![]() ![]() for particle physics simulations.Ī method with roots in number theory, although never used in practical applications. A SWB generator is the basis for the RANLUX generator, widely used e.g. The rationale behind the MIXMAX family of generators relies on results from ergodic theory and classical mechanics.Ī modification of Lagged-Fibonacci generators.Ī modification of Lagged-Fibonacci generators. It is a member of the class of matrix linear congruential generator, a generalisation of LCG. Easy to extend for arbitrary period length and improved statistical performance over higher dimensions and with higher precision. With appropriate initialisations, passes all current empirical test suites, and is formally proven to converge. Simple to implement, fast, but not widely known. The Additive Congruential Random Number generator. Its base is based on prime numbers.Ī specific implementation of a Lehmer generator, widely used because it is included in C++ as the function minstd_rand0 from C++11 onwards. īlum-Blum-Shub is a PRNG algorithm that is considered cryptographically secure. it is used in Excel 2003 and later versions for the Excel function RAND and it was the default generator in the language Python up to version 2.2. Also called Tausworthe generators.Ī combination of three small LCGs, suited to 16-bit CPUs. One of the very earliest and most influential designs.Ī generalisation of the Lehmer generator and historically the most influential and studied generator.Ī hugely influential design. ![]() In its original form, it is of poor quality and of historical interest only. The following algorithms are pseudorandom number generators. ![]() This list includes many common types, regardless of quality or applicability to a given use case. Random number generators are important in many kinds of technical applications, including physics, engineering or mathematical computer studies (e.g., Monte Carlo simulations), cryptography and gambling (on game servers). ![]()
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