Cameron, Peter and Fairbairn, Ben and Gadouleau, Maximilien (2014) 'Computing in matrix groups without memory.', Chicago journal of theoretical computer science., 2014 (8). pp. 1-16.
Memoryless computation is a novel means of computing any function of a set of registers by updating one register at a time while using no memory. We aim to emulate how computations are performed on modern cores, since they typically involve updates of single registers. The computation model of memoryless computation can be fully expressed in terms of transformation semigroups, or in the case of bijective functions, permutation groups. In this paper, we view registers as elements of a finite field and we compute linear permutation without memory. We first determine the maximum complexity of a linear function when only linear instructions are allowed. We also determine which linear functions are hardest to compute when the field in question is the binary field and the number of registers is even. Secondly, we investigate some matrix groups, thus showing that the special linear group is internally computable but not fast. Thirdly, we determine the smallest set of instructions required to generate the special and general linear groups. These results are important for memoryless computation, for they show that linear functions can be computed very fast or that very few instructions are needed to compute any linear function. They thus indicate new advantages of using memoryless computation.
|Keywords:||Memoryless computation, Linear functions, Matrix groups, General linear group, Special linear group, Generating sets, sequential updates.|
|Full text:||(VoR) Version of Record|
Available under License - Creative Commons Attribution.
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|Publisher Web site:||http://dx.doi.org/10.4086/cjtcs.2014.008|
|Publisher statement:||© 2014 Peter J. Cameron, Ben Fairbairn, and Maximilien Gadouleau This article is distributed under a Creative Commons Attribution License (CC-BY)|
|Date accepted:||27 September 2014|
|Date deposited:||21 October 2015|
|Date of first online publication:||November 2014|
|Date first made open access:||No date available|
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