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This thesis presents and evaluates a new algorithm which generates random numbers. The algorithm uses a Number Theory class of numbers called Normal Numbers. Normal Numbers consist of an infinite sequence of digits which are uniformly distributed in all sequence lengths. The algorithm is then integrated into the SPRNG package with some ideas as to how it can be parallelized. Finally, the performance of this algorithm is evaluated using a standard test suite. This new algorithm is compared with similar, known good, generators using the spectral test and also as the random number generator in a Monte Carlo algorithm. The generator is shown to work well in all tests and to produce value with moderately good speed.
A Thesis submitted to the Department of Computer Science in partial fulfillment of the requirements for the degree of Master of Science.
Bibliography Note
Includes bibliographical references.
Advisory Committee
Michael Mascagni, Professor Directing Thesis; Xiuwen Liu, Committee Member; Ashok Srinivasan, Committee Member.
Publisher
Florida State University
Identifier
FSU_migr_etd-3409
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