TOMS659

Algorithm 659: Implementing Sobol’s quasirandom sequence generator: TOMS659 is a FORTRAN77 library which computes elements of the Sobol quasirandom sequence. A quasirandom or low discrepancy sequence, such as the Faure, Halton, Hammersley, Niederreiter or Sobol sequences, is ”less random” than a pseudorandom number sequence, but more useful for such tasks as approximation of integrals in higher dimensions, and in global optimization. This is because low discrepancy sequences tend to sample space ”more uniformly” than random numbers. Algorithms that use such sequences may have superior convergence. The original, true, correct version of ACM TOMS Algorithm 659 is available through ACM: http://www.acm.org/pubs/calgo or NETLIB: http://www.netlib.org/toms/index.html.


References in zbMATH (referenced in 133 articles , 2 standard articles )

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  1. Arnald Puy, Samuele Lo Piano, Andrea Saltelli, Simon A. Levin: sensobol: an R package to compute variance-based sensitivity indices (2021) arXiv
  2. Atanassov, Emanouil; Ivanovska, Sofiya; Karaivanova, Aneta: Optimization of the direction numbers of the Sobol sequences (2021)
  3. Leövey, H.; Römisch, W.: Randomized QMC methods for mixed-integer two-stage stochastic programs with application to electricity optimization (2020)
  4. Mukherjee, Arpan; Rai, Rahul; Singla, Puneet; Singh, Tarunraj; Patra, Abani: Overlapping clustering based technique for scalable uncertainty quantification in physical systems (2020)
  5. Polala, Arun Kumar; Ökten, Giray: Implementing de-biased estimators using mixed sequences (2020)
  6. Teichert, G. H.; Natarajan, A. R.; Van der Ven, A.; Garikipati, K.: Scale bridging materials physics: active learning workflows and integrable deep neural networks for free energy function representations in alloys (2020)
  7. van Appel, Jacques; McWalter, Thomas A.: Moment approximations of displaced forward-LIBOR rates with application to swaptions (2020)
  8. Bayousef, Manal; Mascagni, Michael: A computational investigation of the optimal Halton sequence in QMC applications (2019)
  9. Bian, Qi; Nener, Brett; Wang, Xinmin: An improved NSGA-II based control allocation optimisation for aircraft longitudinal automatic landing system (2019)
  10. ChangYong Oh, Efstratios Gavves, Max Welling: BOCK : Bayesian Optimization with Cylindrical Kernels (2019) arXiv
  11. Dimov, I. T.; Maire, S.: A new unbiased stochastic algorithm for solving linear Fredholm equations of the second kind (2019)
  12. Harase, Shin: Comparison of Sobol’ sequences in financial applications (2019)
  13. Teichert, Gregory H.; Garikipati, Krishna: Machine learning materials physics: surrogate optimization and multi-fidelity algorithms predict precipitate morphology in an alternative to phase field dynamics (2019)
  14. Weiß, Christian; Nikolić, Zoran: An aspect of optimal regression design for LSMC (2019)
  15. Boom, Pieter D.; Zingg, David W.: Optimization of high-order diagonally-implicit Runge-Kutta methods (2018)
  16. Heitzinger, Clemens; Leumüller, Michael; Pammer, Gudmund; Rigger, Stefan: Existence, uniqueness, and a comparison of nonintrusive methods for the stochastic nonlinear Poisson-Boltzmann equation (2018)
  17. Heitzinger, Clemens; Pammer, Gudmund; Rigger, Stefan: Cubature formulas for multisymmetric functions and applications to stochastic partial differential equations (2018)
  18. Henderson, Nélio; de Sá Rêgo, Marroni; Imbiriba, Janaína; de Sá Rêgo, Márlison; Sacco, Wagner F.: Testing the topographical global initialization strategy in the framework of an unconstrained optimization method (2018)
  19. Liuzzi, G.; Truemper, K.: Parallelized hybrid optimization methods for nonsmooth problems using NOMAD and linesearch (2018)
  20. Mak, Simon; Joseph, V. Roshan: Support points (2018)

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