CRApr 28, 2017

Xorshift random number generators from primitive polynomials

arXiv:1705.00098v23 citations
Originality Synthesis-oriented
AI Analysis

This work addresses the need for more robust and customizable random number generators in computational applications, though it appears incremental as it builds on Marsaglia's xorshift RNGs.

The authors tackled the problem of constructing full period xorshift random number generators from primitive polynomials, proposing algorithms to achieve this and identifying a weakness in existing RNGs with suggested improvements.

A class of xorshift random number generators (RNGs) are introduced by Marsaglia. We have proposed an algorithm which constructs a full period xorshift RNG from a given primitive polynomial. It is shown there is a weakness present in those RNGs and is suggested its improvement. A separate algorithm is also proposed which returns a full period xorshift generator with desired number of xorshift operations.%We also introduce the notion of tweaked primitive multiple-recursive matrix method with improved linear complexity.

Foundations

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