AbstractWe study the worst case complexity of weighted approximation and integration for functions defined over Rd. We assume that the functions have all partial derivatives of order up to r uniformly bounded in a weighted Lp-norm for a given weight function ψ. The integration and the error for approximation are defined in a weighted sense for another given weight ϱ. We present a necessary and sufficient condition on weight functions ϱ and ψ for the complexity of the problem to be finite. Under additional conditions, we show that the complexity of the weighted problem is proportional to the complexity of the corresponding classical problem defined over a unit cube and with ϱ=ψ=1. Similar results have been obtained recently for scalar functi...
AbstractWe study the integration and approximation problems for monotone or convex bounded functions...
AbstractWe study multivariate integration in the worst case setting for weighted Korobov spaces of s...
AbstractWe study approximation of functions that may depend on infinitely many variables. We assume ...
AbstractWe study the worst case complexity of weighted approximation and integration for functions d...
AbstractWe study approximation of univariate functions defined over the reals. We assume that the rt...
AbstractWe study approximation of multivariate functions defined over Rd. We assume that all rth ord...
AbstractWe study weighted approximation and integration of Gaussian stochastic processes X defined o...
Using Smolyak's construction [5], we derive a new algorithm for approximating multivariate func...
AbstractWe consider a new averaging technique for studying the complexity of weighted multivariate i...
AbstractWe study the average case complexity of multivariate integration and L2 function approximati...
AbstractWe study the average case complexity of multivariate integration and L2 function approximati...
AbstractWe consider approximation of weighted integrals of functions with infinitely many variables ...
AbstractWe consider approximation of ∞-variate functions with the error measured in a weighted L2-no...
AbstractWe study weighted approximation and integration of Gaussian stochastic processes X defined o...
Given a probability measure ν and a positive integer n. How to choose n knots and n weights such tha...
AbstractWe study the integration and approximation problems for monotone or convex bounded functions...
AbstractWe study multivariate integration in the worst case setting for weighted Korobov spaces of s...
AbstractWe study approximation of functions that may depend on infinitely many variables. We assume ...
AbstractWe study the worst case complexity of weighted approximation and integration for functions d...
AbstractWe study approximation of univariate functions defined over the reals. We assume that the rt...
AbstractWe study approximation of multivariate functions defined over Rd. We assume that all rth ord...
AbstractWe study weighted approximation and integration of Gaussian stochastic processes X defined o...
Using Smolyak's construction [5], we derive a new algorithm for approximating multivariate func...
AbstractWe consider a new averaging technique for studying the complexity of weighted multivariate i...
AbstractWe study the average case complexity of multivariate integration and L2 function approximati...
AbstractWe study the average case complexity of multivariate integration and L2 function approximati...
AbstractWe consider approximation of weighted integrals of functions with infinitely many variables ...
AbstractWe consider approximation of ∞-variate functions with the error measured in a weighted L2-no...
AbstractWe study weighted approximation and integration of Gaussian stochastic processes X defined o...
Given a probability measure ν and a positive integer n. How to choose n knots and n weights such tha...
AbstractWe study the integration and approximation problems for monotone or convex bounded functions...
AbstractWe study multivariate integration in the worst case setting for weighted Korobov spaces of s...
AbstractWe study approximation of functions that may depend on infinitely many variables. We assume ...