We consider a multi-agent framework for distributed optimization where each
agent has access to a local smooth strongly convex function, and the collective
goal is to achieve consensus on the parameters that minimize the sum of the
agents' local functions. We propose an algorithm wherein each agent operates
asynchronously and independently of the other agents. When the local functions
are strongly-convex with Lipschitz-continuous gradients, we show that the
iterates at each agent converge to a neighborhood of the global minimum, where
the neighborhood size depends on the degree of asynchrony in the multi-agent
network. When the agents work at the same rate, convergence to the global
minimizer is achieved. Numerical experiments demonstrate that Asynchronous
Gradient-Push can minimize the global objective faster than state-of-the-art
synchronous first-order methods, is more robust to failing or stalling agents,
and scales better with the network size.

本文提出了一种异步多代理分布式优化算法，每个代理都有一个局部的强凸平滑函数，共同目标是达成对将代理的局部函数之和最小化的参数进行共识。实验结果表明，与同步一阶方法相比，Asynchronous Gradient-Push 算法收敛速度更快，更具鲁棒性，并且随着网络规模的增大，它的扩展性更好。