Dual averaging-type methods are widely used in industrial machine learning applications due to their ability to promoting solution structure (e.g. sparsity) efficiently. In this paper, we propose a novel accelerated dual-averaging primal–dual algorithm for minimizing a composite convex function. We also derive a stochastic version of the proposed method that solves empirical risk minimization, and its advantages on handling sparse data are demonstrated both theoretically and empirically.

中文翻译：

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复合凸最小化的加速对偶平均本原对偶方法

双重平均类型方法由于能够有效地提升解决方案结构（例如稀疏性）而被广泛用于工业机器学习应用中。在本文中，我们提出了一种新颖的加速双重平均原始对偶算法，以最小化复合凸函数。我们还推导了该方法的随机版本，该版本解决了经验风险最小化，并且在处理稀疏数据方面的优势在理论上和经验上都得到了证明。