AbstractWe study the L∞-approximation problem for weighted Banach spaces of smooth d-variate functions, where d can be arbitrarily large. We consider the worst case error for algorithms that use finitely many pieces of information from different classes. Adaptive algorithms are also allowed. For a scale of Banach spaces we prove necessary and sufficient conditions for tractability in the case of product weights. Furthermore, we show the equivalence of weak tractability with the fact that the problem does not suffer from the curse of dimensionality
This dissertation studies the problem of approximating functions of d variables in a separable Banac...
We prove that some multivariate linear tensor product problems are tractable in the worst case setti...
We study d-variate approximation for a weighted unanchored Sobolev space having smoothness m ≥ 1. Fo...
AbstractWe study the L∞-approximation problem for weighted Banach spaces of smooth d-variate functio...
AbstractWe consider approximation of ∞-variate functions with the error measured in a weighted L2-no...
We consider approximation problems for a special space of d variate functions. We show that the prob...
... this paper is to extend known tractability results to weighted spaces of functions with the deri...
Abstract. We study approximating multivariate functions from a reproducing ker-nel Hilbert space wit...
AbstractWe study approximation of functions that may depend on infinitely many variables. We assume ...
AbstractWe study the worst case setting for approximation of d variate functions from a general repr...
AbstractWe study multivariate approximation with the error measured in L∞ and weighted L2 norms. We ...
AbstractWe study the minimal number n(ɛ,d) of information evaluations needed to compute a worst case...
AbstractWe consider approximation of weighted integrals of functions with infinitely many variables ...
Using Smolyak's construction [5], we derive a new algorithm for approximating multivariate func...
AbstractWe prove that some multivariate linear tensor product problems are tractable in the worst ca...
This dissertation studies the problem of approximating functions of d variables in a separable Banac...
We prove that some multivariate linear tensor product problems are tractable in the worst case setti...
We study d-variate approximation for a weighted unanchored Sobolev space having smoothness m ≥ 1. Fo...
AbstractWe study the L∞-approximation problem for weighted Banach spaces of smooth d-variate functio...
AbstractWe consider approximation of ∞-variate functions with the error measured in a weighted L2-no...
We consider approximation problems for a special space of d variate functions. We show that the prob...
... this paper is to extend known tractability results to weighted spaces of functions with the deri...
Abstract. We study approximating multivariate functions from a reproducing ker-nel Hilbert space wit...
AbstractWe study approximation of functions that may depend on infinitely many variables. We assume ...
AbstractWe study the worst case setting for approximation of d variate functions from a general repr...
AbstractWe study multivariate approximation with the error measured in L∞ and weighted L2 norms. We ...
AbstractWe study the minimal number n(ɛ,d) of information evaluations needed to compute a worst case...
AbstractWe consider approximation of weighted integrals of functions with infinitely many variables ...
Using Smolyak's construction [5], we derive a new algorithm for approximating multivariate func...
AbstractWe prove that some multivariate linear tensor product problems are tractable in the worst ca...
This dissertation studies the problem of approximating functions of d variables in a separable Banac...
We prove that some multivariate linear tensor product problems are tractable in the worst case setti...
We study d-variate approximation for a weighted unanchored Sobolev space having smoothness m ≥ 1. Fo...