Let Xn={xj}j=1n\documentclass[12pt]{minimal}
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\begin{document}$$X_n = \{x^j\}_{j=1}^n$$\end{document} be a set of n points in the d-cube Id:=[0,1]d\documentclass[12pt]{minimal}
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\begin{document}$${\mathbb {I}}^d:=[0,1]^d$$\end{document}, and Φn={φj}j=1n\documentclass[12pt]{minimal}
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\begin{document}$$\Phi _n = \{\varphi _j\}_{j =1}^n$$\end{document} a family of n functions on Id\documentclass[12pt]{minimal}
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\begin{document}$${\mathbb {I}}^d$$\end{document}. We consider the approximate recovery of functions f on Id\documentclass[12pt]{minimal}
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\begin{document}$${{\mathbb {I}}}^d$$\end{document} from the sampled values f(x1),…,f(xn)\documentclass[12pt]{minimal}
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\begin{document}$$f(x^1), \ldots , f(x^n)$$\end{document}, by the linear sampling algorithm Ln(Xn,Φn,f):=∑j=1nf(xj)φj.\documentclass[12pt]{minimal}
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\begin{document}$$ L_n(X_n,\Phi _n,f) := \sum _{j=1}^n f(x^j)\varphi _j. $$\end{document} The error of sampling recovery is measured in the norm of the space Lq(Id)\documentclass[12pt]{minimal}
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\begin{document}$$L_q({\mathbb {I}}^d)$$\end{document}-norm or the energy quasi-norm of the isotropic Sobolev space Wqγ(Id)\documentclass[12pt]{minimal}
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\begin{document}$$W^\gamma _q({\mathbb {I}}^d)$$\end{document} for 1<q<∞\documentclass[12pt]{minimal}
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\begin{document}$$1 < q < \infty $$\end{document} and γ>0\documentclass[12pt]{minimal}
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\begin{document}$$\gamma > 0$$\end{document}. Functions f to be recovered are from the unit ball in Besov-type spaces of an anisotropic smoothness, in particular, spaces Bp,θα,β\documentclass[12pt]{minimal}
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\begin{document}$$B^{\alpha ,\beta }_{p,\theta }$$\end{document} of a “hybrid” of mixed smoothness α>0\documentclass[12pt]{minimal}
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\begin{document}$$\alpha > 0$$\end{document} and isotropic smoothness β∈R\documentclass[12pt]{minimal}
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\begin{document}$$\beta \in {\mathbb {R}}$$\end{document}, and spaces Bp,θa\documentclass[12pt]{minimal}
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\begin{document}$$B^a_{p,\theta }$$\end{document} of a nonuniform mixed smoothness a∈R+d\documentclass[12pt]{minimal}
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\begin{document}$$a \in {\mathbb {R}}^d_+$$\end{document}. We constructed asymptotically optimal linear sampling algorithms Ln(Xn∗,Φn∗,·)\documentclass[12pt]{minimal}
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\begin{document}$$L_n(X_n^*,\Phi _n^*,\cdot )$$\end{document} on special sparse grids Xn∗\documentclass[12pt]{minimal}
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\begin{document}$$X_n^*$$\end{document} and a family Φn∗\documentclass[12pt]{minimal}
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\begin{document}$$\Phi _n^*$$\end{document} of linear combinations of integer or half integer translated dilations of tensor products of B-splines. We computed the asymptotic order of the error of the optimal recovery. This construction is based on B-spline quasi-interpolation representations of functions in Bp,θα,β\documentclass[12pt]{minimal}
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\begin{document}$$B^{\alpha ,\beta }_{p,\theta }$$\end{document} and Bp,θa\documentclass[12pt]{minimal}
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\begin{document}$$B^a_{p,\theta }$$\end{document}. As consequences, we obtained the asymptotic order of optimal cubature formulas for numerical integration of functions from the unit ball of these Besov-type spaces.
