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The second row is the same generator with a seed of 3, which produces a cycle of length 2. Using a = 4 and c = 1 (bottom row) gives a cycle length of 9 with any seed in [0, 8]. A linear congruential generator (LCG) is an algorithm that yields a sequence of pseudo-randomized numbers calculated with a discontinuous piecewise linear equation.
Linear congruential generator (LCG) 1958 W. E. Thomson; A. Rotenberg [3] [4] A generalisation of the Lehmer generator and historically the most influential and studied generator. Lagged Fibonacci generator (LFG) 1958 G. J. Mitchell and D. P. Moore [5] Linear-feedback shift register (LFSR) 1965 R. C. Tausworthe [6] A hugely influential design.
A PCG differs from a classical linear congruential generator (LCG) in three ways: the LCG modulus and state is larger, usually twice the size of the desired output, it uses a power-of-2 modulus, which results in a particularly efficient implementation with a full period generator and unbiased output bits, and
ACORN generator proposed recently […] is in fact equivalent to a MLCG with matrix A such that a~ = 1 for i 2 j, aq = 0 otherwise" [10] but the analysis is not taken further. ACORN is not the same as ACG (Additive Congruential Generator) and should not be confused with it - ACG appears to have been used for a variant of the LCG ( Linear ...
The Lehmer random number generator [1] (named after D. H. Lehmer), sometimes also referred to as the Park–Miller random number generator (after Stephen K. Park and Keith W. Miller), is a type of linear congruential generator (LCG) that operates in multiplicative group of integers modulo n. The general formula is
A combined linear congruential generator (CLCG) is a pseudo-random number generator algorithm based on combining two or more linear congruential generators (LCG). A traditional LCG has a period which is inadequate for complex system simulation. [ 1 ]
In computational number theory, Marsaglia's theorem connects modular arithmetic and analytic geometry to describe the flaws with the pseudorandom numbers resulting from a linear congruential generator. As a direct consequence, it is now widely considered that linear congruential generators are weak for the purpose of generating random numbers.
George Marsaglia established the lattice structure of linear congruential generators in the paper "Random numbers fall mainly in the planes", [2] later termed Marsaglia's theorem. [3] This phenomenon means that n -tuples with coordinates obtained from consecutive use of the generator will lie on a small number of equally spaced hyperplanes in n ...