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首页> 外文期刊>IEEE Transactions on Automatic Control >Distributed Stochastic Approximation Algorithm With Expanding Truncations
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Distributed Stochastic Approximation Algorithm With Expanding Truncations

机译：扩展截断的分布式随机近似算法

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In this paper, a novel distributed stochastic approximation algorithm (DSAA) is proposed to seek roots of the sum of local functions, each of which is associated with an agent from multiple agents connected over a network. At each iteration, each agent updates its estimate for the root utilizing the noisy observations of its local function and the information derived from the neighboring agents. The key difference of the proposed algorithm from the existing ones consists in the expanding truncations (so it is called the DSAAWET), by which the boundedness of the estimates can be guaranteed without imposing the growth-rate constraints on the local functions. The estimates generated by the DSAAWET are shown to converge almost surely to a consensus set, which belongs to a connected subset of the root set of the sum function. In comparison with the existing results, we impose weaker conditions on the local functions and on the observation noise. We then apply the proposed algorithm to two applications, one from signal processing and the other one from distributed optimization, and establish the almost sure convergence. Numerical simulation results are also included.
机译：在本文中，提出了一种新颖的分布式随机近似算法（DSAA）来寻找本地函数之和的根，每个函数的总和与来自网络连接的多个代理的代理相关联。在每次迭代时，每个代理更新其利用其本地功能的嘈杂观察和从相邻代理的信息进行估计。来自现有算法的临界算法的关键差异在于扩展截断（因此它被称为DSAAWET），可以保证估计的界限而不施加本地功能上的生长速率约束。 DSAAWET生成的估计显示几乎肯定地融合到共识集，其属于总和函数的根集的连接子集。与现有结果相比，我们对本地功能和观察噪声施加较弱的条件。然后，我们将所提出的算法应用于两个应用程序，一个应用程序，一个来自信号处理，另一个来自分布式优化，并建立几乎肯定的融合。数值模拟结果也包括在内。

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