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• W2912441604 abstract "We introduce a new kind of random number generator called a cycle parity generator (CPG). A CPG is formed by combining n extremely simple generators, where n may be in the thousands, or larger. For statistical quality CPGs rely less on a small number of powerful generators, and more on the fact of combination. CPGs have a collection of attributes that is rarely if ever found in a single type of random number generator. Extremely large periods can be attained, e.g., $10sp{50,000}$. Aperiodic CPGs appear to be practical for some applications. CPGs are satisfactorily fast, have desirable theoretical properties and do well on empirical tests. CPGs have interesting ties to well known topics such as factoring and the Riemann hypothesis. The fundamental difference between CPGs and traditional generators makes CPGs appealing for validating application results that are obtained with other generators. No special calculations are required to construct long-period CPGs that possess output sequences of high statistical quality. CPGs scale to arbitrarily large periods, and as the period increases, statistical quality improves. A simple algorithm constructs a customized CPG that, with arbitrarily high probability, will not be constructed by anyone else using the same algorithm. Arbitrarily many output streams can be defined, through custom CPGs. CPGs have the state jumping property and support efficient seeding.We also present an ANSI C library that is an integrated environment for generating, statistically testing, and using random numbers. The library, called Randpack, is portable, robust, and has many features not found in other libraries. Through scripts, the generators used by a particular application can be changed without changing any application code. Scripts also provide for diverse generator topologies and serve as a record of the generators used in a particular application run. Non-traditional generators are provided, and existing generators can be combined in interesting ways to form new generators. Consequently, users can in effect design their own statistical tests. Randpack in not only a random number library; it can be used for general numerical sequence generation. Stress testing of application functions is possible, for example, by generating the set of all k-tuples over a set of numbers. Almost all of the classical empirical tests for random sequences are included, and some of the tests support a greater range of test parameters than previous implementations." @default.
• W2912441604 created "2019-02-21" @default.
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• W2912441604 date "1997-01-01" @default.
• W2912441604 modified "2023-09-27" @default.
• W2912441604 title "Cycle parity random number generators, and a general random number library" @default.
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