Inspired by the subgradient push method developed recently by Nedi_ et al. we present a distributed dual averaging push algorithm for constrained nonsmooth convex optimization over time-varying directed graph. Our algorithm combines the dual averaging method with the push-sum technique and achieves an O(1/ √k) convergence rate. Compared with the subgradient push algorithm, our algorithm, first, addresses the constrained problems, and, second, has a faster convergence rate, and, third, simplifies the convergence analysis. We also generalize the proposed algorithm so that input variables of subgradient oracles have guaranteed convergence.

中文翻译：

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时变有向图上分布式凸优化的双平均推动


受到 Nedi_ 等人最近开发的次梯度推动方法的启发。我们提出了一种分布式对偶平均推送算法，用于时变有向图上的约束非光滑凸优化。我们的算法将对偶平均方法与推和技术相结合，并实现了 O(1/√k) 收敛速度。与次梯度推送算法相比，我们的算法首先解决了约束问题，其次具有更快的收敛速度，第三简化了收敛分析。我们还推广了所提出的算法，以便次梯度预言的输入变量保证收敛。