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  2. Blum Blum Shub - Wikipedia

    en.wikipedia.org/wiki/Blum_Blum_Shub

    Blum Blum Shub takes the form + =, where M = pq is the product of two large primes p and q.At each step of the algorithm, some output is derived from x n+1; the output is commonly either the bit parity of x n+1 or one or more of the least significant bits of x n+1.

  3. List of random number generators - Wikipedia

    en.wikipedia.org/wiki/List_of_random_number...

    A modification of Lagged-Fibonacci generators. A SWB generator is the basis for the RANLUX generator, [19] widely used e.g. for particle physics simulations. Maximally periodic reciprocals: 1992 R. A. J. Matthews [20] A method with roots in number theory, although never used in practical applications. KISS: 1993 G. Marsaglia [21]

  4. Permuted congruential generator - Wikipedia

    en.wikipedia.org/.../Permuted_Congruential_Generator

    Again, the output is half the size of the input. Beginning with a 2 b-bit input word, the top b−3 bits are used for a shift amount, which is applied to the next-most-significant 2 b−1 +2 b−3 −1 bits, and the least significant 2 b−1 bits of the result are output. The low 2 b−1 −2 b−3 −b+4 bits are discarded.

  5. Linear congruential generator - Wikipedia

    en.wikipedia.org/wiki/Linear_congruential_generator

    Note that a power-of-2 modulus shares the problem as described above for c = 0: the low k bits form a generator with modulus 2 k and thus repeat with a period of 2 k; only the most significant bit achieves the full period. If a pseudorandom number less than r is desired, ⌊ rX/m ⌋ is a much higher-quality result than X mod r.

  6. Mersenne Twister - Wikipedia

    en.wikipedia.org/wiki/Mersenne_Twister

    The Mersenne Twister is a general-purpose pseudorandom number generator (PRNG) developed in 1997 by Makoto Matsumoto (松本 眞) and Takuji Nishimura (西村 拓士). [1] [2] Its name derives from the choice of a Mersenne prime as its period length. The Mersenne Twister was designed specifically to rectify most of the flaws found in older PRNGs.

  7. Fisher–Yates shuffle - Wikipedia

    en.wikipedia.org/wiki/Fisher–Yates_shuffle

    For example, the built-in pseudorandom number generator provided by many programming languages and/or libraries may often have only 32 bits of internal state, which means it can only produce 2 32 different sequences of numbers. If such a generator is used to shuffle a deck of 52 playing cards, it can only ever produce a very small fraction of ...

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  9. Hardware random number generator - Wikipedia

    en.wikipedia.org/wiki/Hardware_random_number...

    A USB-pluggable hardware true random number generator. In computing, a hardware random number generator (HRNG), true random number generator (TRNG), non-deterministic random bit generator (NRBG), [1] or physical random number generator [2] [3] is a device that generates random numbers from a physical process capable of producing entropy (in other words, the device always has access to a ...