–151 (1962)

    Article  MathSciNet  MATH  Google Scholar 

  8. Hofer, R., Kritzer, P.: On hybrid sequences built of Niederreiter-Halton sequences and Kronecker sequences. Bull. Aust. Math. Soc. 84, 238–254 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  9. Hofer, R., Kritzer, P., Larcher, G., Pillichshammer, F.: Distribution properties of generalized van der Corput-Halton sequences and their subsequences. Int. J. Number Theory 5, 719–746 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  10. Hofer, R., Larcher, G.: Metrical results on the discrepancy of Halton-Kronecker sequences. Mathematische Zeitschrift 271, 1–11 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  11. Keller, A.: Quasi-Monte Carlo image synthesis in a nutshell. In: Dick, J., Kuo, F.Y., Peters, G.W., Sloan, I.H. (eds.) Monte Carlo and Quasi-Monte Carlo Methods, pp. 213–249. Springer, Berlin (2013)

    Google Scholar 

  12. Korobov, N.M.: Approximate evaluation of repeated integrals. Doklady Akademii Nauk SSSR 124, 1207–1210 (1959). (in Russian)

    MathSciNet  MATH  Google Scholar 

  13. Kritzer, P.: On an example of finite hybrid quasi-Monte Carlo Point Sets. Monatshefte für Mathematik 168, 443–459 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  14. Kritzer, P., Leobacher, G., Pillichshammer, F.: Component-by-component construction of hybrid point sets based on Hammersley and lattice point sets. In: Dick, J., Kuo, F.Y., Peters, G.W., Sloan, I.H. (eds.) Monte Carlo and Quasi-Monte Carlo Methods 2012, 501–515. Springer, Berlin (2013)

    Google Scholar 

  15. Kritzer, P., Pillichshammer, F.: On the existence of low-diaphony sequences made of digital sequences and lattice point sets. Mathematische Nachrichten 286, 224–235 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  16. Kuo, F.Y., Joe, S.: Component-by-component construction of good lattice rules with a composite number of points. J. Complex. 18, 943–976 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  17. Larcher, G.: Discrepancy estimates for sequences: new results and open problems. In: Kritzer, P., Niederreiter, H., Pillichshammer, F., Winterhof, A. (eds.) Uniform Distribution and Quasi-Monte Carlo Methods, Radon Series in Computational and Applied Mathematics, 171–189. DeGruyter, Berlin (2014)

    Google Scholar 

  18. Niederreiter, H.: Low-discrepancy point sets obtained by digital constructions over finite fields. Czechoslovak Mathematical Journal 42, 143–166 (1992)

    MathSciNet  MATH  Google Scholar 

  19. Novak, E., Woźniakowski, H.: Tractability of Multivariate Problems, Volume I: Linear Information. EMS, Zurich (2008)

    Google Scholar 

  20. Novak, E., Woźniakowski, H.: Tractability of Multivariate Problems, Volume II: Standard Information for Functionals. EMS, Zurich (2010)

    Google Scholar 

  21. Novak, E., Woźniakowski, H.: Tractability of Multivariate Problems, Volume III: Standard Information for Operators. EMS, Zurich (2012)

    Google Scholar 

  22. Sloan, I.H., Woźniakowski, H.: Tractability of multivariate integration for weighted Korobov classes. J. Complex. 17, 697–721 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  23. Traub, J.F., Wasilkowski, G.W., Woźniakowski, H.: Information-Based Complexity. Academic Press, New York (1988)

    MATH  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the anonymous referees for their remarks which helped to improve the presentation of this paper. P. Kritzer is supported by the Austrian Science Fund (FWF), Projects P23389-N18 and F05506-26. The latter is part of the Special Research Program “Quasi-Monte Carlo Methods: Theory and Applications”. F. Pillichshammer is supported by the Austrian Science Fund (FWF) Project F5509-N26, which is part of the Special Research Program “Quasi-Monte Carlo Methods: Theory and Applications”.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Peter Kritzer .

Editor information

Editors and Affiliations

Appendix: The Proof of Theorem 3

Appendix: The Proof of Theorem 3

Proof

We show the result by an inductive argument. We start our considerations by dealing with the case where \(d_1=d_2=1\). According to Algorithm 1, we have chosen \(g_1=1\in G_{b,m}\) and \(z_1\in Z_N\) such that \(e^2_{(1,1),{\varvec{\alpha }},{\varvec{\gamma }}}(g_1,z_1)\) is minimized as a function of \(z_1\). In the following, we denote the points generated by \((g,z)\in G_{b,m}\times Z_N\) by \((x_n (g), y_n (z))\).

According to Eq. (10), we have

$$ e^2_{(1,1),{\varvec{\alpha }},{\varvec{\gamma }}}(g_1,z_1)= e^2_{1,\alpha _1,\gamma ^{(1)}}(1)+\theta _{(1,1)}(z_1), $$

where \(e^2_{1,\alpha _1,\gamma ^{(1)}}(1)\) denotes the squared worst-case error of the polynomial lattice rule generated by 1 in the Walsh space \({\mathscr {H}}(K^{\mathrm{Wal}}_{1,\alpha _1,\gamma ^{(1)}})\), and where

$$\begin{aligned}&\theta _{(1,1)}(z_1):=\frac{\gamma _{1}^{(2)}}{N^2} \sum _{n,n'=0}^{N-1} \left( 1+\gamma _1^{(1)}\sum _{k_1\in {\mathbb {N}}}\frac{\mathrm{wal}_{k_1}(x_{n,1}(1) \ominus x_{n',1}(1))}{b^{\alpha _1 \lfloor \log _b k_1\rfloor }}\right) \\&\qquad \qquad \qquad \qquad \qquad \qquad \qquad \qquad \times \sum _{l_1\in {\mathbb {Z}}\setminus \{0\}} \frac{\mathrm {e}_{l_1}(y_{n,1}(z_1)-y_{n',1}(z_1))}{\left| l_1\right| ^{\alpha _2}}. \end{aligned}$$

By results in [2], we know that

$$\begin{aligned} e^2_{1,\alpha _1,\gamma ^{(1)}}(1) \le \frac{2}{N}\left( 1+\gamma _1^{(1)}\mu (\alpha _1)\right) . \end{aligned}$$
(11)

Then, as \(z_1\) was chosen to minimize the error,

$$\begin{aligned}&{\theta _{(1,1)}(z_1)\le \frac{1}{\phi (N)} \sum _{z\in Z_N} \theta _{(1,1)}(z)}\\&=\frac{\gamma _{1}^{(2)}}{N^2} \sum _{n,n'=0}^{N-1} \left( 1+\gamma _1^{(1)}\sum _{k_1\in {\mathbb {N}}}\frac{\mathrm{wal}_{k_1}(x_{n,1}(1) \ominus x_{n',1}(1))}{b^{\alpha _1 \lfloor \log _b k_1\rfloor }}\right) \\&\,\,\times \frac{1}{\phi (N)} \sum _{z\in Z_N} \sum _{l_1\in {\mathbb {Z}}\setminus \{0\}} \frac{\mathrm {e}_{l_1}(y_{n,1}(z)-y_{n',1}(z))}{\left| l_1\right| ^{\alpha _2}}\\&\le \gamma _{1}^{(2)} \left( 1+\gamma _1^{(1)}\mu (\alpha _1)\right) \varSigma _B, \end{aligned}$$

where

$$\begin{aligned} \varSigma _B:= & {} \frac{1}{N^2}\sum _{n=0}^{N-1}\sum _{n'=0}^{N-1} \left| \frac{1}{\phi (N)} \sum _{z\in Z_N} \sum _{l_1\in {\mathbb {Z}}\setminus \{0\}} \frac{\mathrm {e}^{2\pi \mathtt {i}(n-n')zl_1/N}}{\left| l_1\right| ^{\alpha _2}}\right| \\= & {} \frac{1}{N}\sum _{n=1}^{N} \left| \frac{1}{\phi (N)} \sum _{z\in Z_N} \sum _{l_1\in {\mathbb {Z}}\setminus \{0\}} \frac{\mathrm {e}^{2\pi \mathtt {i}n z l_1/N}}{\left| l_1\right| ^{\alpha _2}}\right| , \end{aligned}$$

since the inner sum in the second line always has the same value. We now use [16, Lemmas 2.1 and 2.3] and obtain \(\varSigma _B\le 4\zeta (\alpha _2)N^{-1}\), where we used that N has only one prime factor. Hence we obtain

$$\begin{aligned} \theta _{(1,1)}(z_1)\le \frac{\gamma _{1}^{(2)}}{N}\left( 1+\gamma _1^{(1)}\mu (\alpha _1)\right) 4\zeta (\alpha _2). \end{aligned}$$
(12)

Combining Eqs. (11) and (12) yields the desired bound for \((g_1,z_1)\).

Let us now assume \(d_1\in [s_1]\) and \(d_2\in [s_2]\) and that we have already found generating vectors \({\varvec{g}}_{d_1}^*\) and \({\varvec{z}}_{d_2}^*\) such that the bound in Theorem 3 is satisfied.

In what follows, we are going to distinguish two cases: In the first case, we assume that \(d_1<s_1\) and add a component \(g_{d_1+1}\) to \({\varvec{g}}_{d_1}^*\), and in the second case, we assume that \(d_2<s_2\) and add a component \(z_{d_2+1}\) to \({\varvec{z}}_{d_2}^*\). In both cases, we will show that the corresponding bounds on the squared worst-case errors hold.

Let us first consider the case where we start from \(({\varvec{g}}_{d_1}^*,{\varvec{z}}_{d_2}^*)\) and add, by Algorithm 1, a component \(g_{d_1+1}\) to \({\varvec{g}}_{d_1}^*\). According to Eq. (10), we have

$$\begin{aligned} e^2_{(d_1+1,d_2),{\varvec{\alpha }},{\varvec{\gamma }}}(({\varvec{g}}_{d_1}^*,g_{d_1+1}),{\varvec{z}}_{d_2}^*)=e^2_{(d_1,d_2),{\varvec{\alpha }},{\varvec{\gamma }}}({\varvec{g}}_{d_1}^*,{\varvec{z}}_{d_2}^*)+ \theta _{(d_1+1,d_2)}(g_{d_1+1}), \end{aligned}$$

where

$$\begin{aligned}&{\theta _{(d_1+1,d_2)}(g_{d_1+1})}\\&:=\frac{\gamma _{d_1 +1}^{(1)}}{N^2} \sum _{n,n'=0}^{N-1} \left[ \prod _{j=1}^{d_1}\left( 1+\gamma _j^{(1)}\sum _{k\in {\mathbb {N}}}\frac{\mathrm{wal}_{k}(x_{n,j}(g_j) \ominus x_{n',j}(g_j))}{b^{\alpha _1\lfloor \log _b k \rfloor }}\right) \right] \\&\quad \times \left[ \prod _{j=1}^{d_2}\left( 1+\gamma _j^{(2)}\sum _{l\in {\mathbb {Z}}\setminus \{0\}}\frac{\mathrm {e}_{l}(y_{n,j}(z_j) - y_{n',j}(z_j))}{\left| l\right| ^{\alpha _2}}\right) \right] \\&\quad \times \sum _{k\in {\mathbb {N}}}\frac{\mathrm{wal}_{k}(x_{n,d_1+1}(g_{d_1+1}) \ominus x_{n',d_1+1}(g_{d_1+1}))}{b^{\alpha _1\lfloor \log _b k \rfloor }}. \end{aligned}$$

However, by the assumption, we know that

$$\begin{aligned} e^2_{(d_1,d_2),{\varvec{\alpha }},{\varvec{\gamma }}}({\varvec{g}}_{d_1}^*,{\varvec{z}}_{d_2}^*) \le \frac{2}{N} \prod _{j=1}^{d_1}\left( 1+\gamma _j^{(1)}2\mu (\alpha _1)\right) \prod _{j=1}^{d_2}\left( 1+\gamma _j^{(2)}4\zeta (\alpha _2)\right) . \end{aligned}$$
(13)

Furthermore, as \(g_{d_1+1}\) was chosen to minimize the error,

$$\begin{aligned}&{\theta _{(d_1+1,d_2)}(g_{d_1+1})\le \frac{1}{N} \sum _{g\in G_{b,m}} \theta _{(d_1+1,d_2)}(g)}\\&\le \gamma _{d_1 +1}^{(1)}\left[ \prod _{j=1}^{d_1}\left( 1+\gamma _j^{(1)}\mu (\alpha _1)\right) \right] \left[ \prod _{j=1}^{d_2}\left( 1+\gamma _j^{(2)}2\zeta (\alpha _2)\right) \right] \varSigma _C, \end{aligned}$$

where

$$\begin{aligned} \varSigma _C:= & {} \frac{1}{N^2}\sum _{n,n'=0}^{N-1}\left| \frac{1}{N} \sum _{g\in G_{b,m}}\sum _{k\in {\mathbb {N}}}\frac{\mathrm{wal}_{k}(x_{n,d_1+1}(g) \ominus x_{n',d_1+1}(g))}{b^{\alpha _1\lfloor \log _b k \rfloor }}\right| \\= & {} \frac{1}{N^2}\sum _{n=0}^{N-1}\sum _{n'=0}^{N-1}\left| \frac{1}{N} \sum _{g\in G_{b,m}}\sum _{k\in {\mathbb {N}}}\frac{\mathrm{wal}_{k}(x_{n\ominus n',d_1+1}(g))}{b^{\alpha _1\lfloor \log _b k \rfloor }}\right| \\= & {} \frac{1}{N}\sum _{n=0}^{N-1}\left| \frac{1}{N} \sum _{g\in G_{b,m}}\sum _{k\in {\mathbb {N}}} \frac{\mathrm{wal}_{k}(x_{n,d_1+1}(g))}{b^{\alpha _1\lfloor \log _b k \rfloor }}\right| , \end{aligned}$$

where we used the group structure of the polynomial lattice points (see [4, Sect. 4.4.4]) in order to get from the first to the second line and where we again used that the inner sum in the second line always has the same value. We now write

$$\begin{aligned} \varSigma _C=&\frac{1}{N}\sum _{k\in {\mathbb {N}}}\frac{1}{b^{\alpha _1\lfloor \log _b k \rfloor }} +\frac{1}{N}\sum _{n=1}^{N-1}\left| \frac{1}{N} \sum _{g\in G_{b,m}}\sum _{k\in {\mathbb {N}}} \frac{\mathrm{wal}_{k}(x_{n,d_1+1}(g))}{b^{\alpha _1\lfloor \log _b k \rfloor }}\right| \\ =&\frac{\mu (\alpha _1)}{N} +\frac{1}{N}\sum _{n=1}^{N-1}\left| \frac{1}{N} \sum _{g\in G_{b,m}}\sum _{k\in {\mathbb {N}}} \frac{\mathrm{wal}_{k}(x_{n,d_1+1}(g))}{b^{\alpha _1\lfloor \log _b k \rfloor }}\right| . \end{aligned}$$

Let now \(n\in \{1,\ldots ,N-1\}\) be fixed, and consider the term

$$\begin{aligned} \varSigma _{C,n} :=&\frac{1}{N} \sum _{g\in G_{b,m}}\sum _{k\in {\mathbb {N}}} \frac{\mathrm{wal}_{k}(x_{n,d_1+1}(g))}{b^{\alpha _1\lfloor \log _b k \rfloor }}\\ =&\sum _{\begin{array}{c} k\in {\mathbb {N}}\\ k\equiv 0 (N) \end{array}}\frac{1}{N} \sum _{g\in G_{b,m}} \frac{\mathrm{wal}_{k}(x_{n,d_1+1}(g))}{b^{\alpha _1\lfloor \log _b k \rfloor }}+\sum _{\begin{array}{c} k\in {\mathbb {N}}\\ k\not \equiv 0 (N) \end{array}}\frac{1}{N} \sum _{g\in G_{b,m}} \frac{\mathrm{wal}_{k}(x_{n,d_1+1}(g))}{b^{\alpha _1\lfloor \log _b k \rfloor }}\\ =:&\varSigma _{C,n,1}+\varSigma _{C,n,2}. \end{aligned}$$

By results in [2],

$$\varSigma _{C,n,1}=\sum _{\begin{array}{c} k\in {\mathbb {N}}\\ k\equiv 0 (N) \end{array}}\frac{1}{b^{\alpha _1\lfloor \log _b k \rfloor }}= \frac{\mu (\alpha _1)}{b^{m\alpha }}\le \frac{\mu (\alpha _1)}{N}.$$

Furthermore,

$$\begin{aligned} \varSigma _{C,n,2}= & {} \sum _{\begin{array}{c} k\in {\mathbb {N}}\\ k\not \equiv 0 (N) \end{array}} \frac{1}{b^{\alpha _1\lfloor \log _b k \rfloor }}\frac{1}{N} \sum _{g\in G_{b,m}} \mathrm{wal}_{k}(x_{n,d_1+1}(g))\\= & {} \sum _{\begin{array}{c} k\in {\mathbb {N}}\\ k\not \equiv 0 (N) \end{array}}\frac{1}{b^{\alpha _1\lfloor \log _b k \rfloor }}\frac{1}{N} \sum _{g=0}^{b^m-1}\mathrm{wal}_{k}\left( \frac{g}{b^m}\right) , \end{aligned}$$

where we used that

$$\begin{aligned} \sum _{g\in G_{b,m}}\mathrm{wal}_{k}(x_{n,d_1+1}(g))=&\sum _{g\in G_{b,m}}\mathrm{wal}_{k} \left( \nu _m \left( \frac{n(x)g(x)}{f(x)}\right) \right) \\ =&\sum _{g\in G_{b,m}}\mathrm{wal}_{k} \left( \nu _m \left( \frac{g(x)}{f(x)}\right) \right) =\sum _{g=0}^{b^m-1}\mathrm{wal}_{k}\left( \frac{g}{b^m}\right) , \end{aligned}$$

since \(n\ne 0\) and since g takes on all values in \(G_{b,m}\), and f is irreducible. However, \( \sum _{g=0}^{b^m-1}\mathrm{wal}_{k}\left( \frac{g}{b^m}\right) =0\) and so \(\varSigma _{C,n,2}=0\). This yields \(\left| \varSigma _{C,n}\right| \le \mu (\alpha _1) N^{-1}\) and \(\varSigma _C \le 2\mu (\alpha _1)N^{-1}\), which in turn implies

$$\begin{aligned} \theta _{(d_1+1,d_2)}(g_{d_1+1})\le&\frac{2\gamma _{d_1 +1}^{(1)}\mu (\alpha _1)}{N} \prod _{j=1}^{d_1}\left( 1+\gamma _j^{(1)}\mu (\alpha _1)\right) \prod _{j=1}^{d_2}\left( 1+\gamma _j^{(2)}2\zeta (\alpha _2)\right) . \end{aligned}$$

Combining the latter result with Eq. (13), we obtain

$$\begin{aligned} e^2_{(d_1+1,d_2),{\varvec{\alpha }},{\varvec{\gamma }}}(({\varvec{g}}_{d_1}^*,g_{d_1+1}),{\varvec{z}}_{d_2}^*))\le&\frac{2}{N} \prod _{j=1}^{d_1+1}\left( 1+2\gamma _j^{(1)}\mu (\alpha _1)\right) \prod _{j=1}^{d_2}\left( 1+\gamma _j^{(2)}4\zeta (\alpha _2)\right) . \end{aligned}$$

The case where we start from \(({\varvec{g}}_{d_1}^*,{\varvec{z}}_{d_2}^*)\) and add, by Algorithm 1, a component \(z_{d_2+1}\) to \({\varvec{z}}_{d_2}^*\) can be shown by a similar reasoning. We just sketch the basic points: According to Eq. (10), we have

$$\begin{aligned} e^2_{(d_1,d_2+1),{\varvec{\alpha }},{\varvec{\gamma }}}({\varvec{g}}_{d_1}^*,({\varvec{z}}_{d_2}^*,z_{d_2+1}))= e^2_{(d_1,d_2),{\varvec{\alpha }},{\varvec{\gamma }}}({\varvec{g}}_{d_1}^*,{\varvec{z}}_{d_2}^*)+\theta _{(d_1,d_2+1)}(z_{d_2+1}), \end{aligned}$$

where \(e^2_{(d_1,d_2),{\varvec{\alpha }},{\varvec{\gamma }}}({\varvec{g}}_{d_1}^*,{\varvec{z}}_{d_2}^*)\) satisfies (13) and where

$$\begin{aligned} \theta _{(d_1,d_2+1)}(z_{d_2+1})\le \gamma _{d_2 +1}^{(1)}\left[ \prod _{j=1}^{d_1}\left( 1+\gamma _j^{(1)}\mu (\alpha _1)\right) \right] \left[ \prod _{j=1}^{d_2}\left( 1+\gamma _j^{(2)}2\zeta (\alpha _2)\right) \right] \varSigma _D, \end{aligned}$$

with

$$\begin{aligned} \varSigma _D=\frac{1}{N}\sum _{n=0}^{N-1}\left| \frac{1}{\phi (N)} \sum _{z\in Z_N} \sum _{l\in {\mathbb {Z}}\setminus \{0\}}\frac{\mathrm {e}^{2\pi \mathtt {i}nzl/N}}{\left| l\right| ^{\alpha _2}}\right| \le \frac{4\zeta (\alpha _2)}{N}, \end{aligned}$$

according to [16, Lemmas 2.1 and 2.3]. This implies

$$\theta _{(d_1,d_2+1)}(z_{d_2+1})\le \frac{\gamma _{d_2 +1}^{(1)}4\zeta (\alpha _2)}{N} \prod _{j=1}^{d_1}\left( 1+\gamma _j^{(1)}\mu (\alpha _1)\right) \prod _{j=1}^{d_2}\left( 1+\gamma _j^{(2)}2\zeta (\alpha _2)\right) .$$

Combining these results we obtain the desired bound. \(\square \)

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Kritzer, P., Pillichshammer, F. (2016). Tractability of Multivariate Integration in Hybrid Function Spaces. In: Cools, R., Nuyens, D. (eds) Monte Carlo and Quasi-Monte Carlo Methods. Springer Proceedings in Mathematics & Statistics, vol 163. Springer, Cham. https://doi.org/10.1007/978-3-319-33507-0_22

Download citation

Publish with us

Policies and ethics