Optimizing tensor product computations in stochastic automata networks

IF 1.8 4区管理学 Q3 OPERATIONS RESEARCH & MANAGEMENT SCIENCE

Rairo-Operations Research Pub Date : 1998-01-01 DOI:10.1051/RO/1998320303251

Paulo Fernandes, B. Plateau, W. Stewart

引用次数: 26

Abstract

In this paper we consider some numerical issues in computing solutions to networks of stochastic automata (SAN). In particular our concern is with keeping the amount of computation per iteration to a minimum, since iterative methods appear to be the most effective in determining numerical solutions. In a previous paper we presented complexity results concerning the vector-descriptor multiplication phase of the analysis. In this paper our concern is with optimizations related to the implementation of this algorithm. We also consider the possible benefits of grouping automata in a SAN with many small automata, to create an equivalent SAN having a smaller number of larger automata.

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随机自动机网络中优化张量积计算

本文研究了随机自动机网络(SAN)计算解中的一些数值问题。我们特别关注的是将每次迭代的计算量保持在最低限度，因为迭代方法在确定数值解方面似乎是最有效的。在之前的一篇论文中，我们给出了有关向量描述符乘法阶段分析的复杂性结果。在本文中，我们关注的是与该算法的实现相关的优化。我们还考虑了在具有许多小型自动机的SAN中对自动机进行分组的可能好处，以创建具有较少数量的大型自动机的等效SAN。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Rairo-Operations Research 管理科学-运筹学与管理科学

CiteScore

3.60

自引率

22.20%

发文量

206

审稿时长

>12 weeks

期刊介绍： RAIRO-Operations Research is an international journal devoted to high-level pure and applied research on all aspects of operations research. All papers published in RAIRO-Operations Research are critically refereed according to international standards. Any paper will either be accepted (possibly with minor revisions) either submitted to another evaluation (after a major revision) or rejected. Every effort will be made by the Editorial Board to ensure a first answer concerning a submitted paper within three months, and a final decision in a period of time not exceeding six months.