Sparse-grid sampling recovery and numerical integration of functions having mixed smoothness
From MaRDI portal
Publication:6650705
DOI10.1007/s40306-024-00527-7MaRDI QIDQ6650705
Publication date: 9 December 2024
Published in: Acta Mathematica Vietnamica (Search for Journal in Brave)
sparse gridsasymptotic orderquadratureSobolev spaces of mixed smoothnesssampling recoverynumerical weighted integrationsampling widths
Rate of convergence, degree of approximation (41A25) Approximate quadratures (41A55) Numerical quadrature and cubature formulas (65D32) Approximation by arbitrary nonlinear expressions; widths and entropy (41A46) Numerical integration (65D30)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Sampling on energy-norm based sparse grids for the optimal recovery of Sobolev type functions in \(H^\gamma\)
- Sampling and cubature on sparse grids based on a B-spline quasi-interpolation
- B-spline quasi-interpolant representations and sampling recovery of functions with mixed smoothness
- Linear vs. nonlinear algorithms for linear problems
- Function spaces in Lipschitz domains and optimal rates of convergence for sampling
- Bases in function spaces, sampling, discrepancy, numerical integration
- Non-linear sampling recovery based on quasi-interpolant wavelet representations
- Multiplicative estimates for integral norms of differentiable functions of several variables
- Gaussian rules on unbounded intervals
- Optimal sampling recovery of mixed order Sobolev embeddings via discrete {L}ittlewood--{P}aley type characterizations
- B-spline quasi-interpolation sampling representation and sampling recovery in Sobolev spaces of mixed smoothness
- Optimal order quadrature error bounds for infinite-dimensional higher-order digital sequences
- Markov-Sonin Gaussian rule for singular functions
- Hyperbolic cross approximation. Lecture notes given at the courses on constructive approximation and harmonic analysis, Barcelona, Spain, May 30 -- June 3, 2016
- Deep UQ: learning deep neural network surrogate models for high dimensional uncertainty quantification
- High-dimensional integration on \(\mathbb{R}^d\), weighted Hermite spaces, and orthogonal transforms
- Integration in Hermite spaces of analytic functions
- On approximate recovery of functions with bounded mixed derivative
- Pointwise multipliers for Sobolev and Besov spaces of dominating mixed smoothness
- A sharp upper bound for sampling numbers in \(L_2\)
- Constructive sparse trigonometric approximation and other problems for functions with mixed smoothness
- Explicit Constructions of Quasi-Monte Carlo Rules for the Numerical Integration of High-Dimensional Periodic Functions
- Walsh Spaces Containing Smooth Functions and Quasi–Monte Carlo Rules of Arbitrary High Order
- Multivariate Approximation
- On the Optimal Order of Integration in Hermite Spaces with Finite Smoothness
- Weighted Polynomial Approximation and Numerical Methods for Integral Equations
- Lower bounds for the integration error for multivariate functions with mixed smoothness and optimal Fibonacci cubature for functions on the square
- Sparse grids
- High-dimensional integration: The quasi-Monte Carlo way
- Sampling numbers of smoothness classes via \(\ell^1\)-minimization
- Random points are good for universal discretization
- Numerical weighted integration of functions having mixed smoothness
- Optimal numerical integration and approximation of functions on \(\mathbb{R}^d\) equipped with Gaussian measure
This page was built for publication: Sparse-grid sampling recovery and numerical integration of functions having mixed smoothness