Embeddings of Weighted Hilbert Spaces and Applications to Multivariate and Infinite-Dimensional Integration
arXiv:1608.00906 · doi:10.1016/j.jat.2017.05.003
Abstract
We study embeddings and norm estimates for tensor products of weighted reproducing kernel Hilbert spaces. These results lead to a transfer principle that is directly applicable to tractability studies of multivariate problems as integration and approximation, and to their infinite-dimensional counterparts. In an application we consider weighted tensor product Sobolev spaces of mixed smoothness of any integer order, equipped with the classical, the anchored, or the ANOVA norm. Here we derive new results for multivariate and infinite-dimensional integration.
References in corpus (1)
Cited by in corpus (7)
- Approximation of high-dimensional periodic functions with Fourier-based methods
- Embeddings for Infinite-Dimensional Integration and -Approximation with Increasing Smoothness
- MDFEM: Multivariate decomposition finite element method for elliptic PDEs with lognormal diffusion coefficients using higher-order QMC and FEM
- Explicit error bounds for randomized Smolyak algorithms and an application to infinite-dimensional integration
- Infinite-dimensional integration and -approximation on Hermite spaces
- MDFEM: Multivariate decomposition finite element method for elliptic PDEs with uniform random diffusion coefficients using higher-order QMC and FEM
- Infinite-Variate -Approximation with Nested Subspace Sampling