In this paper we consider nonconvex optimization and learning over a network of distributed nodes. We develop a Proximal Primal-Dual Algorithm (Prox-PDA), which enables the network nodes to distributedly and collectively compute the set of first-order stationary solutions in a global sublinear manner [with a rate of $O(1/r)$, where $r$ is the iteration counter]. To the best of our knowledge, this is the first algorithm that enables distributed nonconvex optimization with global rate guarantees. Our numerical experiments also demonstrate the effectiveness of the proposed algorithm.
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机译：在本文中，我们考虑了非凸优化和在分布式节点网络上的学习。我们开发了一种近邻原始对偶算法（Prox-PDA），该算法使网络节点能够以全局亚线性方式[速率为$ O（1 / r）$ ，其中$ r $是迭代计数器]。据我们所知，这是第一个启用具有全局速率保证的分布式非凸优化的算法。我们的数值实验也证明了该算法的有效性。
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