论文标题
分散的鞍点问题具有强大的凸度和强凹的不同常数
Decentralized Saddle-Point Problems with Different Constants of Strong Convexity and Strong Concavity
论文作者
论文摘要
大规模的鞍点问题在具有仿射约束的gan和线性模型等机器学习任务中出现。在本文中，我们研究了分布的鞍点问题（SPP），具有强烈的convex-rong-concave平滑目标，这些目标具有不同的凸度和强大的复合术语凹凹参数，这些参数对应于最小变量和最大变量，以及双线性鞍点部分。我们考虑两种类型的一阶牙齿：确定性（返回梯度）和随机（返回无偏的随机梯度）。我们的方法在两种情况下都起作用，并在Oracle调用之间采取几个共识步骤。
Large-scale saddle-point problems arise in such machine learning tasks as GANs and linear models with affine constraints. In this paper, we study distributed saddle-point problems (SPP) with strongly-convex-strongly-concave smooth objectives that have different strong convexity and strong concavity parameters of composite terms, which correspond to min and max variables, and bilinear saddle-point part. We consider two types of first-order oracles: deterministic (returns gradient) and stochastic (returns unbiased stochastic gradient). Our method works in both cases and takes several consensus steps between oracle calls.
