31 research outputs found
Strong deviations from randomness in m-sequences based on trinomials
The fixed vector of any m-sequence based on a trinomial is explicitly obtained. Local nonrandomness around the fixed vector is analyzed through model-construction and experiments. We conclude that the initial vector near the fixed vector should be avoided
Hurst's Rescaled Range Statistical Analysis for Pseudorandom Number Generators used in Physical Simulations
The rescaled range statistical analysis (R/S) is proposed as a new method to
detect correlations in pseudorandom number generators used in Monte Carlo
simulations. In an extensive test it is demonstrated that the RS analysis
provides a very sensitive method to reveal hidden long run and short run
correlations. Several widely used and also some recently proposed pseudorandom
number generators are subjected to this test. In many generators correlations
are detected and quantified.Comment: 12 pages, 12 figures, 6 tables. Replaces previous version to correct
citation [19
Improved long-period generators based on linear recurrences modulo 2
Fast uniform random number generators with extremely long periods have been defined and implemented based on linear recurrences modulo 2. The twisted GFSR and the Mersenne twister are famous recent examples. Besides the period length, the statistical quality of these generators is usually assessed via their equidistribution properties. The huge-period generators proposed so far are not quite optimal in this respect. In this article, we propose new generators of that form with better equidistribution and "bit-mixing" properties for equivalent period length and speed. The state of our new generators evolves in a more chaotic way than for the Mersenne twister. We illustrate how this can reduce the impact of persistent dependencies among successive output values, which can be observed in certain parts of the period of gigantic generators such as the Mersenne twister
Periodic orbits of the ensemble of Sinai-Arnold cat maps and pseudorandom number generation
We propose methods for constructing high-quality pseudorandom number
generators (RNGs) based on an ensemble of hyperbolic automorphisms of the unit
two-dimensional torus (Sinai-Arnold map or cat map) while keeping a part of the
information hidden. The single cat map provides the random properties expected
from a good RNG and is hence an appropriate building block for an RNG, although
unnecessary correlations are always present in practice. We show that
introducing hidden variables and introducing rotation in the RNG output,
accompanied with the proper initialization, dramatically suppress these
correlations. We analyze the mechanisms of the single-cat-map correlations
analytically and show how to diminish them. We generalize the Percival-Vivaldi
theory in the case of the ensemble of maps, find the period of the proposed RNG
analytically, and also analyze its properties. We present efficient practical
realizations for the RNGs and check our predictions numerically. We also test
our RNGs using the known stringent batteries of statistical tests and find that
the statistical properties of our best generators are not worse than those of
other best modern generators.Comment: 18 pages, 3 figures, 9 table