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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aronszajn, N.: Theory of reproducing kernels. Trans. Am. Math. Soc. 68, 337–404 (1950)
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)
Dick, J., Pillichshammer, F.: Multivariate integration in weighted Hilbert spaces based on Walsh functions and weighted Sobolev spaces. J. Complex. 21, 149–195 (2005)
Dick, J., Pillichshammer, F.: Digital Nets and Sequences. Discrepancy Theory and Quasi-Monte Carlo Integration. Cambridge University Press, Cambridge (2010)
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)
Hellekalek, P., Kritzer, P.: On the diaphony of some finite hybrid point sets. Acta Arithmetica 156, 257–282 (2012)
Hlawka, E.: Zur angenäherten Berechnung mehrfacher Integrale. Monatshefte für Mathematik 66, 140–151 (1962)
Hofer, R., Kritzer, P.: On hybrid sequences built of Niederreiter-Halton sequences and Kronecker sequences. Bull. Aust. Math. Soc. 84, 238–254 (2011)
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)
Hofer, R., Larcher, G.: Metrical results on the discrepancy of Halton-Kronecker sequences. Mathematische Zeitschrift 271, 1–11 (2012)
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)
Korobov, N.M.: Approximate evaluation of repeated integrals. Doklady Akademii Nauk SSSR 124, 1207–1210 (1959). (in Russian)
Kritzer, P.: On an example of finite hybrid quasi-Monte Carlo Point Sets. Monatshefte für Mathematik 168, 443–459 (2012)
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)
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)
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)
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)
Niederreiter, H.: Low-discrepancy point sets obtained by digital constructions over finite fields. Czechoslovak Mathematical Journal 42, 143–166 (1992)
Novak, E., Woźniakowski, H.: Tractability of Multivariate Problems, Volume I: Linear Information. EMS, Zurich (2008)
Novak, E., Woźniakowski, H.: Tractability of Multivariate Problems, Volume II: Standard Information for Functionals. EMS, Zurich (2010)
Novak, E., Woźniakowski, H.: Tractability of Multivariate Problems, Volume III: Standard Information for Operators. EMS, Zurich (2012)
Sloan, I.H., Woźniakowski, H.: Tractability of multivariate integration for weighted Korobov classes. J. Complex. 17, 697–721 (2001)
Traub, J.F., Wasilkowski, G.W., Woźniakowski, H.: Information-Based Complexity. Academic Press, New York (1988)
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
Corresponding author
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
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
By results in [2], we know that
Then, as \(z_1\) was chosen to minimize the error,
where
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
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
where
However, by the assumption, we know that
Furthermore, as \(g_{d_1+1}\) was chosen to minimize the error,
where
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
Let now \(n\in \{1,\ldots ,N-1\}\) be fixed, and consider the term
By results in [2],
Furthermore,
where we used that
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
Combining the latter result with Eq. (13), we obtain
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
where \(e^2_{(d_1,d_2),{\varvec{\alpha }},{\varvec{\gamma }}}({\varvec{g}}_{d_1}^*,{\varvec{z}}_{d_2}^*)\) satisfies (13) and where
with
according to [16, Lemmas 2.1 and 2.3]. This implies
Combining these results we obtain the desired bound. \(\square \)
Rights 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
DOI: https://doi.org/10.1007/978-3-319-33507-0_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-33505-6
Online ISBN: 978-3-319-33507-0
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)