论文标题
基于Sarah的差异算法，用于随机有限和cocoercive差异不平等现象
SARAH-based Variance-reduced Algorithm for Stochastic Finite-sum Cocoercive Variational Inequalities
论文作者
论文摘要
变分的不平等是一种广泛的形式主义，涵盖了大量应用。由机器学习及其他地区的应用激励，随机方法非常重要。在本文中，我们考虑了随机有限和cocoercive差异不平等的问题。对于这类问题，我们根据SARAH方差降低技术研究了该方法的收敛性。我们表明，对于强烈单调问题，可以使用此方法实现与解决方案的线性收敛。实验证实了我们方法的重要性和实际适用性。
Variational inequalities are a broad formalism that encompasses a vast number of applications. Motivated by applications in machine learning and beyond, stochastic methods are of great importance. In this paper we consider the problem of stochastic finite-sum cocoercive variational inequalities. For this class of problems, we investigate the convergence of the method based on the SARAH variance reduction technique. We show that for strongly monotone problems it is possible to achieve linear convergence to a solution using this method. Experiments confirm the importance and practical applicability of our approach.
