Good parameters and implementations for combined multiple recursive random number generators. Combining parallel multiple recursive sequences provides an efficient way of implementing random number generators with long periods and good structural properties. Such generators are statistically more robust than simple linear congruential generators that fit into a computer word. We made extensive computer searches for good parameter sets, with respect to the spectral test, for combined multiple recursive generators of different sizes. We also compare different implementations and give a specific code in C that is faster than previous implementations of similar generators.

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

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  1. Almaraz Luengo, Elena: A brief and understandable guide to pseudo-random number generators and specific models for security (2022)
  2. Puchhammer, Florian; Ben Abdellah, Amal; L’Ecuyer, Pierre: Variance reduction with array-RQMC for tau-leaping simulation of stochastic biological and chemical reaction networks (2021)
  3. Cooper, Kyle; Hunter, Susan R.: PyMOSO: software for multiobjective simulation optimization with R-PERLE and R-MinRLE (2020)
  4. Kolonko, Michael; Gu, Feng; Wu, Zijun: Improving the statistical quality of random number generators by applying a simple ratio transformation (2019)
  5. Savvidy, George; Savvidy, Konstantin: Exponential decay of correlations functions in MIXMAX generator of pseudorandom numbers (2018)
  6. L’Ecuyer, Pierre; Munger, David; Oreshkin, Boris; Simard, Richard: Random numbers for parallel computers: requirements and methods, with emphasis on gpus (2017)
  7. Savvidy, Konstantin; Savvidy, George: Spectrum and entropy of C-systems MIXMAX random number generator (2016)
  8. Self, Julian; Mackey, Michael C.: Random numbers from a delay equation (2016)
  9. Karl, Andrew T.; Eubank, Randy; Milovanovic, Jelena; Reiser, Mark; Young, Dennis: Using rngstreams for parallel random number generation in C++ and R (2014)
  10. L’Ecuyer, Pierre; Simard, Richard: On the lattice structure of a special class of multiple recursive random number generators (2014)
  11. Sezgin, Fatin; Sezgin, Tevfik Metin: Finding the best portable congruential random number generators (2013)
  12. Deng, Lih-Yuan; Shiau, Jyh-Jen Horng; Lu, Henry Horng-Shing: Efficient computer search of large-order multiple recursive pseudo-random number generators (2012)
  13. L’Ecuyer, Pierre; Munger, David: On figures of merit for randomly-shifted lattice rules (2012)
  14. Meterelliyoz, Melike; Alexopoulos, Christos; Goldsman, David: Folded overlapping variance estimators for simulation (2012)
  15. El Haddad, R.; Lécot, C.; L’Ecuyer, P.; Nassif, N.: Quasi-Monte Carlo methods for Markov chains with continuous multi-dimensional state space (2010)
  16. El-Ocla, Hosam: TCP CERL: Congestion control enhancement over wireless networks (2010) ioport
  17. Plesser, Hans Ekkehard; Jahnsen, Anders Grønvik: Re-seeding invalidates tests of random number generators (2010)
  18. L’Ecuyer, Pierre: Quasi-Monte Carlo methods with applications in finance (2009)
  19. Bouras, Ch.; Stamos, K.: Performance analysis of adaptive admission control algorithms for bandwidth brokers (2007) ioport
  20. Kimms, Alf; Müller-Bungart, Michael: Simulation of stochastic demand data streams for network revenue management problems (2007)

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