标题： 随机对偶坐标上升方法在正则化损失最小化中的应用 显示英文标题

标题： Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization

摘要： 随机梯度下降（SGD）由于具有很强的理论保证，已成为解决大规模监督机器学习优化问题（如SVM）的流行方法。尽管密切相关的目标坐标上升（DCA）方法已在各种软件包中实现，但它迄今为止缺乏良好的收敛性分析。 本文对随机目标坐标上升（SDCA）进行了新的分析，表明此类方法享有与SGD相当或更好的强大理论保证。这种分析证明了SDCA在实际应用中的有效性。 显示英文摘要

摘要： Stochastic Gradient Descent (SGD) has become popular for solving large scale supervised machine learning optimization problems such as SVM, due to their strong theoretical guarantees. While the closely related Dual Coordinate Ascent (DCA) method has been implemented in various software packages, it has so far lacked good convergence analysis. This paper presents a new analysis of Stochastic Dual Coordinate Ascent (SDCA) showing that this class of methods enjoy strong theoretical guarantees that are comparable or better than SGD. This analysis justifies the effectiveness of SDCA for practical applications.