To recover or approximate smooth multivariate functions, sparse grids are superior to full grids due to a significant reduction of the required support nodes. The order of the convergence rate in the maximum norm is preserved up to a logarithmic factor. We describe three possible piecewise multilinear hierarchical interpolation schemes in detail and conduct a numerical comparison. Furthermore, we document the features of our sparse grid interpolation software package spinterp for MATLAB. (Source: http://dl.acm.org/)

This software is also peer reviewed by journal TOMS.

References in zbMATH (referenced in 29 articles , 1 standard article )

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  1. Khan, Arbaz; Powell, Catherine E.; Silvester, David J.: Robust preconditioning for stochastic Galerkin formulations of parameter-dependent nearly incompressible elasticity equations (2019)
  2. Elman, Howard C.; Silvester, David J.: Collocation methods for exploring perturbations in linear stability analysis (2018)
  3. Karvonen, Toni; Särkkä, Simo: Fully symmetric kernel quadrature (2018)
  4. Chen, Peng; Quarteroni, Alfio; Rozza, Gianluigi: Reduced basis methods for uncertainty quantification (2017)
  5. Hou, Thomas Y.; Li, Qin; Zhang, Pengchuan: Exploring the locally low dimensional structure in solving random elliptic PDEs (2017)
  6. Nagy, Stanislav; Gijbels, Irène: Law of large numbers for discretely observed random functions (2017)
  7. Sun, Xianming; Vanmaele, Michèle: Uncertainty quantification of derivative instruments (2017)
  8. Zhang, Cheng; Shahbaba, Babak; Zhao, Hongkai: Precomputing strategy for Hamiltonian Monte Carlo method based on regularity in parameter space (2017)
  9. Elman, Howard C.; Forstall, Virginia: Preconditioning techniques for reduced basis methods for parameterized elliptic partial differential equations (2015)
  10. Nance, J.; Kelley, C. T.: A sparse interpolation algorithm for dynamical simulations in computational chemistry (2015)
  11. Schillings, C.; Schulz, V.: On the influence of robustness measures on shape optimization with stochastic uncertainties (2015)
  12. Torres Valderrama, Aldemar; Witteveen, Jeroen; Navarro, Maria; Blom, Joke: Uncertainty propagation in nerve impulses through the action potential mechanism (2015)
  13. Dinh, Vu; Rundell, Ann E.; Buzzard, Gregery T.: Experimental design for dynamics identification of cellular processes (2014)
  14. Griebel, Michael; Hamaekers, Jan: Fast discrete Fourier transform on generalized sparse grids (2014)
  15. Gunzburger, Max D.; Webster, Clayton G.; Zhang, Guannan: Stochastic finite element methods for partial differential equations with random input data (2014)
  16. Conrad, Patrick R.; Marzouk, Youssef M.: Adaptive Smolyak pseudospectral approximations (2013)
  17. Bazil, Jason N.; Buzzard, Gregory T.; Rundell, Ann E.: A global parallel model based design of experiments method to minimize model output uncertainty (2012)
  18. Elman, Howard C.; Miller, Christopher W.: Stochastic collocation with kernel density estimation (2012)
  19. Agarwal, Nitin; Aluru, N. R.: Weighted Smolyak algorithm for solution of stochastic differential equations on non-uniform probability measures (2011)
  20. Borzì, A.; von Winckel, G.: A POD framework to determine robust controls in PDE optimization (2011)

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