论文标题
分布式优化在依赖随机网络上
Distributed Optimization Over Dependent Random Networks
论文作者
论文摘要
我们在随机网络上研究基于平均的分布式优化求解器。我们使用重量纳学对此类方案的融合显示了一般结果，这些重量几乎肯定是对一类依赖的依赖重量矩阵序列的预期，并且圆柱形成式。除了暗示有关该领域的许多先前已知的结果外，我们的工作还显示了分布式优化结果对链接失效的鲁棒性。此外，它提供了一种新工具，用于合成分布式优化算法。 {为了证明我们的主要定理，我们建立了有关平均动态（因）随机网络的收敛性分析速率的新结果。这些次要结果以及确定它们所需的Martingale型结果，可能会引起随机网络分布式计算的更广泛的研究努力。
We study the averaging-based distributed optimization solvers over random networks. We show a general result on the convergence of such schemes using weight-matrices that are row-stochastic almost surely and column-stochastic in expectation for a broad class of dependent weight-matrix sequences. In addition to implying many of the previously known results on this domain, our work shows the robustness of distributed optimization results to link-failure. Also, it provides a new tool for synthesizing distributed optimization algorithms. {To prove our main theorem, we establish new results on the rate of convergence analysis of averaging dynamics over (dependent) random networks. These secondary results, along with the required martingale-type results to establish them, might be of interest to broader research endeavors in distributed computation over random networks.
