Sampling and cubature on sparse grids based on a B-spline quasi-interpolation

Abstract

Let $X_n = \{x^j\}_{j=1}^n$ be a set of $n$ points in the $d$-cube $[0,1]^d$, and $\Phi_n = \{\varphi_j\}_{j =1}^n$ a family of $n$ functions on $[0,1]^d$. We consider the approximate recovery functions $f$ on $[0,1]^d$ from the sampled values $f(x^1), ..., f(x^n)$, by the linear sampling algorithm \begin{equation} \nonumber L_n(X_n,\Phi_n,f) \ := \ \sum_{j=1}^n f(x^j)\varphi_j. \end{equation} The error of sampling recovery is measured in the norm of the space $L_q([0,1]^d)$-norm or the energy norm of the isotropic Sobolev sapce $W^\gamma_q([0,1]^d)$ for $0 < q \le \infty$ and $\gamma > 0$. Functions $f$ to be recovered are from the unit ball in Besov type spaces of an anisotropic smoothness, in particular, spaces $B^a_{p,\theta}$ of a nonuniform mixed smoothness $a \in {\mathbb R}^d_+$, and spaces $B^{\alpha,\beta}_{p,\theta}$ of a "hybrid" of mixed smoothness $\alpha > 0$ and isotropic smoothness $\beta \in \mathbb R$. We constructed optimal linear sampling algorithms $L_n(X_n^*,\Phi_n^*,\cdot)$ on special sparse grids $X_n^*$ and a family $\Phi_n^*$ of linear combinations of integer or half integer translated dilations of tensor products of B-splines. We computed the asymptotic of the error of the optimal recovery. This construction is based on a B-spline quasi-interpolation representations of functions in $B^a_{p,\theta}$ and $B^{\alpha,\beta}_{p,\theta}$. As consequences we obtained the asymptotic of optimal cubature formulas for numerical integration of functions from the unit ball of these Besov type spaces.