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 which 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原对偶法

双平均型方法由于能够有效地提升解结构（例如，稀疏性）而被广泛用于工业机器学习应用中。在本文中，我们提出了一种新的加速对偶平均原始对偶算法，用于最小化复合凸函数。我们还推导出了所提出方法的随机版本，该方法解决了经验风险最小化问题，并且从理论上和经验上都证明了其在处理稀疏数据方面的优势。