Abstract
We study the numerical integration of functions depending on an infinite number of variables. We provide lower error bounds for general deterministic algorithms and provide matching upper error bounds with the help of suitable multilevel algorithms and changing-dimension algorithms. More precisely, the spaces of integrands we consider are weighted, reproducing kernel Hilbert spaces with norms induced by an underlying anchored function space decomposition. Here the weights model the relative importance of different groups of variables. The error criterion used is the deterministic worst-case error. We study two cost models for function evaluations that depend on the number of active variables of the chosen sample points, and we study two classes of weights, namely product and order-dependent weights and the newly introduced finite projective dimension weights. We show for these classes of weights that multilevel algorithms achieve the optimal rate of convergence in the first cost model while changing-dimension algorithms achieve the optimal convergence rate in the second model. As an illustrative example, we discuss the anchored Sobolev space with smoothness parameter \(\alpha \) and provide new optimal quasi-Monte Carlo multilevel algorithms and quasi-Monte Carlo changing-dimension algorithms based on higher-order polynomial lattice rules.
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Notes
In [10] it was actually called “variable subspace sampling model”. We have chosen a different name to emphasize the difference between this model and the “unrestricted subspace sampling model” explained below.
In [36] the cost model did not receive a specific name.
We chose this notion since it seems to us to be consistent with the common notion of tractability in the multivariate setting. A more precise notion would be strongly polynomially tractable, to distinguish this kind of tractability from more general notions of tractability as introduced in [27]; see also [42]. But for convenience we stay with the shorter notion of strongly tractable.
Recall that polynomial lattice rules consist of \(n\) points, where \(n\) is a power of a prime \(b\). If required to construct a quadrature rule consisting of \(n\) points, \(n\in \mathbb {N}\) arbitrary, we generate a polynomial lattice rule consisting of \(b^m\) points, \(b^m\le n <b^{m+1}\), and simply set the quadrature weights corresponding to the “missing” \(n-b^m\) points to zero.
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Acknowledgments
We want to thank Michael Griebel for suggesting that we study algorithms for infinite-dimensional integration of higher-order convergence. We are grateful for the opportunity to work at the Hausdorff Institute in Bonn, where the work on this paper was initiated. Furthermore, we want to thank Greg Wasilkowski, Henryk Woźniakowski, and two anonymous referees for valuable comments. Josef Dick is supported by an ARC Queen Elizabeth II Fellowship. Michael Gnewuch was supported by the German Science Foundation DFG under Grant GN 91/3-1 and by the Australian Research Council.
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Appendix
Appendix
Here we provide a detailed proof of Lemma 6.
Lemma 8
Let \(r \!>\! 1\) be a real number, and define the POD weights \(\gamma _u \!=\! \Gamma _{|u|} \prod _{j\in u} j^{-r}\) for \(u \in {\mathcal {U}}\). Then there is a constant \(c_r > 0\) such that
If \(r \ge 2\), then there is a constant \(C_r > 0\) such that
Note that \(\sin x < x\) for \(x > 0\); thus, \(\sin \pi /r < \pi /r\), which implies
Proof
We have
where \(\zeta (\underbrace{r,\ldots , r}_{k \text{ times }})\) is the multiple Hurwitz zeta function.
The general behavior of the multiple Hurwitz zeta function is given in [8, Eq. (48)]. From [8, p. 8] it is known that if \(r \ge 2\) is an even integer, then
where \(R_{r,j}\) are some numbers with \(|R_{r,j}| < 1\) and \(N_{r}\) is a positive integer satisfying \(N_r < 2^{r/2}/r\). From Stirling’s formula we obtain
where \(f(k) \asymp _k g(k)\) means that there are constants \(C,c> 0\) independent of \(k\) such that \(c g(k) \le f(k) \le C g(k)\). Thus,
Thus, for any fixed positive even integer \(r\) we have
Therefore, (61) follows since decreasing \(r\) only increases the sum \(\sum _{u \in {\mathcal {U}}} \gamma _u\), and the result holds for all even integers \(r \ge 2\), as shown earlier.
Now assume that \(r \ge 2\). For \(1/r < \lambda \le 1\) we have by Jensen’s inequality that
Choose \(1/r < \lambda \le 1\) such that \(\lambda r\) is the largest even integer less than or equal to \(r\). Then
for some constant \(C_r > 0\). Thus,
from which (62) follows. \(\square \)
Corollary 8
Let \(\varvec{\gamma }= (\gamma _u)_{u \in {\mathcal {U}}}\) be POD weights with \(\gamma _u = \Gamma _{|u|} \prod _{j\in u} \gamma _j\). Let \(p^*:={{\mathrm{decay}}}_{\varvec{\gamma },1} < \infty \). Further, let \(c, c_0 > 0\) be constants such that
If for some \(q \le p^*/2\) we have
then \(\mathrm {decay}_{\varvec{\gamma },\infty } \ge q\).
On the other hand, if for \(q < p^*\) we have
then \(\mathrm {decay}_{\varvec{\gamma },\infty } \le q\).
Proof
We have
Thus, we have for some \(q \le p^*/2\)
that the right-hand side is finite, then \(\mathrm {decay}_{\varvec{\gamma },\infty } \ge q\).
On the other hand, for \(q < p^*\) we have
If the right-hand side is infinite for some \(q < p^*\), then \(\mathrm {decay}_{\varvec{\gamma },\infty } \le q\).
We suspect that the condition \(q \le p^*/2\) in the preceding Corollary can be replaced by \(q \le p^*\).
The preceding corollary allows us to construct an example of POD weights where
For instance, let \(\gamma _j = j^{-p^*}\). Thus, \(\mathrm {decay}_{\varvec{\gamma },1} = p^*\) and \(c_0 = c = 1\) in the preceding corollary. Let \(q^*\) be such that \(p^*/(2q^*) \in \mathbb {N}\). For \(k \in \mathbb {N}_0\) let
Then we have for \(q=q^*\) that (64) is of the same form as (63), which is
Due to (64), we have \({{\mathrm{decay}}}_{\varvec{\gamma },\infty } \le q^*\).
Now let \(q<q^*\) such that \(\lfloor p^*/2q \rfloor = p^*/2q^*\). For this \(q\) the left-hand side of (63) is
Thus, (63) gives us \({{\mathrm{decay}}}_{\varvec{\gamma },\infty } \ge q\).
Together with Lemma 3, this establishes Lemma 6.
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Dick, J., Gnewuch, M. Infinite-Dimensional Integration in Weighted Hilbert Spaces: Anchored Decompositions, Optimal Deterministic Algorithms, and Higher-Order Convergence. Found Comput Math 14, 1027–1077 (2014). https://doi.org/10.1007/s10208-014-9198-8
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DOI: https://doi.org/10.1007/s10208-014-9198-8
Keywords
- Path integration
- Multilevel algorithms
- Changing-dimension algorithms
- Quasi-Monte Carlo methods
- Polynomial lattice rules
- Reproducing kernel Hilbert spaces