AbstractIt has been an open problem to derive a necessary and sufficient condition for a linear tensor product problem S={Sd} in the average case setting to be weakly tractable but not polynomially tractable. As a result of the tensor product structure, the eigenvalues of the covariance operator of the induced measure in the one-dimensional problem characterize the complexity of approximating Sd, d≥1, with accuracy ε. If ∑j=1∞λj<1 and λ2>0, we know that S is not polynomially tractable iff lim supj→∞λjjp=∞ for all p>1. Thus we settle the open problem by showing that S is weakly tractable iff ∑j>nλj=o(ln−2n). In particular, assume that ℓ=limj→∞λjjln3(j+1), exists. Then S is weakly tractable iff ℓ=0
AbstractWe study the ε-approximation of linear multivariate problems defined over weighted tensor pr...
Tractability of multivariate problems has become nowadays a popular re- search subject. Polynomial t...
AbstractWe study approximation of functions that may depend on infinitely many variables. We assume ...
AbstractIt has been an open problem to derive a necessary and sufficient condition for a linear tens...
AbstractIt has been an open problem to derive a necessary and sufficient condition for a linear tens...
AbstractIt has been an open problem to derive a necessary and sufficient condition for a linear tens...
AbstractWe prove that some multivariate linear tensor product problems are tractable in the worst ca...
We prove that some multivariate linear tensor product problems are tractable in the worst case setti...
AbstractThis paper deals with the worst case setting for approximating multivariate tensor product l...
AbstractMany papers study polynomial tractability for multivariate problems. Let n(ɛ,d) be the minim...
AbstractWe study d-variate approximation problems in the average case setting with respect to a zero...
AbstractWe prove that some multivariate linear tensor product problems are tractable in the worst ca...
AbstractWe study d-variate approximation problems in the average case setting with respect to a zero...
AbstractMany papers study polynomial tractability for multivariate problems. Let n(ɛ,d) be the minim...
AbstractTractability of multivariate problems has become a popular research subject. Polynomial trac...
AbstractWe study the ε-approximation of linear multivariate problems defined over weighted tensor pr...
Tractability of multivariate problems has become nowadays a popular re- search subject. Polynomial t...
AbstractWe study approximation of functions that may depend on infinitely many variables. We assume ...
AbstractIt has been an open problem to derive a necessary and sufficient condition for a linear tens...
AbstractIt has been an open problem to derive a necessary and sufficient condition for a linear tens...
AbstractIt has been an open problem to derive a necessary and sufficient condition for a linear tens...
AbstractWe prove that some multivariate linear tensor product problems are tractable in the worst ca...
We prove that some multivariate linear tensor product problems are tractable in the worst case setti...
AbstractThis paper deals with the worst case setting for approximating multivariate tensor product l...
AbstractMany papers study polynomial tractability for multivariate problems. Let n(ɛ,d) be the minim...
AbstractWe study d-variate approximation problems in the average case setting with respect to a zero...
AbstractWe prove that some multivariate linear tensor product problems are tractable in the worst ca...
AbstractWe study d-variate approximation problems in the average case setting with respect to a zero...
AbstractMany papers study polynomial tractability for multivariate problems. Let n(ɛ,d) be the minim...
AbstractTractability of multivariate problems has become a popular research subject. Polynomial trac...
AbstractWe study the ε-approximation of linear multivariate problems defined over weighted tensor pr...
Tractability of multivariate problems has become nowadays a popular re- search subject. Polynomial t...
AbstractWe study approximation of functions that may depend on infinitely many variables. We assume ...