Accelerating Distributed Consensus Using Extrapolation
Accelerating Distributed Consensus Using Extrapolation
复制标题
使用外推法加速分布式共识
DOI：
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复制
发表时间：
2007
影响因子：
3.9
通讯作者：
P. Frossard
中科院分区：
文献类型：
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作者：
E. Kokiopoulou;P. Frossard
在过去的几年里，分布式共识问题受到了人们的广泛关注，特别是在自组织传感器网络的框架下。文献中提出的大多数方法都是通过分布式线性迭代算法来解决这一问题，并具有一致解的渐近收敛特性。在这封信中，我们建议使用外推方法来加速分布式线性迭代。外推方法被保证在有限的步数内收敛，上限由传感器的数目决定。特别地，我们证明了标量Epsilon算法(SEA)可以加速由分布式线性迭代产生的向量序列，而不需要通信开销并且不需要知道整个网络的拓扑结构。仿真结果验证了该方案的有效性和有效性。
In the past few years, the problem of distributed consensus has received a lot of attention, particularly in the framework of ad hoc sensor networks. Most methods proposed in the literature attack this problem by distributed linear iterative algorithms, with asymptotic convergence of the consensus solution. In this letter, we propose the use of extrapolation methods in order to accelerate distributed linear iterations. The extrapolation methods are guaranteed to converge in a finite number of steps, upper bounded by the number of sensors. In particular, we show that the Scalar Epsilon Algorithm (SEA) can accelerate vector sequences produced by distributed linear iterations, with no communication overhead and without knowledge of the full network topology. We provide simulation results that demonstrate the validity and effectiveness of the proposed scheme.