TESTPACK

TESTPACK is a MATLAB program which demonstrates the testing of a routine for multidimensional integration. In this case, the code being tested is a subroutine known as ADAPT, written by Genz. The code is tested on six test integrand functions, also defined by Genz. The test is done with a variety of spatial dimensions, parameter values, and difficulty factors.

Keywords for this software

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References in zbMATH (referenced in 84 articles )

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  1. van den Bos, L. M. M.; Sanderse, B.: A geometrical interpretation of the addition of nodes to an interpolatory quadrature rule while preserving positive weights (2021)
  2. van den Bos, Laurent; Sanderse, Benjamin; Bierbooms, Wim; van Bussel, Gerard: Generating nested quadrature rules with positive weights based on arbitrary sample sets (2020)
  3. van den Bos, L. M. M.; Sanderse, B.; Bierbooms, W. A. A. M.: Adaptive sampling-based quadrature rules for efficient Bayesian prediction (2020)
  4. Aiton, Kevin W.; Driscoll, Tobin A.: An adaptive partition of unity method for multivariate Chebyshev polynomial approximations (2019)
  5. Gorodetsky, Alex; Karaman, Sertac; Marzouk, Youssef: A continuous analogue of the tensor-train decomposition (2019)
  6. Halder, Yous V.; Sanderse, Benjamin; Koren, Barry: An adaptive minimum spanning tree multielement method for uncertainty quantification of smooth and discontinuous responses (2019)
  7. Hinrichs, Aicke; Kritzer, Peter; Pillichshammer, Friedrich; Wasilkowski, G. W.: Truncation dimension for linear problems on multivariate function spaces (2019)
  8. Sayah, Toni: Adaptive stratified Monte Carlo algorithm for numerical computation of integrals (2019)
  9. Wu, Kailiang; Xiu, Dongbin: Sequential approximation of functions in Sobolev spaces using random samples (2019)
  10. Adcock, Ben; Bao, Anyi; Jakeman, John D.; Narayan, Akil: Compressed sensing with sparse corruptions: fault-tolerant sparse collocation approximations (2018)
  11. Brauchart, Johann S.: Explicit families of functions on the sphere with exactly known Sobolev space smoothness (2018)
  12. Heitzinger, Clemens; Pammer, Gudmund; Rigger, Stefan: Cubature formulas for multisymmetric functions and applications to stochastic partial differential equations (2018)
  13. Jakeman, John D.; Narayan, Akil: Generation and application of multivariate polynomial quadrature rules (2018)
  14. Mak, Simon; Joseph, V. Roshan: Support points (2018)
  15. Shin, Yeonjong; Wu, Kailiang; Xiu, Dongbin: Sequential function approximation with noisy data (2018)
  16. Amaral, Sergio; Allaire, Douglas; Willcox, Karen: Optimal (L_2)-norm empirical importance weights for the change of probability measure (2017)
  17. Butler, T.; Graham, L.; Mattis, S.; Walsh, S.: A measure-theoretic interpretation of sample based numerical integration with applications to inverse and prediction problems under uncertainty (2017)
  18. Liao, Qinzhuo; Zhang, Dongxiao; Tchelepi, Hamdi: A two-stage adaptive stochastic collocation method on nested sparse grids for multiphase flow in randomly heterogeneous porous media (2017)
  19. Salehi, Saeed; Raisee, Mehrdad; Cervantes, Michel J.; Nourbakhsh, Ahmad: Efficient uncertainty quantification of stochastic CFD problems using sparse polynomial chaos and compressed sensing (2017)
  20. Shin, Yeonjong; Xiu, Dongbin: A randomized algorithm for multivariate function approximation (2017)

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