论文标题
一种有效的随机算法，用于分散的非convex-rong-concave minimax优化
An Efficient Stochastic Algorithm for Decentralized Nonconvex-Strongly-Concave Minimax Optimization
论文作者
论文摘要
本文研究了多代理网络上的随机非convex-rong-concave minimax优化。我们提出了一种有效的算法，称为分散递归梯度下降方法（Dream），该方法获得了找到$ε$ - 定位点的最著名理论保证。具体而言，它需要$ \ MATHCAL {o}（\ min（κ^3ε^{ - 3}，κ^2 \ sqrt {n} sqrt {n}ε^{ - 2}）$随机的第一级Oracle（sfo）呼叫和$ \ tilde {回合，其中$κ$是条件编号，而$ n $是单个功能的总数。我们的数值实验也验证了梦想优于以前的方法。
This paper studies the stochastic nonconvex-strongly-concave minimax optimization over a multi-agent network. We propose an efficient algorithm, called Decentralized Recursive gradient descEnt Ascent Method (DREAM), which achieves the best-known theoretical guarantee for finding the $ε$-stationary points. Concretely, it requires $\mathcal{O}(\min (κ^3ε^{-3},κ^2 \sqrt{N} ε^{-2} ))$ stochastic first-order oracle (SFO) calls and $\tilde{\mathcal{O}}(κ^2 ε^{-2})$ communication rounds, where $κ$ is the condition number and $N$ is the total number of individual functions. Our numerical experiments also validate the superiority of DREAM over previous methods.
