Ergodicity and class-ergodicity of balanced asymmetric stochastic chains
Ergodicity and class-ergodicity of balanced asymmetric stochastic chains
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DOI：
10.23919/ecc.2013.6669845
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发表时间：
2012-12
期刊：
影响因子：
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通讯作者：
Sadegh Bolouki;R. Malhamé
中科院分区：
文献类型：
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作者：
Sadegh Bolouki;R. Malhamé
无条件共识是多个代理的共识算法的属性，以产生共识，而不管代理状态被初始化的特定时间或状态。在一个较弱的条件下，即所谓的平衡不对称条件下，在智能体状态更新算法中的随机矩阵序列（An）上，证明了：（i）当n变大时，状态的聚集点集是有限的;（ii）单个共识或多个共识的渐近无条件发生与该序列的绝对无限流性质直接相关，如Touri和Nedic '所介绍的。后一个条件必须满足每个岛屿上的所谓的无界相互作用图（An），由Hendrickx等人定义的平衡不对称性的属性是满足许多著名的离散时间共识模型在文献中研究。
Unconditional consensus is the property of a consensus algorithm for multiple agents, to produce consensus irrespective of the particular time or state at which the agent states are initialized. Under a weak condition, so-called balanced asymmetry, on the sequence (An) of stochastic matrices in the agents states update algorithm, it is shown that (i) the set of accumulation points of states as n grows large is finite, (ii) the asymptotic unconditional occurrence of single consensus or multiple consensuses is directly related to the property of absolute infinite flow of this sequence, as introduced by Touri and Nedić. The latter condition must be satisfied on each of the islands of the so-called unbounded interactions graph induced by (An), as defined by Hendrickx et al. The property of balanced asymmetry is satisfied by many of the well known discrete time consensus models studied in the literature.