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We investigate multivariate integration for a space of infinitely times differentiable functions $mathcal{F}_{s, boldsymbol{u}} := {f in C^infty [0,1]^s mid | f |_{mathcal{F}_{s, boldsymbol{u}}} < infty }$, where $| f |_{mathcal{F}_{s, boldsymbol{u}}} := sup_{boldsymbol{alpha} = (alpha_1, dots, alpha_s) in mathbb{N}_0^s} |f^{(boldsymbol{alpha})}|_{L^1}/prod_{j=1}^s u_j^{alpha_j}$, $f^{(boldsymbol{alpha})} := frac{partial^{|boldsymbol{alpha}|}}{partial x_1^{alpha_1} cdots partial x_s^{alpha_s}}f$ and $boldsymbol{u} = {u_j}_{j geq 1}$ is a sequence of positive decreasing weights. Let $e(n,s)$ be the minimal worst-case error of all algorithms that use $n$ function values in the $s$-variate case. We prove that for any $boldsymbol{u}$ and $s$ considered $e(n,s) leq C(s) exp(-c(s)(log{n})^2)$ holds for all $n$, where $C(s)$ and $c(s)$ are constants which may depend on $s$. Further we show that if the weights $boldsymbol{u}$ decay sufficiently fast then there exist some $1 < p < 2$ and absolute constants $C$ and $c$ such that $e(n,s) leq C exp(-c(log{n})^p)$ holds for all $s$ and $n$. These bounds are attained by quasi-Monte Carlo integration using digital nets. These convergence and tractability results come from those for the Walsh space into which $mathcal{F}_{s, boldsymbol{u}}$ is embedded.
This article studies the problem of approximating functions belonging to a Hilbert space $H_d$ with an isotropic or anisotropic Gaussian reproducing kernel, $$ K_d(bx,bt) = expleft(-sum_{ell=1}^dgamma_ell^2(x_ell-t_ell)^2right) mbox{for all} bx,b
We consider approximation problems for a special space of d variate functions. We show that the problems have small number of active variables, as it has been postulated in the past using concentration of measure arguments. We also show that, dependi
In this paper, we study how to quickly compute the <-minimal monomial interpolating basis for a multivariate polynomial interpolation problem. We address the notion of reverse reduced basis of linearly independent polynomials and design an algorithm
We present an optimized algorithm calculating determinant for multivariate polynomial matrix on GPU. The novel algorithm provides precise determinant for input multivariate polynomial matrix in controllable time. Our approach is based on modular meth
For $m,n in mathbb{N}$, $mgeq 1$ and a given function $f : mathbb{R}^mlongrightarrow mathbb{R}$ the polynomial interpolation problem (PIP) is to determine a emph{generic node set} $P subseteq mathbb{R}^m$ and the coefficients of the uniquely defined