论文标题
随机梯度加速凸的优化：概括强增长条件
Accelerated Convex Optimization with Stochastic Gradients: Generalizing the Strong-Growth Condition
论文作者
论文摘要
本文为随机梯度提供了足够的条件，以免减慢Nesterov加速梯度方法的收敛性。新的条件具有Schmidt \＆Roux作为特殊情况的强劲增长条件，它还使我们能够（i）建模具有约束的问题，以及（ii）设计新型的甲壳类型（例如，用于诸如SAGA之类的有限和有限问题的Oracles）。我们的结果是通过重新访问Nesterov的加速算法而获得的，并且对于设计随机甲壳而不更改基础一阶方法很有用。
This paper presents a sufficient condition for stochastic gradients not to slow down the convergence of Nesterov's accelerated gradient method. The new condition has the strong-growth condition by Schmidt \& Roux as a special case, and it also allows us to (i) model problems with constraints and (ii) design new types of oracles (e.g., oracles for finite-sum problems such as SAGA). Our results are obtained by revisiting Nesterov's accelerated algorithm and are useful for designing stochastic oracles without changing the underlying first-order method.
