Push-Sum Distributed Dual Averaging for convex optimization
Push-Sum Distributed Dual Averaging for convex optimization
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DOI：
10.1109/cdc.2012.6426375
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发表时间：
2012-12
期刊：
影响因子：
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通讯作者：
Konstantinos I. Tsianos;Sean F. Lawlor;M. Rabbat
中科院分区：
文献类型：
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作者：
Konstantinos I. Tsianos;Sean F. Lawlor;M. Rabbat
最近，由于从大规模机器学习到无线传感器网络等各种应用的推动，在开发基于共识的分布式优化算法方面进行了大量研究。这项工作描述并证明了一种称为推和分布式双平均的新算法的收敛性，该算法将最新的优化算法 [1] 与推和共识协议 [2] 相结合。正如我们所讨论的，使用推和具有显着的优势。不需要限制双随机共识协议，并且在不提前知道更新矩阵的平稳分布的情况下保证收敛到真正的平均共识。此外，仅对传入信息求和的通信语义使该算法真正异步，并在对不同的相互通信间隔和通信延迟进行建模时允许进行清晰的分析。我们在模拟和小集群上进行了实验，以补充理论分析。
Recently there has been a significant amount of research on developing consensus based algorithms for distributed optimization motivated by applications that vary from large scale machine learning to wireless sensor networks. This work describes and proves convergence of a new algorithm called Push-Sum Distributed Dual Averaging which combines a recent optimization algorithm [1] with a push-sum consensus protocol [2]. As we discuss, the use of push-sum has significant advantages. Restricting to doubly stochastic consensus protocols is not required and convergence to the true average consensus is guaranteed without knowing the stationary distribution of the update matrix in advance. Furthermore, the communication semantics of just summing the incoming information make this algorithm truly asynchronous and allow a clean analysis when varying intercommunication intervals and communication delays are modelled. We include experiments in simulation and on a small cluster to complement the theoretical analysis.