论文标题
具有不精确模型的加速和非加速随机梯度下降
Accelerated and nonaccelerated stochastic gradient descent with inexact model
论文作者
论文摘要
在本文中，我们提出了一种新的方法，以获得最佳的收敛速率，以实现平滑（强）凸优化任务。我们的方法基于梯度具有非随机噪声的优化任务的结果。与以前已知的结果相反，我们将概念扩展到了不精确的模型概念。
In this paper, we propose a new way to obtain optimal convergence rates for smooth stochastic (strong) convex optimization tasks. Our approach is based on results for optimization tasks where gradients have nonrandom noise. In contrast to previously known results, we extend our idea to the inexact model conception.
