In this paper we study multivariate integration for a weighted Korobov space for which the Fourier coefficients of the functions decay exponentially fast. This implies that the functions of this space are infinitely times differentiable. Weights of the Korobov space monitor the influence of each variable and each group of variables. We show that there are numerical integration rules which achieve an exponential convergence of the worst-case integration error. We also investigate the dependence of the worst-case error on the number of variables s s , and show various tractability results under certain conditions on weights of the Korobov space. Tractability means that the dependence on s s is never exponential, and sometimes the dependence on s s is polynomial or there is no dependence on s s at all.
We study multivariate integration for a weighted Korobov space of periodic infinitely many times differentiable functions for which the Fourier coefficients decay exponentially fast. The weights are defined in terms of two non-decreasing sequences a = { a i } \mathbf {a}=\{a_i\} and b = { b i } \mathbf {b}=\{b_i\} of numbers no less than one and a parameter ω ∈ ( 0 , 1 ) \omega \in (0,1) . Let e ( n , s ) e(n,s) be the minimal worst-case error of all algorithms that use n n function values in the s s -variate case. We would like to check conditions on a \mathbf {a} , b \mathbf {b} and ω \omega such that e ( n , s ) e(n,s) decays exponentially fast, i.e., for some q ∈ ( 0 , 1 ) q\in (0,1) and p > 0 p>0 we have
We investigate multivariate integration for a space of infinitely differentiable functions $ \mathcal{F}_{s, \boldsymbol{u}} := \{f \in C^\infty [0,1]^s \mid \| f \|_{\mathcal{F}_{s, \boldsymbol{u}} } := \sup_{(\alpha_1, \dots, \alpha_s) \in \mathbb{N}_0^s} \|f^{(\alpha_1, \dots, \alpha_s)}\|_{L^1}/\prod_{j=1}^s u_j^{\alpha_j} 1$ such that
e(n,s)∈O(q(logn)p) holds as
n goes to infinity, where
e(n,s) may depend on
s in an arbitrary way. We prove that accelerating convergence holds for
p=2 for any
u and
s considered. We also study a sufficient condition for accelerating convergence with strong tractability (AC-ST), which means that there exist constants
C>0,
01 independent of
s such that
e(n,s)≤Cq(logn)p holds for all
s and
n. We prove that AC-ST holds if the weights
u decay sufficiently fast. These results come from the convergence and tractability results for the Walsh space into which
Fs,u is embedded.
We study multivariate
L2-approximation for a weighted Korobov space of analytic periodic functions for which the Fourier coefficients decay exponentially fast. The weights are defined, in particular, in terms of two sequences
a={aj} and
b={bj} of numbers no less than one. Let
eL2−app,Λ(n,s) be the minimal worst-case error of all algorithms that use
n information functionals from the class
Λ in the
s-variate case. We consider two classes
Λ: the class
Λall consists of all linear functionals and the class
Λstd consists of only function valuations. We study (EXP) exponential convergence. This means that
eL2−app,Λ(n,s)≤C(s)q(n/C1(s))p(s)foralln,s∈N where
q∈(0,1), and
C,C1,p:N→(0,∞). If we can take
p(s)=p>0 for all
s then we speak of (UEXP) uniform exponential convergence. We also study EXP and UEXP with (WT) weak, (PT) polynomial and (SPT) strong polynomial tractability. These concepts are defined as follows. Let $n(\e,s)$ be the minimal
n for which $e^{L_2-\mathrm{app},Λ}(n,s)\le \e$. Then WT holds iff $\lim_{s+\log\,\e^{-1}\to\infty}(\log n(\e,s))/(s+\log\,\e^{-1})=0$, PT holds iff there are
c,τ1,τ2 such that $n(\e,s)\le cs^{τ_1}(1+\log\,\e^{-1})^{τ_2}$ for all
s and $\e\in(0,1)$, and finally SPT holds iff the last estimate holds for
τ1=0. The infimum of
τ2 for which SPT holds is called the exponent
τ∗ of SPT. We prove that the results are the same for both classes
Λ, and obtain conditions for WT, PT, SPT with and without EXP and UEXP.
We study multivariate
L∞-approximation for a weighted Korobov space of periodic functions for which the Fourier coefficients decay exponentially fast. The weights are defined, in particular, in terms of two sequences
a={aj} and
b={bj} of positive real numbers bounded away from zero. We study the minimal worst-case error
eL∞−app,Λ(n,s) of all algorithms that use
n information evaluations from a class
Λ in the
s-variate case. We consider two classes
Λ in this paper: the class
Λall of all linear functionals and the class
Λstd of only function evaluations. We study exponential convergence of the minimal worst-case error, which means that
eL∞−app,Λ(n,s) converges to zero exponentially fast with increasing
n. Furthermore, we consider how the error depends on the dimension
s. To this end, we define the notions of
κ-EC-weak, EC-polynomial and EC-strong polynomial tractability, where EC stands for "exponential convergence". In particular, EC-polynomial tractability means that we need a polynomial number of information evaluations in
s and
1+logε−1 to compute an
ε-approximation. We derive necessary and sufficient conditions on the sequences
a and
b for obtaining exponential error convergence, and also for obtaining the various notions of tractability. The results are the same for both classes
Λ.
Abstract. We study quasi-Monte Carlo (QMC) integration over the multidimensional unit cube in several weighted function spaces with different smoothness classes. We consider approximating the integral of a function by the median of several integral estimates under independent and random choices of the underlying QMC point sets (either linearly scrambled digital nets or infinite-precision polynomial lattice point sets). Even though our approach does not require any information on the smoothness and weights of a target function space as an input, we can prove a probabilistic upper bound on the worst-case error for the respective weighted function space, where the failure probability converges to 0 exponentially fast as the number of estimates increases. Our obtained rates of convergence are nearly optimal for function spaces with finite smoothness, and we can attain a dimension-independent super-polynomial convergence for a class of infinitely differentiable functions. This implies that our median-based QMC rule is universal in the sense that it does not need to be adjusted to the smoothness and the weights of the function spaces and yet exhibits the nearly optimal rate of convergence. Numerical experiments support our theoretical results.