随机动力学与Edmonds算法

IF 1.1 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Journal of Dynamics and Games Pub Date : 2021-01-01 DOI:10.3934/jdg.2021029

Jonathan Newton, William H. Sandholm

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引用次数: 2

摘要

最近，人们对随机动力学的循环分解重新产生了兴趣。这些分解考虑了短期、中期和长期的动态行为，将行为周期聚合为逐渐增大的周期，最终包含整个状态空间。我们证明这些分解等价于Edmonds算法的聚合阶段，并且这种等价可以用于恢复文献中众所周知的结果。

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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Stochastic dynamics and Edmonds' algorithm

Recently, there has been a revival of interest in cyclic decompositions of stochastic dynamics. These decompositions consider the behavior of dynamics over the short, medium and long run, aggregating cycles of behavior into progressively larger cycles, eventually encompassing the entire state space. We show that these decompositions are equivalent to the aggregative stage of Edmonds' algorithm and that this equivalence can be used to recover well-known results in the literature.

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来源期刊

Journal of Dynamics and Games MATHEMATICS, INTERDISCIPLINARY APPLICATIONS-

CiteScore

2.00

自引率

0.00%

发文量

期刊介绍： The Journal of Dynamics and Games (JDG) is a pure and applied mathematical journal that publishes high quality peer-review and expository papers in all research areas of expertise of its editors. The main focus of JDG is in the interface of Dynamical Systems and Game Theory.